From 65e55bd251da00736f87100a4bf3f6d3ff253148 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Tue, 17 Dec 2024 23:29:19 -0500
Subject: [PATCH] as-caida data

---
 pytorch/batch.py                              |   2 +-
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 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_075.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_075.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_080.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_080.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_085.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_085.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_090.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_090.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_095.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_095.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_100.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_100.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_105.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_105.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_110.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_110.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_115.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_115.output (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_120.json (100%)
 rename pytorch/{output_as-caida_maxcore => output_as-caida_maxcore_old}/xeon_4216_max_csr_20_10_10_as-caida_G_120.output (100%)

diff --git a/pytorch/batch.py b/pytorch/batch.py
index 42211cf..8978d39 100644
--- a/pytorch/batch.py
+++ b/pytorch/batch.py
@@ -117,7 +117,7 @@ elif args.matrix_type == MatrixType.SYNTHETIC:
     parameter_list = enumerate([(size, density)
         for size in args.synthetic_size
         for density in args.synthetic_density
-        if size ** 2 * density <= 30000000])
+        if size ** 2 * density <= 50000000])
 
 #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
 for i, parameter in parameter_list:
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..cc64c57
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5806, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.171998977661133, "TIME_S_1KI": 1.7519805335275807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.83616289138786, "W": 23.626422472883572, "J_1KI": 53.88152995029071, "W_1KI": 4.069311483445328, "W_D": 5.210422472883575, "J_D": 68.9909179153442, "W_D_1KI": 0.8974203363561101, "J_D_1KI": 0.15456774653050467}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..04c69bd
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.1808183193206787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.7415, 0.8054, 0.6431,  ..., 0.7043, 0.2095, 0.2852])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.1808183193206787 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5806 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.171998977661133}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.0310, 0.3080, 0.7594,  ..., 0.0941, 0.5225, 0.9795])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.171998977661133 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.0310, 0.3080, 0.7594,  ..., 0.0941, 0.5225, 0.9795])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.171998977661133 seconds
+
+[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76]
+[20.8, 20.88, 21.04, 25.72, 26.8, 29.92, 30.76, 28.4, 26.88, 24.6, 24.56, 24.56, 24.72]
+13.240945100784302
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572}
+[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76, 20.2, 20.36, 20.24, 20.2, 20.24, 20.36, 20.4, 20.56, 20.6, 20.48]
+368.31999999999994
+18.415999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572, 'J_1KI': 53.88152995029071, 'W_1KI': 4.069311483445328, 'W_D': 5.210422472883575, 'J_D': 68.9909179153442, 'W_D_1KI': 0.8974203363561101, 'J_D_1KI': 0.15456774653050467}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..ba4817b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5720, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.46983003616333, "TIME_S_1KI": 1.8303898664621205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.3828331851959, "W": 23.035559333910587, "J_1KI": 57.23476104636292, "W_1KI": 4.027195687746606, "W_D": 4.593559333910587, "J_D": 65.28395717859267, "W_D_1KI": 0.803069813620732, "J_D_1KI": 0.14039682056306504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..96b688f
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.23993563652038574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.3498, 0.5888, 0.0645,  ..., 0.9730, 0.2168, 0.4693])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.23993563652038574 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4376 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 8.03219199180603}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.9782, 0.4472, 0.6352,  ..., 0.3530, 0.3134, 0.8485])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 8.03219199180603 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5720 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.46983003616333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5450, 0.3229, 0.9483,  ..., 0.0104, 0.7843, 0.5252])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.46983003616333 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5450, 0.3229, 0.9483,  ..., 0.0104, 0.7843, 0.5252])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.46983003616333 seconds
+
+[20.48, 20.36, 20.36, 20.28, 20.44, 20.76, 20.8, 20.72, 20.64, 20.24]
+[20.2, 20.04, 22.12, 23.48, 25.88, 25.88, 27.08, 28.04, 26.28, 26.48, 25.0, 24.88, 24.72, 24.44]
+14.212063550949097
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5720, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.46983003616333, 'TIME_S_1KI': 1.8303898664621205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.3828331851959, 'W': 23.035559333910587}
+[20.48, 20.36, 20.36, 20.28, 20.44, 20.76, 20.8, 20.72, 20.64, 20.24, 20.28, 20.36, 20.52, 20.68, 20.48, 20.48, 20.52, 20.4, 20.32, 20.44]
+368.84000000000003
+18.442
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5720, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.46983003616333, 'TIME_S_1KI': 1.8303898664621205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.3828331851959, 'W': 23.035559333910587, 'J_1KI': 57.23476104636292, 'W_1KI': 4.027195687746606, 'W_D': 4.593559333910587, 'J_D': 65.28395717859267, 'W_D_1KI': 0.803069813620732, 'J_D_1KI': 0.14039682056306504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..9fa31bf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5449, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288, "TIME_S_1KI": 1.9124832300335624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.5276795768738, "W": 22.970903604955094, "J_1KI": 59.924330992269006, "W_1KI": 4.21561820608462, "W_D": 4.577903604955093, "J_D": 65.0741593434811, "W_D_1KI": 0.8401364663158548, "J_D_1KI": 0.15418177029103594}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..e3feb4a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.24444031715393066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.0041, 0.0059, 0.8299,  ..., 0.3077, 0.8545, 0.8513])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.24444031715393066 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4295 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.275424003601074}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.5048, 0.0128, 0.9259,  ..., 0.5690, 0.7343, 0.9731])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 8.275424003601074 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5449 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.2882, 0.9342, 0.0969,  ..., 0.6573, 0.4161, 0.3369])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.42112112045288 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.2882, 0.9342, 0.0969,  ..., 0.6573, 0.4161, 0.3369])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.42112112045288 seconds
+
+[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24]
+[20.4, 20.36, 20.28, 25.0, 25.96, 28.32, 29.16, 26.92, 25.8, 24.32, 24.4, 24.24, 24.24, 24.24]
+14.21483826637268
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094}
+[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24, 20.48, 20.36, 20.32, 20.2, 20.16, 20.6, 20.76, 20.72, 20.8, 20.44]
+367.86
+18.393
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094, 'J_1KI': 59.924330992269006, 'W_1KI': 4.21561820608462, 'W_D': 4.577903604955093, 'J_D': 65.0741593434811, 'W_D_1KI': 0.8401364663158548, 'J_D_1KI': 0.15418177029103594}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..380b35d
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5290, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.617355108261108, "TIME_S_1KI": 2.0070614571382057, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.70390956878657, "W": 22.650037252323052, "J_1KI": 61.0026294080882, "W_1KI": 4.281670558095095, "W_D": 4.262037252323051, "J_D": 60.72290604782095, "W_D_1KI": 0.8056781195317676, "J_D_1KI": 0.15230210199088234}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..79a4d8b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.2548098564147949}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.8564, 0.5356, 0.2431,  ..., 0.6892, 0.8837, 0.8255])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.2548098564147949 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4120 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 8.176596403121948}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.6319, 0.0708, 0.0554,  ..., 0.3150, 0.6632, 0.2317])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 8.176596403121948 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5290 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.617355108261108}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4957, 0.1363, 0.6360,  ..., 0.3901, 0.1824, 0.9506])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.617355108261108 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4957, 0.1363, 0.6360,  ..., 0.3901, 0.1824, 0.9506])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.617355108261108 seconds
+
+[20.12, 20.28, 20.16, 20.16, 20.12, 20.24, 20.32, 20.4, 20.4, 20.48]
+[20.6, 20.72, 20.8, 22.2, 23.96, 25.28, 26.36, 26.92, 26.56, 25.12, 25.08, 25.24, 25.24, 25.32]
+14.247389793395996
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5290, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.617355108261108, 'TIME_S_1KI': 2.0070614571382057, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.70390956878657, 'W': 22.650037252323052}
+[20.12, 20.28, 20.16, 20.16, 20.12, 20.24, 20.32, 20.4, 20.4, 20.48, 20.32, 20.36, 20.48, 20.48, 20.68, 20.76, 20.64, 20.68, 20.8, 20.68]
+367.76000000000005
+18.388
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5290, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.617355108261108, 'TIME_S_1KI': 2.0070614571382057, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.70390956878657, 'W': 22.650037252323052, 'J_1KI': 61.0026294080882, 'W_1KI': 4.281670558095095, 'W_D': 4.262037252323051, 'J_D': 60.72290604782095, 'W_D_1KI': 0.8056781195317676, 'J_D_1KI': 0.15230210199088234}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..8393438
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5009, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179, "TIME_S_1KI": 2.0834632347291233, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.9102258396148, "W": 23.870320724339745, "J_1KI": 67.85989735268812, "W_1KI": 4.7654862695827, "W_D": 5.56032072433975, "J_D": 79.17823539018632, "W_D_1KI": 1.110066026021112, "J_D_1KI": 0.22161429946518504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..ee06650
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.2360525131225586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.8761, 0.0368, 0.8631,  ..., 0.6340, 0.9685, 0.1396])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.2360525131225586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4448 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 9.323699712753296}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.4095, 0.9128, 0.5370,  ..., 0.1298, 0.9549, 0.0765])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 9.323699712753296 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5009 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.6896, 0.4674, 0.9391,  ..., 0.8690, 0.1471, 0.0542])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.436067342758179 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.6896, 0.4674, 0.9391,  ..., 0.8690, 0.1471, 0.0542])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.436067342758179 seconds
+
+[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52]
+[20.44, 20.72, 20.72, 23.84, 26.4, 29.12, 30.32, 31.08, 27.36, 26.24, 24.52, 25.04, 25.36, 25.64]
+14.239868402481079
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745}
+[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52, 20.2, 20.16, 20.16, 20.16, 20.2, 20.24, 20.16, 20.2, 19.92, 19.96]
+366.19999999999993
+18.309999999999995
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745, 'J_1KI': 67.85989735268812, 'W_1KI': 4.7654862695827, 'W_D': 5.56032072433975, 'J_D': 79.17823539018632, 'W_D_1KI': 1.110066026021112, 'J_D_1KI': 0.22161429946518504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..8348412
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4924, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.37181830406189, "TIME_S_1KI": 2.106380646641326, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 321.23179523468013, "W": 22.538047273928385, "J_1KI": 65.23797628649068, "W_1KI": 4.577182630773433, "W_D": 4.022047273928383, "J_D": 57.325705755233706, "W_D_1KI": 0.8168251977921167, "J_D_1KI": 0.16588651458003995}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..1954b2e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.27124905586242676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.5482, 0.1173, 0.8258,  ..., 0.9814, 0.8155, 0.1910])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.27124905586242676 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3870 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 8.251606702804565}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8510, 0.7355, 0.4606,  ..., 0.8447, 0.7677, 0.2060])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 8.251606702804565 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4924 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.37181830406189}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8967, 0.1733, 0.8053,  ..., 0.0536, 0.8153, 0.7243])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.37181830406189 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8967, 0.1733, 0.8053,  ..., 0.0536, 0.8153, 0.7243])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.37181830406189 seconds
+
+[20.32, 20.36, 20.4, 20.36, 20.52, 20.64, 20.56, 20.6, 20.48, 20.4]
+[20.12, 20.28, 21.16, 22.48, 22.48, 24.72, 25.72, 26.68, 26.52, 26.0, 25.68, 25.52, 25.08, 25.04]
+14.252867221832275
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4924, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.37181830406189, 'TIME_S_1KI': 2.106380646641326, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.23179523468013, 'W': 22.538047273928385}
+[20.32, 20.36, 20.4, 20.36, 20.52, 20.64, 20.56, 20.6, 20.48, 20.4, 20.56, 20.4, 20.44, 20.64, 20.64, 20.56, 20.84, 20.76, 20.92, 21.12]
+370.32000000000005
+18.516000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4924, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.37181830406189, 'TIME_S_1KI': 2.106380646641326, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.23179523468013, 'W': 22.538047273928385, 'J_1KI': 65.23797628649068, 'W_1KI': 4.577182630773433, 'W_D': 4.022047273928383, 'J_D': 57.325705755233706, 'W_D_1KI': 0.8168251977921167, 'J_D_1KI': 0.16588651458003995}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..b64425d
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4906, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053, "TIME_S_1KI": 2.113679259152885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 320.3935369491577, "W": 22.512475730235575, "J_1KI": 65.30646900716626, "W_1KI": 4.5887639075082705, "W_D": 4.301475730235577, "J_D": 61.217834938526146, "W_D_1KI": 0.8767785834153234, "J_D_1KI": 0.17871556938755065}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..8c69e76
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.28984951972961426}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.2495, 0.4746, 0.6508,  ..., 0.6030, 0.3808, 0.6963])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.28984951972961426 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3622 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.750367879867554}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.1862, 0.1434, 0.8787,  ..., 0.7704, 0.8925, 0.8878])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 7.750367879867554 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4906 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7972, 0.7657, 0.0835,  ..., 0.8008, 0.2416, 0.9619])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.369710445404053 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7972, 0.7657, 0.0835,  ..., 0.8008, 0.2416, 0.9619])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.369710445404053 seconds
+
+[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2]
+[20.36, 20.32, 21.32, 22.76, 24.92, 24.92, 25.72, 26.36, 26.24, 25.64, 24.64, 24.68, 24.72, 24.56]
+14.231821537017822
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575}
+[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2, 20.36, 20.4, 20.56, 20.4, 20.6, 20.6, 20.6, 20.28, 20.12, 20.52]
+364.21999999999997
+18.211
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575, 'J_1KI': 65.30646900716626, 'W_1KI': 4.5887639075082705, 'W_D': 4.301475730235577, 'J_D': 61.217834938526146, 'W_D_1KI': 0.8767785834153234, 'J_D_1KI': 0.17871556938755065}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..cebff66
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4720, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.206109046936035, "TIME_S_1KI": 2.1623112387576344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.96216671943665, "W": 22.387743007233112, "J_1KI": 67.57673023716879, "W_1KI": 4.743165891362947, "W_D": 4.006743007233112, "J_D": 57.084782090902344, "W_D_1KI": 0.8488862303459983, "J_D_1KI": 0.1798487776156776}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..fe71ee4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_040.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.222426176071167}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.9066, 0.3947, 0.5988,  ..., 0.8238, 0.0350, 0.4398])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.222426176071167 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4720 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.206109046936035}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.1500, 0.4836, 0.1224,  ..., 0.2519, 0.6750, 0.1609])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.206109046936035 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.1500, 0.4836, 0.1224,  ..., 0.2519, 0.6750, 0.1609])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.206109046936035 seconds
+
+[20.2, 20.0, 20.04, 20.24, 20.28, 20.28, 20.56, 20.56, 20.4, 20.4]
+[20.32, 20.48, 20.84, 22.0, 24.12, 26.52, 27.16, 27.04, 25.84, 24.28, 24.16, 24.2, 24.2, 24.04]
+14.247178316116333
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4720, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.206109046936035, 'TIME_S_1KI': 2.1623112387576344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.96216671943665, 'W': 22.387743007233112}
+[20.2, 20.0, 20.04, 20.24, 20.28, 20.28, 20.56, 20.56, 20.4, 20.4, 20.48, 20.48, 20.52, 20.6, 20.76, 20.68, 20.56, 20.64, 20.4, 20.16]
+367.62
+18.381
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4720, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.206109046936035, 'TIME_S_1KI': 2.1623112387576344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.96216671943665, 'W': 22.387743007233112, 'J_1KI': 67.57673023716879, 'W_1KI': 4.743165891362947, 'W_D': 4.006743007233112, 'J_D': 57.084782090902344, 'W_D_1KI': 0.8488862303459983, 'J_D_1KI': 0.1798487776156776}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..332fb37
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4905, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495, "TIME_S_1KI": 2.1514306860718646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.58112309455873, "W": 23.14675123767438, "J_1KI": 66.9890159214187, "W_1KI": 4.719011465377039, "W_D": 4.7337512376743796, "J_D": 67.19825526070599, "W_D_1KI": 0.9650868986084362, "J_D_1KI": 0.196755738758091}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..88c6f0e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.2738626003265381}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.8777, 0.5124, 0.0822,  ..., 0.9706, 0.3708, 0.5874])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.2738626003265381 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3834 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.640145778656006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9169, 0.5590, 0.9513,  ..., 0.6480, 0.9706, 0.8048])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 8.640145778656006 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4659 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.972679376602173}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6180, 0.9542, 0.9412,  ..., 0.9357, 0.2218, 0.1163])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 9.972679376602173 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4905 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7047, 0.9313, 0.0358,  ..., 0.9576, 0.8194, 0.2072])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.552767515182495 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7047, 0.9313, 0.0358,  ..., 0.9576, 0.8194, 0.2072])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.552767515182495 seconds
+
+[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6]
+[20.6, 20.52, 20.28, 22.6, 24.08, 26.32, 27.4, 28.32, 27.04, 26.4, 25.72, 25.72, 25.88, 25.88]
+14.195561170578003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438}
+[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6, 20.8, 20.4, 20.12, 19.96, 20.16, 20.24, 20.4, 20.6, 20.48, 20.4]
+368.26
+18.413
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438, 'J_1KI': 66.9890159214187, 'W_1KI': 4.719011465377039, 'W_D': 4.7337512376743796, 'J_D': 67.19825526070599, 'W_D_1KI': 0.9650868986084362, 'J_D_1KI': 0.196755738758091}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..777b8dc
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4641, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.064989805221558, "TIME_S_1KI": 2.16871144262477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 338.8994520378113, "W": 23.81807615970891, "J_1KI": 73.02293730614335, "W_1KI": 5.132100012865527, "W_D": 5.377076159708913, "J_D": 76.5086210939885, "W_D_1KI": 1.1586029217213776, "J_D_1KI": 0.24964510271953838}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..a75c16e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_050.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.22620630264282227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.0575, 0.9187, 0.6279,  ..., 0.6013, 0.5410, 0.6623])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.22620630264282227 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4641 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.064989805221558}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.7076, 0.1289, 0.6333,  ..., 0.8099, 0.6262, 0.3286])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.064989805221558 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.7076, 0.1289, 0.6333,  ..., 0.8099, 0.6262, 0.3286])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.064989805221558 seconds
+
+[20.48, 20.44, 20.44, 20.48, 20.24, 20.4, 20.56, 20.44, 20.68, 20.8]
+[20.72, 20.76, 24.0, 25.88, 28.52, 28.52, 29.36, 30.0, 25.88, 25.72, 23.92, 24.0, 24.2, 24.4]
+14.228666067123413
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4641, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.064989805221558, 'TIME_S_1KI': 2.16871144262477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.8994520378113, 'W': 23.81807615970891}
+[20.48, 20.44, 20.44, 20.48, 20.24, 20.4, 20.56, 20.44, 20.68, 20.8, 20.8, 20.68, 20.84, 20.4, 20.32, 20.32, 20.36, 20.4, 20.48, 20.6]
+368.81999999999994
+18.440999999999995
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4641, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.064989805221558, 'TIME_S_1KI': 2.16871144262477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.8994520378113, 'W': 23.81807615970891, 'J_1KI': 73.02293730614335, 'W_1KI': 5.132100012865527, 'W_D': 5.377076159708913, 'J_D': 76.5086210939885, 'W_D_1KI': 1.1586029217213776, 'J_D_1KI': 0.24964510271953838}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..ddd2c19
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757, "TIME_S_1KI": 2.212199888688875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 340.41829671859733, "W": 23.891063458309905, "J_1KI": 71.93962314425134, "W_1KI": 5.048829978510124, "W_D": 5.390063458309907, "J_D": 76.80178092050545, "W_D_1KI": 1.1390666649006567, "J_D_1KI": 0.24071569418864255}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..d8de122
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,86 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.2837095260620117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1141, 0.9590, 0.8822,  ..., 0.4586, 0.3032, 0.3922])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.2837095260620117 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3700 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 8.209553480148315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([1.3531e-01, 9.2471e-01, 3.7424e-01,  ..., 8.7339e-04, 4.3447e-01,
+        4.7205e-01])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 8.209553480148315 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4732 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1672, 0.2165, 0.1528,  ..., 0.1782, 0.4621, 0.9393])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.468129873275757 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1672, 0.2165, 0.1528,  ..., 0.1782, 0.4621, 0.9393])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.468129873275757 seconds
+
+[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6]
+[20.6, 20.72, 20.72, 24.32, 26.2, 29.28, 30.44, 28.2, 28.0, 25.56, 25.68, 25.84, 25.84, 25.8]
+14.24877119064331
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905}
+[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6, 20.64, 20.36, 20.52, 20.52, 20.48, 20.36, 20.36, 20.32, 20.44, 20.64]
+370.02
+18.500999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905, 'J_1KI': 71.93962314425134, 'W_1KI': 5.048829978510124, 'W_D': 5.390063458309907, 'J_D': 76.80178092050545, 'W_D_1KI': 1.1390666649006567, 'J_D_1KI': 0.24071569418864255}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..15a8fac
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4630, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.542139053344727, "TIME_S_1KI": 2.2769198819319065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.9495843696594, "W": 22.493385217832277, "J_1KI": 69.10358193729145, "W_1KI": 4.858182552447577, "W_D": 4.2303852178322785, "J_D": 60.173690134286886, "W_D_1KI": 0.9136901118428247, "J_D_1KI": 0.19734127685590167}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..1f5627b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.2887892723083496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.1431, 0.0707, 0.8757,  ..., 0.5198, 0.2078, 0.1401])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.2887892723083496 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3635 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 8.24190092086792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.3060, 0.9734, 0.9302,  ..., 0.1120, 0.6990, 0.6634])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 8.24190092086792 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4630 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.542139053344727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.9904, 0.2821, 0.0606,  ..., 0.1141, 0.3773, 0.3266])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.542139053344727 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.9904, 0.2821, 0.0606,  ..., 0.1141, 0.3773, 0.3266])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.542139053344727 seconds
+
+[20.32, 20.48, 20.36, 20.28, 20.28, 20.0, 19.96, 19.96, 19.88, 19.96]
+[20.16, 20.52, 20.88, 22.52, 24.0, 26.12, 27.04, 27.0, 26.32, 24.6, 24.6, 24.36, 24.24, 24.4]
+14.224163293838501
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4630, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.542139053344727, 'TIME_S_1KI': 2.2769198819319065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.9495843696594, 'W': 22.493385217832277}
+[20.32, 20.48, 20.36, 20.28, 20.28, 20.0, 19.96, 19.96, 19.88, 19.96, 20.64, 20.48, 20.6, 20.4, 20.44, 20.52, 20.48, 20.2, 20.32, 20.32]
+365.26
+18.262999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4630, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.542139053344727, 'TIME_S_1KI': 2.2769198819319065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.9495843696594, 'W': 22.493385217832277, 'J_1KI': 69.10358193729145, 'W_1KI': 4.858182552447577, 'W_D': 4.2303852178322785, 'J_D': 60.173690134286886, 'W_D_1KI': 0.9136901118428247, 'J_D_1KI': 0.19734127685590167}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..4edf738
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4558, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836, "TIME_S_1KI": 2.2826242886284245, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.84002380371095, "W": 23.226044906428264, "J_1KI": 72.3650776225781, "W_1KI": 5.095665841691151, "W_D": 4.6110449064282655, "J_D": 65.48283049583436, "W_D_1KI": 1.0116377591988295, "J_D_1KI": 0.2219477312853948}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..bfc13d8
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.2867097854614258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2765, 0.7404, 0.5834,  ..., 0.0305, 0.2184, 0.6277])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.2867097854614258 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3662 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 8.434634447097778}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9579, 0.2280, 0.6543,  ..., 0.1974, 0.2729, 0.8108])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 8.434634447097778 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4558 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9497, 0.5746, 0.8058,  ..., 0.6531, 0.4871, 0.2425])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.40420150756836 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9497, 0.5746, 0.8058,  ..., 0.6531, 0.4871, 0.2425])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.40420150756836 seconds
+
+[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48]
+[20.4, 20.4, 20.32, 24.04, 25.44, 28.52, 29.28, 29.6, 26.24, 25.04, 24.36, 24.48, 24.64, 24.64]
+14.201299667358398
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264}
+[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48, 20.76, 20.76, 20.84, 20.72, 20.92, 21.16, 21.12, 20.96, 21.04, 21.2]
+372.29999999999995
+18.615
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264, 'J_1KI': 72.3650776225781, 'W_1KI': 5.095665841691151, 'W_D': 4.6110449064282655, 'J_D': 65.48283049583436, 'W_D_1KI': 1.0116377591988295, 'J_D_1KI': 0.2219477312853948}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..7d51d5a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5424, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.584465742111206, "TIME_S_1KI": 1.9514133005367267, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.1352438354492, "W": 23.054671486011802, "J_1KI": 60.496910736624116, "W_1KI": 4.250492530606896, "W_D": 4.752671486011803, "J_D": 67.64438252258302, "W_D_1KI": 0.8762299937337394, "J_D_1KI": 0.16154682775327053}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..123630a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.23440051078796387}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.9209, 0.5249, 0.5745,  ..., 0.6031, 0.2037, 0.8915])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.23440051078796387 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4479 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 8.669935703277588}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5609, 0.8905, 0.0560,  ..., 0.5802, 0.7080, 0.9443])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 8.669935703277588 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5424 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.584465742111206}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.6743, 0.1388, 0.2848,  ..., 0.9657, 0.9554, 0.7994])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.584465742111206 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.6743, 0.1388, 0.2848,  ..., 0.9657, 0.9554, 0.7994])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.584465742111206 seconds
+
+[20.16, 20.08, 20.12, 20.28, 20.52, 20.28, 20.4, 20.4, 20.52, 20.36]
+[20.52, 20.52, 20.6, 25.24, 26.36, 28.8, 29.48, 27.2, 26.08, 23.96, 23.96, 23.84, 24.04, 24.28]
+14.232917785644531
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5424, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.584465742111206, 'TIME_S_1KI': 1.9514133005367267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.1352438354492, 'W': 23.054671486011802}
+[20.16, 20.08, 20.12, 20.28, 20.52, 20.28, 20.4, 20.4, 20.52, 20.36, 20.28, 20.48, 20.28, 20.32, 20.36, 20.4, 20.32, 20.36, 20.32, 20.4]
+366.04
+18.302
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5424, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.584465742111206, 'TIME_S_1KI': 1.9514133005367267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.1352438354492, 'W': 23.054671486011802, 'J_1KI': 60.496910736624116, 'W_1KI': 4.250492530606896, 'W_D': 4.752671486011803, 'J_D': 67.64438252258302, 'W_D_1KI': 0.8762299937337394, 'J_D_1KI': 0.16154682775327053}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..dda358e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4325, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273, "TIME_S_1KI": 2.5104655006717396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.7423639297486, "W": 23.58156237869744, "J_1KI": 77.62829223809216, "W_1KI": 5.45238436501675, "W_D": 5.256562378697442, "J_D": 74.84027779102334, "W_D_1KI": 1.2153901453635703, "J_D_1KI": 0.28101506251180813}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..d65e9d8
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.24277067184448242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.0081, 0.1025, 0.4852,  ..., 0.3080, 0.4481, 0.5761])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.24277067184448242 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4325 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2760, 0.7522, 0.4563,  ..., 0.1844, 0.1938, 0.2151])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.857763290405273 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2760, 0.7522, 0.4563,  ..., 0.1844, 0.1938, 0.2151])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.857763290405273 seconds
+
+[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48]
+[20.4, 20.44, 20.72, 24.96, 27.28, 28.88, 29.68, 27.12, 26.68, 26.68, 24.92, 25.16, 24.88, 24.6]
+14.237494468688965
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744}
+[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48, 20.2, 20.44, 20.6, 20.68, 20.64, 20.44, 20.36, 20.08, 20.16, 20.16]
+366.5
+18.325
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744, 'J_1KI': 77.62829223809216, 'W_1KI': 5.45238436501675, 'W_D': 5.256562378697442, 'J_D': 74.84027779102334, 'W_D_1KI': 1.2153901453635703, 'J_D_1KI': 0.28101506251180813}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..a97de83
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4213, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.434804439544678, "TIME_S_1KI": 2.4768109279716777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.78344322204595, "W": 22.486804268370133, "J_1KI": 75.90397418040493, "W_1KI": 5.337480244094501, "W_D": 3.4238042683701337, "J_D": 48.68970729637151, "W_D_1KI": 0.8126760665488093, "J_D_1KI": 0.1928972386776191}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..1abd6b9
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_080.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.24919962882995605}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.9078, 0.2533, 0.5140,  ..., 0.6043, 0.4804, 0.0746])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.24919962882995605 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4213 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.434804439544678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6004, 0.8484, 0.6906,  ..., 0.0809, 0.2650, 0.3823])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.434804439544678 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6004, 0.8484, 0.6906,  ..., 0.0809, 0.2650, 0.3823])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.434804439544678 seconds
+
+[20.2, 20.16, 20.88, 21.76, 22.52, 23.08, 23.36, 22.4, 21.68, 20.8]
+[20.48, 20.48, 21.2, 22.52, 22.52, 25.0, 26.08, 26.52, 26.56, 25.76, 24.92, 24.96, 25.0, 25.0]
+14.220937728881836
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4213, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.434804439544678, 'TIME_S_1KI': 2.4768109279716777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.78344322204595, 'W': 22.486804268370133}
+[20.2, 20.16, 20.88, 21.76, 22.52, 23.08, 23.36, 22.4, 21.68, 20.8, 20.96, 20.92, 20.32, 20.32, 20.52, 20.48, 20.4, 20.6, 20.64, 20.48]
+381.26
+19.063
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4213, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.434804439544678, 'TIME_S_1KI': 2.4768109279716777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.78344322204595, 'W': 22.486804268370133, 'J_1KI': 75.90397418040493, 'W_1KI': 5.337480244094501, 'W_D': 3.4238042683701337, 'J_D': 48.68970729637151, 'W_D_1KI': 0.8126760665488093, 'J_D_1KI': 0.1928972386776191}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..940e178
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404, "TIME_S_1KI": 2.423833153585579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.41223700523375, "W": 22.905972813666082, "J_1KI": 75.92748011287131, "W_1KI": 5.32820954028055, "W_D": 4.372972813666085, "J_D": 62.31526816534997, "W_D_1KI": 1.0172069815459606, "J_D_1KI": 0.23661478984553633}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..e7f8144
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.2965834140777588}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.4492, 0.3348, 0.2820,  ..., 0.6380, 0.4778, 0.0633])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.2965834140777588 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3540 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.64511513710022}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.6270, 0.7806, 0.1009,  ..., 0.0616, 0.3576, 0.1481])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 8.64511513710022 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4299 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3742, 0.9296, 0.9529,  ..., 0.5552, 0.6324, 0.8504])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.420058727264404 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3742, 0.9296, 0.9529,  ..., 0.5552, 0.6324, 0.8504])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.420058727264404 seconds
+
+[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68]
+[20.72, 20.36, 20.68, 24.16, 25.6, 27.56, 28.48, 26.48, 26.4, 24.6, 24.6, 24.68, 24.36, 24.12]
+14.25009274482727
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082}
+[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68, 20.32, 20.56, 20.4, 20.44, 20.48, 20.4, 20.72, 20.68, 20.76, 20.76]
+370.65999999999997
+18.532999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082, 'J_1KI': 75.92748011287131, 'W_1KI': 5.32820954028055, 'W_D': 4.372972813666085, 'J_D': 62.31526816534997, 'W_D_1KI': 1.0172069815459606, 'J_D_1KI': 0.23661478984553633}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..6bc90bf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4321, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.904336929321289, "TIME_S_1KI": 2.5235679077346194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.7823661422729, "W": 22.454372882102824, "J_1KI": 73.77513680682085, "W_1KI": 5.19656859109068, "W_D": 4.009372882102824, "J_D": 56.92064440250394, "W_D_1KI": 0.9278807873415468, "J_D_1KI": 0.214737511534725}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..2870a58
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.29803013801574707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.5656, 0.8065, 0.8914,  ..., 0.2667, 0.3733, 0.9779])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.29803013801574707 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3523 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 9.117663621902466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.9346, 0.5158, 0.7987,  ..., 0.7589, 0.1868, 0.6814])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 9.117663621902466 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4057 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 9.856732368469238}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.5477, 0.2740, 0.4778,  ..., 0.8531, 0.3991, 0.2979])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 9.856732368469238 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4321 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.904336929321289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.0196, 0.5237, 0.2833,  ..., 0.4769, 0.9606, 0.8820])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.904336929321289 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.0196, 0.5237, 0.2833,  ..., 0.4769, 0.9606, 0.8820])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.904336929321289 seconds
+
+[21.0, 20.92, 21.0, 20.84, 20.44, 20.36, 20.24, 20.12, 20.04, 20.16]
+[20.16, 20.08, 20.28, 21.96, 24.0, 25.96, 26.96, 26.84, 25.8, 24.72, 24.88, 24.8, 25.0, 25.0]
+14.196894645690918
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4321, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.904336929321289, 'TIME_S_1KI': 2.5235679077346194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.7823661422729, 'W': 22.454372882102824}
+[21.0, 20.92, 21.0, 20.84, 20.44, 20.36, 20.24, 20.12, 20.04, 20.16, 20.4, 20.32, 20.4, 20.48, 20.24, 20.24, 20.32, 20.56, 21.08, 21.04]
+368.9
+18.445
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4321, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.904336929321289, 'TIME_S_1KI': 2.5235679077346194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.7823661422729, 'W': 22.454372882102824, 'J_1KI': 73.77513680682085, 'W_1KI': 5.19656859109068, 'W_D': 4.009372882102824, 'J_D': 56.92064440250394, 'W_D_1KI': 0.9278807873415468, 'J_D_1KI': 0.214737511534725}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..7c8a651
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729, "TIME_S_1KI": 2.4973505897093755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.92240665435787, "W": 23.947333156007627, "J_1KI": 79.49541783310521, "W_1KI": 5.600405321797855, "W_D": 5.373333156007625, "J_D": 76.27222314262384, "W_D_1KI": 1.2566260888698844, "J_D_1KI": 0.2938788795299075}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..b48990a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.31911325454711914}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.3207, 0.9700, 0.3262,  ..., 0.9362, 0.9941, 0.3912])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.31911325454711914 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3290 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.077399730682373}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.5291, 0.8021, 0.1066,  ..., 0.7881, 0.0805, 0.4870])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 8.077399730682373 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4276 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.1501, 0.9759, 0.0443,  ..., 0.6183, 0.1649, 0.2013])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.67867112159729 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.1501, 0.9759, 0.0443,  ..., 0.6183, 0.1649, 0.2013])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.67867112159729 seconds
+
+[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28]
+[20.32, 20.36, 20.52, 24.24, 26.84, 29.28, 30.8, 30.56, 26.92, 26.36, 25.44, 25.44, 25.36, 25.4]
+14.19458293914795
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627}
+[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28, 20.76, 20.88, 20.68, 20.84, 20.88, 21.0, 20.76, 20.88, 20.84, 20.96]
+371.48
+18.574
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627, 'J_1KI': 79.49541783310521, 'W_1KI': 5.600405321797855, 'W_D': 5.373333156007625, 'J_D': 76.27222314262384, 'W_D_1KI': 1.2566260888698844, 'J_D_1KI': 0.2938788795299075}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..2a6c84e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4147, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.192631244659424, "TIME_S_1KI": 2.4578324679670662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.7802805519104, "W": 22.470932033330318, "J_1KI": 77.11123234914646, "W_1KI": 5.418599477533233, "W_D": 3.9009320333303172, "J_D": 55.51354693174359, "W_D_1KI": 0.940663620287031, "J_D_1KI": 0.226829906025327}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..ee273bc
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.2886645793914795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.9338, 0.7408, 0.0307,  ..., 0.7058, 0.8816, 0.1464])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.2886645793914795 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3637 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 9.20716404914856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.9437, 0.5183, 0.7873,  ..., 0.9915, 0.6438, 0.1403])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 9.20716404914856 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4147 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.192631244659424}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.1025, 0.8382, 0.5877,  ..., 0.7441, 0.8416, 0.8243])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.192631244659424 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.1025, 0.8382, 0.5877,  ..., 0.7441, 0.8416, 0.8243])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.192631244659424 seconds
+
+[20.28, 20.52, 20.48, 20.44, 21.0, 21.0, 21.12, 21.12, 21.12, 20.92]
+[20.56, 20.52, 20.72, 21.6, 23.2, 24.8, 26.04, 26.72, 26.72, 25.36, 25.32, 25.32, 24.88, 25.04]
+14.230841875076294
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4147, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.192631244659424, 'TIME_S_1KI': 2.4578324679670662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.7802805519104, 'W': 22.470932033330318}
+[20.28, 20.52, 20.48, 20.44, 21.0, 21.0, 21.12, 21.12, 21.12, 20.92, 20.28, 20.48, 20.24, 20.36, 20.4, 20.48, 20.6, 20.64, 20.4, 20.52]
+371.40000000000003
+18.57
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4147, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.192631244659424, 'TIME_S_1KI': 2.4578324679670662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.7802805519104, 'W': 22.470932033330318, 'J_1KI': 77.11123234914646, 'W_1KI': 5.418599477533233, 'W_D': 3.9009320333303172, 'J_D': 55.51354693174359, 'W_D_1KI': 0.940663620287031, 'J_D_1KI': 0.226829906025327}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..b1ad1ed
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4151, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944, "TIME_S_1KI": 2.8412091648742335, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 356.85992715835573, "W": 25.058368416517443, "J_1KI": 85.96962832049041, "W_1KI": 6.036706436164163, "W_D": 5.961368416517441, "J_D": 84.89672845101359, "W_D_1KI": 1.4361282622301712, "J_D_1KI": 0.3459716362876828}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..d09ad65
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.33879852294921875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.1080, 0.7126, 0.2741,  ..., 0.0141, 0.6709, 0.0416])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.33879852294921875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3099 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 7.838690519332886}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.5517, 0.4219, 0.5041,  ..., 0.2191, 0.6881, 0.0206])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 7.838690519332886 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4151 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.6544, 0.9539, 0.4979,  ..., 0.4670, 0.0344, 0.9767])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 11.793859243392944 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.6544, 0.9539, 0.4979,  ..., 0.4670, 0.0344, 0.9767])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 11.793859243392944 seconds
+
+[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48]
+[22.96, 22.84, 22.8, 26.76, 28.44, 29.48, 30.2, 28.12, 28.12, 28.04, 26.36, 27.0, 27.12, 27.12]
+14.241147756576538
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443}
+[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48, 21.08, 20.44, 20.4, 20.6, 20.6, 20.6, 20.64, 20.48, 20.48, 20.6]
+381.94
+19.097
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443, 'J_1KI': 85.96962832049041, 'W_1KI': 6.036706436164163, 'W_D': 5.961368416517441, 'J_D': 84.89672845101359, 'W_D_1KI': 1.4361282622301712, 'J_D_1KI': 0.3459716362876828}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..1d18bd7
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4044, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.143035173416138, "TIME_S_1KI": 2.5081689350682836, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 330.16996871948237, "W": 23.153445351321693, "J_1KI": 81.6444037387444, "W_1KI": 5.725382134352545, "W_D": 4.751445351321696, "J_D": 67.7559878978729, "W_D_1KI": 1.174937030494979, "J_D_1KI": 0.29053833592853096}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..2bdcafe
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_110.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.25959014892578125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.1398, 0.1495, 0.6361,  ..., 0.4484, 0.6307, 0.8400])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.25959014892578125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4044 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.143035173416138}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.3022, 0.4527, 0.1551,  ..., 0.4286, 0.4985, 0.4790])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.143035173416138 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.3022, 0.4527, 0.1551,  ..., 0.4286, 0.4985, 0.4790])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.143035173416138 seconds
+
+[20.64, 20.72, 20.32, 20.56, 20.36, 20.32, 20.52, 21.16, 21.16, 21.04]
+[21.28, 21.2, 20.76, 24.0, 26.08, 28.36, 29.2, 26.96, 26.92, 24.32, 24.24, 24.24, 24.4, 24.8]
+14.260079383850098
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4044, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.143035173416138, 'TIME_S_1KI': 2.5081689350682836, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.16996871948237, 'W': 23.153445351321693}
+[20.64, 20.72, 20.32, 20.56, 20.36, 20.32, 20.52, 21.16, 21.16, 21.04, 20.6, 20.2, 20.24, 19.92, 20.12, 20.24, 20.08, 20.32, 20.44, 20.44]
+368.03999999999996
+18.401999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4044, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.143035173416138, 'TIME_S_1KI': 2.5081689350682836, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.16996871948237, 'W': 23.153445351321693, 'J_1KI': 81.6444037387444, 'W_1KI': 5.725382134352545, 'W_D': 4.751445351321696, 'J_D': 67.7559878978729, 'W_D_1KI': 1.174937030494979, 'J_D_1KI': 0.29053833592853096}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..cde0fa8
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3898, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087, "TIME_S_1KI": 2.6363732571354768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.2206564903259, "W": 22.616425322779282, "J_1KI": 76.76261069531193, "W_1KI": 5.802058830882319, "W_D": 4.17842532277928, "J_D": 55.28155534458152, "W_D_1KI": 1.0719408216468136, "J_D_1KI": 0.27499764536860277}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..78918ca
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.29033493995666504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.0287, 0.9302, 0.4533,  ..., 0.1887, 0.8093, 0.0476])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.29033493995666504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3616 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 9.739661455154419}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.1589, 0.7243, 0.3638,  ..., 0.5413, 0.9750, 0.3668])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 9.739661455154419 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3898 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.5554, 0.1424, 0.7572,  ..., 0.7612, 0.5304, 0.9292])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.276582956314087 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.5554, 0.1424, 0.7572,  ..., 0.7612, 0.5304, 0.9292])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.276582956314087 seconds
+
+[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24]
+[20.12, 20.44, 21.0, 22.68, 25.16, 26.56, 26.56, 27.28, 26.48, 26.6, 24.04, 24.28, 24.76]
+13.230236530303955
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282}
+[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24, 20.4, 20.36, 20.36, 20.48, 20.52, 20.4, 20.52, 20.52, 20.68, 20.68]
+368.76000000000005
+18.438000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282, 'J_1KI': 76.76261069531193, 'W_1KI': 5.802058830882319, 'W_D': 4.17842532277928, 'J_D': 55.28155534458152, 'W_D_1KI': 1.0719408216468136, 'J_D_1KI': 0.27499764536860277}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..87760c6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4027, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.212517261505127, "TIME_S_1KI": 2.5360112395095924, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.28165603637694, "W": 23.272101675798375, "J_1KI": 82.51344823351799, "W_1KI": 5.779017053836199, "W_D": 5.076101675798377, "J_D": 72.47714428806304, "W_D_1KI": 1.2605169296742929, "J_D_1KI": 0.3130163719081929}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..f95bfbb
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.311673641204834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7695, 0.0539, 0.1056,  ..., 0.2425, 0.4084, 0.5084])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.311673641204834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3368 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.780853271484375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1115, 0.2867, 0.9875,  ..., 0.7692, 0.4068, 0.8761])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.780853271484375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4027 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.212517261505127}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.2551, 0.3437, 0.0774,  ..., 0.8176, 0.1852, 0.3451])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.212517261505127 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.2551, 0.3437, 0.0774,  ..., 0.8176, 0.1852, 0.3451])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.212517261505127 seconds
+
+[20.0, 20.04, 19.96, 20.28, 20.6, 20.64, 20.72, 20.56, 20.44, 20.44]
+[20.44, 20.44, 20.56, 24.2, 25.36, 27.88, 29.04, 29.4, 26.08, 25.44, 24.64, 24.64, 24.96, 24.96]
+14.278111219406128
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4027, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.212517261505127, 'TIME_S_1KI': 2.5360112395095924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.28165603637694, 'W': 23.272101675798375}
+[20.0, 20.04, 19.96, 20.28, 20.6, 20.64, 20.72, 20.56, 20.44, 20.44, 19.8, 19.8, 19.92, 19.96, 20.04, 20.24, 20.24, 20.08, 20.12, 20.32]
+363.91999999999996
+18.195999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4027, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.212517261505127, 'TIME_S_1KI': 2.5360112395095924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.28165603637694, 'W': 23.272101675798375, 'J_1KI': 82.51344823351799, 'W_1KI': 5.779017053836199, 'W_D': 5.076101675798377, 'J_D': 72.47714428806304, 'W_D_1KI': 1.2605169296742929, 'J_D_1KI': 0.3130163719081929}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..4d89138
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130629, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848, "TIME_S_1KI": 0.08456685409111948, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1447.5023779034616, "W": 103.45, "J_1KI": 11.081018593906878, "W_1KI": 0.7919374717711994, "W_D": 67.28425, "J_D": 941.4607237356305, "W_D_1KI": 0.5150789640891379, "J_D_1KI": 0.003943067497180089}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..531c512
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.02265310287475586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.1109, 0.3688, 0.8394,  ..., 0.5778, 0.7474, 0.0459])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.02265310287475586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46351', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 3.7256975173950195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.7624, 0.1488, 0.7288,  ..., 0.4517, 0.7426, 0.4871])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 3.7256975173950195 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130629', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.6772, 0.5623, 0.5296,  ..., 0.8301, 0.9620, 0.4995])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 11.046883583068848 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.6772, 0.5623, 0.5296,  ..., 0.8301, 0.9620, 0.4995])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 11.046883583068848 seconds
+
+[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12]
+[103.45]
+13.992289781570435
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45}
+[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12, 40.63, 39.49, 44.41, 39.16, 39.85, 39.65, 39.59, 39.3, 39.78, 39.59]
+723.315
+36.16575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45, 'J_1KI': 11.081018593906878, 'W_1KI': 0.7919374717711994, 'W_D': 67.28425, 'J_D': 941.4607237356305, 'W_D_1KI': 0.5150789640891379, 'J_D_1KI': 0.003943067497180089}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..19e53df
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120346, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.589650392532349, "TIME_S_1KI": 0.08799337238073844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1358.952927970886, "W": 103.07, "J_1KI": 11.292048991830937, "W_1KI": 0.856447243780433, "W_D": 67.06599999999999, "J_D": 884.2489285659789, "W_D_1KI": 0.5572765193691521, "J_D_1KI": 0.004630619375543451}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..bd641e1
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.028674840927124023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.9587, 0.4811, 0.8625,  ..., 0.9129, 0.1225, 0.6838])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.028674840927124023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36617', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 3.194762945175171}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.6315, 0.1026, 0.7910,  ..., 0.8210, 0.9570, 0.6355])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 3.194762945175171 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120346', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.589650392532349}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5481, 0.8859, 0.1543,  ..., 0.7989, 0.5471, 0.0598])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.589650392532349 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5481, 0.8859, 0.1543,  ..., 0.7989, 0.5471, 0.0598])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.589650392532349 seconds
+
+[40.32, 39.72, 39.52, 39.19, 39.62, 39.54, 39.3, 39.53, 39.19, 44.46]
+[103.07]
+13.184757232666016
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120346, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.589650392532349, 'TIME_S_1KI': 0.08799337238073844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1358.952927970886, 'W': 103.07}
+[40.32, 39.72, 39.52, 39.19, 39.62, 39.54, 39.3, 39.53, 39.19, 44.46, 39.95, 39.65, 39.59, 39.68, 39.55, 39.32, 45.78, 39.25, 39.47, 39.63]
+720.0800000000002
+36.004000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120346, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.589650392532349, 'TIME_S_1KI': 0.08799337238073844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1358.952927970886, 'W': 103.07, 'J_1KI': 11.292048991830937, 'W_1KI': 0.856447243780433, 'W_D': 67.06599999999999, 'J_D': 884.2489285659789, 'W_D_1KI': 0.5572765193691521, 'J_D_1KI': 0.004630619375543451}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..4246a76
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122232, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186, "TIME_S_1KI": 0.08191718068357864, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.7570972156525, "W": 103.87, "J_1KI": 11.001677933893355, "W_1KI": 0.8497774723476668, "W_D": 67.6435, "J_D": 875.7492702946663, "W_D_1KI": 0.5534025459781399, "J_D_1KI": 0.004527476814403265}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..78932cf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.02150440216064453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.2547, 0.3736, 0.4755,  ..., 0.0068, 0.6237, 0.5320])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.02150440216064453 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '48827', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.194327354431152}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.7417, 0.4748, 0.0091,  ..., 0.5058, 0.6479, 0.7190])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 4.194327354431152 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122232', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8581, 0.1137, 0.8207,  ..., 0.0910, 0.4048, 0.6394])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.012900829315186 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8581, 0.1137, 0.8207,  ..., 0.0910, 0.4048, 0.6394])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.012900829315186 seconds
+
+[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74]
+[103.87]
+12.946539878845215
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87}
+[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74, 39.91, 39.57, 39.25, 39.28, 39.45, 39.43, 39.72, 39.1, 39.4, 39.09]
+724.53
+36.2265
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87, 'J_1KI': 11.001677933893355, 'W_1KI': 0.8497774723476668, 'W_D': 67.6435, 'J_D': 875.7492702946663, 'W_D_1KI': 0.5534025459781399, 'J_D_1KI': 0.004527476814403265}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..2b73f1e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117384, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.323657274246216, "TIME_S_1KI": 0.08794773797320092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1313.2328101539613, "W": 103.76, "J_1KI": 11.187494123168074, "W_1KI": 0.8839364819736932, "W_D": 68.35725000000002, "J_D": 865.1598256736401, "W_D_1KI": 0.5823387344101413, "J_D_1KI": 0.0049609719758241435}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..3dbbbe4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.027828693389892578}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.1453, 0.8777, 0.6885,  ..., 0.8576, 0.3773, 0.4559])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.027828693389892578 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37730', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 3.3749337196350098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.1719, 0.8159, 0.9898,  ..., 0.9766, 0.3485, 0.4418])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 3.3749337196350098 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117384', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.323657274246216}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.6104, 0.1780, 0.2799,  ..., 0.5188, 0.3967, 0.6247])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.323657274246216 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.6104, 0.1780, 0.2799,  ..., 0.5188, 0.3967, 0.6247])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.323657274246216 seconds
+
+[41.71, 39.1, 39.14, 39.11, 39.01, 39.07, 39.54, 39.01, 39.19, 39.4]
+[103.76]
+12.656445741653442
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.323657274246216, 'TIME_S_1KI': 0.08794773797320092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.2328101539613, 'W': 103.76}
+[41.71, 39.1, 39.14, 39.11, 39.01, 39.07, 39.54, 39.01, 39.19, 39.4, 40.11, 39.48, 39.53, 39.22, 39.48, 39.26, 39.66, 39.01, 39.15, 38.97]
+708.0549999999998
+35.40274999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.323657274246216, 'TIME_S_1KI': 0.08794773797320092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.2328101539613, 'W': 103.76, 'J_1KI': 11.187494123168074, 'W_1KI': 0.8839364819736932, 'W_D': 68.35725000000002, 'J_D': 865.1598256736401, 'W_D_1KI': 0.5823387344101413, 'J_D_1KI': 0.0049609719758241435}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..634c711
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589, "TIME_S_1KI": 0.10023047952266595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1377.8382610416413, "W": 104.04, "J_1KI": 11.47070598113223, "W_1KI": 0.8661482875172747, "W_D": 68.01325, "J_D": 900.7233574374318, "W_D_1KI": 0.5662203000382956, "J_D_1KI": 0.004713867197574848}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..41dc918
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.028760671615600586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0546, 0.6478, 0.5019,  ..., 0.1774, 0.5884, 0.7696])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.028760671615600586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36508', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 3.191291332244873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7513, 0.6718, 0.2286,  ..., 0.8031, 0.6348, 0.1488])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 3.191291332244873 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120118', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0727, 0.2026, 0.8265,  ..., 0.2293, 0.8547, 0.4127])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 12.039484739303589 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0727, 0.2026, 0.8265,  ..., 0.2293, 0.8547, 0.4127])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 12.039484739303589 seconds
+
+[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23]
+[104.04]
+13.243351221084595
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04}
+[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23, 40.57, 39.23, 39.41, 39.27, 39.7, 44.55, 39.47, 39.31, 39.26, 39.61]
+720.5350000000001
+36.02675000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04, 'J_1KI': 11.47070598113223, 'W_1KI': 0.8661482875172747, 'W_D': 68.01325, 'J_D': 900.7233574374318, 'W_D_1KI': 0.5662203000382956, 'J_D_1KI': 0.004713867197574848}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..7b69b2e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120261, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.17577338218689, "TIME_S_1KI": 0.08461407590313476, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.515518093109, "W": 104.29999999999998, "J_1KI": 11.512589435420535, "W_1KI": 0.8672803319446868, "W_D": 68.51474999999998, "J_D": 909.4893057839868, "W_D_1KI": 0.5697171152742783, "J_D_1KI": 0.004737338915145211}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..990ec0e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.028615236282348633}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8200, 0.5639, 0.4277,  ..., 0.0741, 0.1847, 0.6824])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.028615236282348633 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36693', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 3.2036514282226562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.5381, 0.7793, 0.3349,  ..., 0.9005, 0.4954, 0.1180])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 3.2036514282226562 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120261', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.17577338218689}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.6109, 0.7009, 0.2057,  ..., 0.1567, 0.4641, 0.2303])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.17577338218689 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.6109, 0.7009, 0.2057,  ..., 0.1567, 0.4641, 0.2303])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.17577338218689 seconds
+
+[42.34, 39.73, 39.59, 39.75, 39.28, 39.48, 39.88, 39.38, 40.44, 39.3]
+[104.3]
+13.274357795715332
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120261, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.17577338218689, 'TIME_S_1KI': 0.08461407590313476, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.515518093109, 'W': 104.29999999999998}
+[42.34, 39.73, 39.59, 39.75, 39.28, 39.48, 39.88, 39.38, 40.44, 39.3, 40.72, 39.38, 39.58, 39.29, 39.9, 40.41, 39.95, 39.32, 39.36, 39.61]
+715.705
+35.785250000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120261, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.17577338218689, 'TIME_S_1KI': 0.08461407590313476, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.515518093109, 'W': 104.29999999999998, 'J_1KI': 11.512589435420535, 'W_1KI': 0.8672803319446868, 'W_D': 68.51474999999998, 'J_D': 909.4893057839868, 'W_D_1KI': 0.5697171152742783, 'J_D_1KI': 0.004737338915145211}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..206e20b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117637, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627, "TIME_S_1KI": 0.09034036808721975, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1348.652883746624, "W": 104.36999999999999, "J_1KI": 11.464529729138144, "W_1KI": 0.8872208573833061, "W_D": 68.05524999999999, "J_D": 879.3993404867051, "W_D_1KI": 0.5785190883820566, "J_D_1KI": 0.00491783272594555}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..2b9239c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.029236316680908203}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.3402, 0.2499, 0.3172,  ..., 0.1488, 0.4072, 0.2122])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.029236316680908203 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35914', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 3.2055723667144775}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.5217, 0.6973, 0.2862,  ..., 0.9929, 0.5018, 0.4794])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 3.2055723667144775 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117637', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.6504, 0.2249, 0.2739,  ..., 0.8117, 0.7999, 0.0068])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.62736988067627 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.6504, 0.2249, 0.2739,  ..., 0.8117, 0.7999, 0.0068])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.62736988067627 seconds
+
+[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38]
+[104.37]
+12.921844244003296
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999}
+[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38, 40.08, 39.64, 42.5, 41.53, 39.76, 39.42, 39.32, 39.21, 40.44, 41.29]
+726.2950000000001
+36.314750000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999, 'J_1KI': 11.464529729138144, 'W_1KI': 0.8872208573833061, 'W_D': 68.05524999999999, 'J_D': 879.3993404867051, 'W_D_1KI': 0.5785190883820566, 'J_D_1KI': 0.00491783272594555}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..c10f72f
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120015, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 11.120282411575317, "TIME_S_1KI": 0.09265743791672139, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1376.9933811092376, "W": 104.52, "J_1KI": 11.473510653745262, "W_1KI": 0.870891138607674, "W_D": 68.45974999999999, "J_D": 901.9194663451312, "W_D_1KI": 0.5704266133399991, "J_D_1KI": 0.004752960991042779}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..2845a38
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_040.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.02805638313293457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.1538, 0.8881, 0.1064,  ..., 0.9482, 0.5337, 0.0273])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.02805638313293457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37424', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 3.2741847038269043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.4384, 0.9373, 0.4871,  ..., 0.2356, 0.2534, 0.8446])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 3.2741847038269043 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120015', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 11.120282411575317}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7128, 0.1137, 0.0056,  ..., 0.4103, 0.4063, 0.8720])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 11.120282411575317 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7128, 0.1137, 0.0056,  ..., 0.4103, 0.4063, 0.8720])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 11.120282411575317 seconds
+
+[41.64, 39.35, 39.52, 39.43, 39.11, 39.72, 39.25, 39.84, 44.65, 39.54]
+[104.52]
+13.174448728561401
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120015, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 11.120282411575317, 'TIME_S_1KI': 0.09265743791672139, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.9933811092376, 'W': 104.52}
+[41.64, 39.35, 39.52, 39.43, 39.11, 39.72, 39.25, 39.84, 44.65, 39.54, 40.23, 39.89, 39.33, 39.08, 39.12, 44.38, 39.71, 39.24, 39.35, 39.06]
+721.205
+36.06025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120015, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 11.120282411575317, 'TIME_S_1KI': 0.09265743791672139, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.9933811092376, 'W': 104.52, 'J_1KI': 11.473510653745262, 'W_1KI': 0.870891138607674, 'W_D': 68.45974999999999, 'J_D': 901.9194663451312, 'W_D_1KI': 0.5704266133399991, 'J_D_1KI': 0.004752960991042779}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..3e95d74
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459, "TIME_S_1KI": 0.08677222450439022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1476.8643749785424, "W": 104.97, "J_1KI": 11.395822241090013, "W_1KI": 0.8099724530660432, "W_D": 69.20224999999999, "J_D": 973.6337781590818, "W_D_1KI": 0.5339803390510582, "J_D_1KI": 0.004120314043157312}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..958bdda
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.027070283889770508}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9579, 0.3823, 0.7927,  ..., 0.9257, 0.1735, 0.9344])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.027070283889770508 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38787', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 3.142535448074341}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7648, 0.3979, 0.1181,  ..., 0.8603, 0.9960, 0.9728])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 3.142535448074341 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129597', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.1180, 0.3748, 0.1643,  ..., 0.9664, 0.3966, 0.4847])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.245419979095459 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.1180, 0.3748, 0.1643,  ..., 0.9664, 0.3966, 0.4847])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.245419979095459 seconds
+
+[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3]
+[104.97]
+14.069394826889038
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97}
+[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3, 40.3, 39.27, 39.34, 39.54, 39.86, 39.21, 39.39, 41.12, 39.24, 39.74]
+715.355
+35.76775
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97, 'J_1KI': 11.395822241090013, 'W_1KI': 0.8099724530660432, 'W_D': 69.20224999999999, 'J_D': 973.6337781590818, 'W_D_1KI': 0.5339803390510582, 'J_D_1KI': 0.004120314043157312}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..36af758
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117251, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.673714637756348, "TIME_S_1KI": 0.09103303714046232, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1359.178429517746, "W": 103.34, "J_1KI": 11.59204125779521, "W_1KI": 0.8813570886389029, "W_D": 67.419, "J_D": 886.7277969775199, "W_D_1KI": 0.5749972281686296, "J_D_1KI": 0.004903985707317035}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..661f643
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.028463363647460938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.6452, 0.5948, 0.0594,  ..., 0.8774, 0.4998, 0.0874])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.028463363647460938 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36889', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 3.303457498550415}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.0413, 0.9306, 0.8847,  ..., 0.0971, 0.6839, 0.6839])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 3.303457498550415 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117251', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.673714637756348}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.1911, 0.5487, 0.3865,  ..., 0.4028, 0.6858, 0.4918])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.673714637756348 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.1911, 0.5487, 0.3865,  ..., 0.4028, 0.6858, 0.4918])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.673714637756348 seconds
+
+[40.43, 39.16, 39.11, 39.49, 39.45, 39.08, 39.11, 38.88, 44.34, 39.11]
+[103.34]
+13.152491092681885
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117251, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.673714637756348, 'TIME_S_1KI': 0.09103303714046232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.178429517746, 'W': 103.34}
+[40.43, 39.16, 39.11, 39.49, 39.45, 39.08, 39.11, 38.88, 44.34, 39.11, 40.07, 39.08, 39.26, 39.54, 41.34, 43.45, 39.2, 39.4, 39.22, 39.01]
+718.4200000000001
+35.92100000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117251, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.673714637756348, 'TIME_S_1KI': 0.09103303714046232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.178429517746, 'W': 103.34, 'J_1KI': 11.59204125779521, 'W_1KI': 0.8813570886389029, 'W_D': 67.419, 'J_D': 886.7277969775199, 'W_D_1KI': 0.5749972281686296, 'J_D_1KI': 0.004903985707317035}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..1a48113
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496, "TIME_S_1KI": 0.09067436739580567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1444.9410432815553, "W": 104.47000000000001, "J_1KI": 11.830009687753233, "W_1KI": 0.855315943737617, "W_D": 68.78025000000001, "J_D": 951.3104833173753, "W_D_1KI": 0.5631171095937516, "J_D_1KI": 0.004610347870460215}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..d27a247
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.02579522132873535}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.4101, 0.8209, 0.7582,  ..., 0.7042, 0.4089, 0.9250])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.02579522132873535 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '40705', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 3.499220609664917}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.5165, 0.5886, 0.2570,  ..., 0.5351, 0.5985, 0.2855])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 3.499220609664917 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122142', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1241, 0.2147, 0.8696,  ..., 0.1307, 0.0728, 0.1644])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 11.075148582458496 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1241, 0.2147, 0.8696,  ..., 0.1307, 0.0728, 0.1644])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 11.075148582458496 seconds
+
+[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66]
+[104.47]
+13.831157684326172
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001}
+[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66, 39.88, 39.36, 39.59, 39.25, 40.14, 39.15, 39.32, 39.55, 39.92, 39.6]
+713.7950000000001
+35.689750000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001, 'J_1KI': 11.830009687753233, 'W_1KI': 0.855315943737617, 'W_D': 68.78025000000001, 'J_D': 951.3104833173753, 'W_D_1KI': 0.5631171095937516, 'J_D_1KI': 0.004610347870460215}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..f8ce4db
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 115616, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.107509136199951, "TIME_S_1KI": 0.0874231000570851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1385.0806881904603, "W": 104.2, "J_1KI": 11.98000872016382, "W_1KI": 0.9012593412676446, "W_D": 68.209, "J_D": 906.6695648827554, "W_D_1KI": 0.5899615970107944, "J_D_1KI": 0.005102767757151211}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..c56ae67
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.027286767959594727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.8984, 0.0469, 0.3481,  ..., 0.4740, 0.2565, 0.1000])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.027286767959594727 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38480', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 3.4946682453155518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.1173, 0.8452, 0.0058,  ..., 0.9193, 0.4108, 0.2600])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 3.4946682453155518 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '115616', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.107509136199951}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.3457, 0.6349, 0.8927,  ..., 0.7830, 0.7463, 0.9414])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.107509136199951 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.3457, 0.6349, 0.8927,  ..., 0.7830, 0.7463, 0.9414])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.107509136199951 seconds
+
+[40.73, 39.3, 39.38, 43.93, 40.14, 39.25, 39.56, 39.33, 39.77, 39.89]
+[104.2]
+13.292520999908447
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115616, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.107509136199951, 'TIME_S_1KI': 0.0874231000570851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1385.0806881904603, 'W': 104.2}
+[40.73, 39.3, 39.38, 43.93, 40.14, 39.25, 39.56, 39.33, 39.77, 39.89, 45.99, 39.65, 39.68, 39.25, 39.25, 39.32, 39.23, 40.28, 39.58, 39.23]
+719.82
+35.991
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115616, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.107509136199951, 'TIME_S_1KI': 0.0874231000570851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1385.0806881904603, 'W': 104.2, 'J_1KI': 11.98000872016382, 'W_1KI': 0.9012593412676446, 'W_D': 68.209, 'J_D': 906.6695648827554, 'W_D_1KI': 0.5899615970107944, 'J_D_1KI': 0.005102767757151211}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..9c73b0a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121043, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519, "TIME_S_1KI": 0.08666592661125815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.8674520850182, "W": 104.75, "J_1KI": 11.540258024710377, "W_1KI": 0.8653949422932347, "W_D": 68.499, "J_D": 913.4512992875575, "W_D_1KI": 0.5659063308080599, "J_D_1KI": 0.004675250372248373}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..ca600af
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.028383970260620117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.6987, 0.1536, 0.6933,  ..., 0.9556, 0.5512, 0.6559])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.028383970260620117 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36992', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 3.2088987827301025}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2523, 0.5417, 0.6382,  ..., 0.2729, 0.0339, 0.5004])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 3.2088987827301025 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121043', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9743, 0.5130, 0.5318,  ..., 0.8200, 0.9366, 0.1557])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.490303754806519 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9743, 0.5130, 0.5318,  ..., 0.8200, 0.9366, 0.1557])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.490303754806519 seconds
+
+[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67]
+[104.75]
+13.33525013923645
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75}
+[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67, 40.18, 39.27, 39.88, 39.63, 40.85, 43.9, 39.97, 39.48, 39.27, 39.18]
+725.02
+36.251
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75, 'J_1KI': 11.540258024710377, 'W_1KI': 0.8653949422932347, 'W_D': 68.499, 'J_D': 913.4512992875575, 'W_D_1KI': 0.5659063308080599, 'J_D_1KI': 0.004675250372248373}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..1df7b10
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120877, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.189666986465454, "TIME_S_1KI": 0.08429781502242324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1361.2093210601809, "W": 104.18000000000002, "J_1KI": 11.261111055537288, "W_1KI": 0.8618678491358986, "W_D": 68.58550000000002, "J_D": 896.1338250103, "W_D_1KI": 0.567399091638608, "J_D_1KI": 0.004694020298639179}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..58dfc95
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.029041528701782227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8996, 0.1819, 0.9787,  ..., 0.8060, 0.2127, 0.8007])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.029041528701782227 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36155', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 3.14058518409729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.7195, 0.1055, 0.1671,  ..., 0.2379, 0.6021, 0.6792])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 3.14058518409729 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120877', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.189666986465454}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8991, 0.4291, 0.0091,  ..., 0.3125, 0.3059, 0.1879])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.189666986465454 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8991, 0.4291, 0.0091,  ..., 0.3125, 0.3059, 0.1879])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.189666986465454 seconds
+
+[40.29, 39.27, 39.66, 39.17, 39.24, 39.24, 39.26, 39.74, 39.48, 39.37]
+[104.18]
+13.065937042236328
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120877, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.189666986465454, 'TIME_S_1KI': 0.08429781502242324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1361.2093210601809, 'W': 104.18000000000002}
+[40.29, 39.27, 39.66, 39.17, 39.24, 39.24, 39.26, 39.74, 39.48, 39.37, 40.49, 39.82, 39.66, 39.31, 39.92, 39.38, 40.13, 39.49, 39.46, 39.17]
+711.8900000000001
+35.594500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120877, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.189666986465454, 'TIME_S_1KI': 0.08429781502242324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1361.2093210601809, 'W': 104.18000000000002, 'J_1KI': 11.261111055537288, 'W_1KI': 0.8618678491358986, 'W_D': 68.58550000000002, 'J_D': 896.1338250103, 'W_D_1KI': 0.567399091638608, 'J_D_1KI': 0.004694020298639179}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..6f29e46
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120423, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314, "TIME_S_1KI": 0.08943129150862493, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.2409217905997, "W": 104.71, "J_1KI": 11.984761397661574, "W_1KI": 0.8695182813914285, "W_D": 68.72225, "J_D": 947.213861498654, "W_D_1KI": 0.5706737915514478, "J_D_1KI": 0.0047389102708905095}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..d3e5c99
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.026674270629882812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.6187, 0.1169, 0.0618,  ..., 0.0036, 0.3565, 0.7239])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.026674270629882812 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '39363', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 3.4321558475494385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.5402, 0.1074, 0.0917,  ..., 0.8328, 0.8213, 0.8141])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 3.4321558475494385 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120423', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9766, 0.5800, 0.0793,  ..., 0.9152, 0.2119, 0.8249])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.76958441734314 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9766, 0.5800, 0.0793,  ..., 0.9152, 0.2119, 0.8249])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.76958441734314 seconds
+
+[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23]
+[104.71]
+13.783219575881958
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71}
+[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23, 53.53, 39.71, 40.17, 39.13, 39.61, 39.81, 39.45, 39.24, 39.21, 39.43]
+719.7549999999999
+35.98774999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71, 'J_1KI': 11.984761397661574, 'W_1KI': 0.8695182813914285, 'W_D': 68.72225, 'J_D': 947.213861498654, 'W_D_1KI': 0.5706737915514478, 'J_D_1KI': 0.0047389102708905095}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..f266af1
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 114354, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 11.189604997634888, "TIME_S_1KI": 0.09785057800894492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1410.8444130945206, "W": 104.18, "J_1KI": 12.337516948200506, "W_1KI": 0.9110306591811393, "W_D": 68.22325000000001, "J_D": 923.9046948133112, "W_D_1KI": 0.5965969708099411, "J_D_1KI": 0.0052171062735885156}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..e865b8e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_080.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.02819371223449707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5811, 0.0590, 0.8955,  ..., 0.6743, 0.3795, 0.4859])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.02819371223449707 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37242', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 3.4195492267608643}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.3591, 0.6873, 0.8611,  ..., 0.6830, 0.4965, 0.4869])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 3.4195492267608643 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '114354', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 11.189604997634888}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.3030, 0.2008, 0.0144,  ..., 0.0164, 0.8222, 0.8093])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 11.189604997634888 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.3030, 0.2008, 0.0144,  ..., 0.0164, 0.8222, 0.8093])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 11.189604997634888 seconds
+
+[40.04, 40.51, 39.79, 39.26, 39.95, 39.3, 39.37, 39.22, 39.38, 44.29]
+[104.18]
+13.542372941970825
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 114354, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 11.189604997634888, 'TIME_S_1KI': 0.09785057800894492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.8444130945206, 'W': 104.18}
+[40.04, 40.51, 39.79, 39.26, 39.95, 39.3, 39.37, 39.22, 39.38, 44.29, 40.85, 39.16, 39.24, 39.16, 39.19, 44.57, 39.27, 39.94, 39.71, 39.05]
+719.135
+35.95675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 114354, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 11.189604997634888, 'TIME_S_1KI': 0.09785057800894492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.8444130945206, 'W': 104.18, 'J_1KI': 12.337516948200506, 'W_1KI': 0.9110306591811393, 'W_D': 68.22325000000001, 'J_D': 923.9046948133112, 'W_D_1KI': 0.5965969708099411, 'J_D_1KI': 0.0052171062735885156}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..df39cfe
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123818, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852, "TIME_S_1KI": 0.10213301566095818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1404.5768914031983, "W": 105.84, "J_1KI": 11.343882887812743, "W_1KI": 0.8548030173318903, "W_D": 69.798, "J_D": 926.2722776470184, "W_D_1KI": 0.5637144841622381, "J_D_1KI": 0.004552766836503886}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..b82b034
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.027594804763793945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3579, 0.6537, 0.3650,  ..., 0.6368, 0.7499, 0.3578])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.027594804763793945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38050', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 3.2266926765441895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.5666, 0.3607, 0.0681,  ..., 0.1783, 0.0421, 0.2428])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 3.2266926765441895 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123818', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.9239, 0.0838, 0.6171,  ..., 0.0890, 0.6862, 0.2789])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 12.64590573310852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.9239, 0.0838, 0.6171,  ..., 0.0890, 0.6862, 0.2789])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 12.64590573310852 seconds
+
+[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12]
+[105.84]
+13.270756721496582
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84}
+[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12, 40.73, 39.74, 39.65, 39.19, 44.6, 39.27, 39.31, 39.32, 39.56, 39.64]
+720.8399999999999
+36.041999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84, 'J_1KI': 11.343882887812743, 'W_1KI': 0.8548030173318903, 'W_D': 69.798, 'J_D': 926.2722776470184, 'W_D_1KI': 0.5637144841622381, 'J_D_1KI': 0.004552766836503886}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..9bccef5
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129633, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 11.644664525985718, "TIME_S_1KI": 0.08982793367418573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1514.9775815963744, "W": 105.77999999999999, "J_1KI": 11.686666061854423, "W_1KI": 0.8159959269630417, "W_D": 70.1225, "J_D": 1004.2920728445054, "W_D_1KI": 0.5409309357956694, "J_D_1KI": 0.004172787297953989}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..ac05e35
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.028497934341430664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.7120, 0.8564, 0.6416,  ..., 0.1452, 0.7450, 0.8345])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.028497934341430664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36844', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 2.984281539916992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.2139, 0.0345, 0.3504,  ..., 0.9548, 0.7408, 0.1286])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 2.984281539916992 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129633', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 11.644664525985718}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8962, 0.9859, 0.6697,  ..., 0.6735, 0.2074, 0.6302])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 11.644664525985718 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8962, 0.9859, 0.6697,  ..., 0.6735, 0.2074, 0.6302])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 11.644664525985718 seconds
+
+[40.36, 39.19, 39.41, 39.52, 39.41, 40.55, 39.53, 39.22, 41.03, 39.36]
+[105.78]
+14.321966171264648
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129633, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 11.644664525985718, 'TIME_S_1KI': 0.08982793367418573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1514.9775815963744, 'W': 105.77999999999999}
+[40.36, 39.19, 39.41, 39.52, 39.41, 40.55, 39.53, 39.22, 41.03, 39.36, 40.6, 40.22, 39.22, 39.14, 39.24, 39.39, 39.44, 39.29, 39.39, 39.6]
+713.1499999999999
+35.65749999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129633, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 11.644664525985718, 'TIME_S_1KI': 0.08982793367418573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1514.9775815963744, 'W': 105.77999999999999, 'J_1KI': 11.686666061854423, 'W_1KI': 0.8159959269630417, 'W_D': 70.1225, 'J_D': 1004.2920728445054, 'W_D_1KI': 0.5409309357956694, 'J_D_1KI': 0.004172787297953989}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..d2c7977
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123367, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541, "TIME_S_1KI": 0.08752578008324383, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.98059548378, "W": 106.36000000000001, "J_1KI": 11.202190176333865, "W_1KI": 0.8621430366305415, "W_D": 70.495, "J_D": 915.9714373695851, "W_D_1KI": 0.5714250974733924, "J_D_1KI": 0.004631912079189673}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..61411f0
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.02869391441345215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6977, 0.1794, 0.1733,  ..., 0.5021, 0.2371, 0.5915])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.02869391441345215 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36593', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 3.28193736076355}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2038, 0.2117, 0.0495,  ..., 0.7353, 0.4058, 0.7220])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 3.28193736076355 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117073', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.964244604110718}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2057, 0.4266, 0.2763,  ..., 0.1377, 0.7100, 0.3501])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 9.964244604110718 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123367', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2200, 0.4810, 0.3293,  ..., 0.7198, 0.3850, 0.3915])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.797792911529541 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2200, 0.4810, 0.3293,  ..., 0.7198, 0.3850, 0.3915])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.797792911529541 seconds
+
+[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6]
+[106.36]
+12.9934241771698
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001}
+[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6, 41.41, 39.21, 39.7, 39.75, 39.19, 39.65, 39.19, 40.91, 42.9, 39.08]
+717.3000000000001
+35.865
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001, 'J_1KI': 11.202190176333865, 'W_1KI': 0.8621430366305415, 'W_D': 70.495, 'J_D': 915.9714373695851, 'W_D_1KI': 0.5714250974733924, 'J_D_1KI': 0.004631912079189673}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..4e0e1c0
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120963, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 11.165674448013306, "TIME_S_1KI": 0.09230652718610903, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.6955507874488, "W": 105.85, "J_1KI": 11.356328387915717, "W_1KI": 0.8750609690566536, "W_D": 69.60775, "J_D": 903.3524466256499, "W_D_1KI": 0.5754466241743342, "J_D_1KI": 0.004757211909214671}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..163cdad
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.027922391891479492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.7044, 0.9718, 0.4063,  ..., 0.8134, 0.1834, 0.4981])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.027922391891479492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37604', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 3.264139175415039}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.5057, 0.0011, 0.7436,  ..., 0.8079, 0.3676, 0.2944])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 3.264139175415039 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120963', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 11.165674448013306}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.8382, 0.6727, 0.9499,  ..., 0.3590, 0.1899, 0.3958])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 11.165674448013306 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.8382, 0.6727, 0.9499,  ..., 0.3590, 0.1899, 0.3958])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 11.165674448013306 seconds
+
+[41.25, 40.42, 39.99, 40.1, 40.02, 39.82, 39.84, 39.36, 44.69, 39.26]
+[105.85]
+12.97775673866272
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120963, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 11.165674448013306, 'TIME_S_1KI': 0.09230652718610903, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.6955507874488, 'W': 105.85}
+[41.25, 40.42, 39.99, 40.1, 40.02, 39.82, 39.84, 39.36, 44.69, 39.26, 40.13, 39.22, 39.32, 39.59, 39.64, 44.99, 39.36, 39.16, 39.44, 39.13]
+724.845
+36.24225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120963, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 11.165674448013306, 'TIME_S_1KI': 0.09230652718610903, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.6955507874488, 'W': 105.85, 'J_1KI': 11.356328387915717, 'W_1KI': 0.8750609690566536, 'W_D': 69.60775, 'J_D': 903.3524466256499, 'W_D_1KI': 0.5754466241743342, 'J_D_1KI': 0.004757211909214671}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..706b79f
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130315, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334, "TIME_S_1KI": 0.0862452585287445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1479.8992812085153, "W": 106.81, "J_1KI": 11.356323379568856, "W_1KI": 0.8196293596285923, "W_D": 71.0115, "J_D": 983.8954012502431, "W_D_1KI": 0.5449219199631662, "J_D_1KI": 0.004181574799241578}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..8b920ee
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.02874302864074707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.0653, 0.6055, 0.4558,  ..., 0.6884, 0.5872, 0.3001])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.02874302864074707 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36530', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 3.1528003215789795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.0149, 0.2762, 0.5141,  ..., 0.7208, 0.6938, 0.9073])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 3.1528003215789795 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121658', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 9.802452087402344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.9575, 0.4028, 0.3578,  ..., 0.2947, 0.5779, 0.9432])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 9.802452087402344 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130315', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.3162, 0.7565, 0.7515,  ..., 0.4628, 0.3648, 0.1630])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 11.23905086517334 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.3162, 0.7565, 0.7515,  ..., 0.4628, 0.3648, 0.1630])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 11.23905086517334 seconds
+
+[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98]
+[106.81]
+13.855437517166138
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81}
+[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98, 41.03, 39.28, 39.81, 39.38, 39.32, 39.19, 40.5, 39.21, 39.67, 39.6]
+715.97
+35.798500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81, 'J_1KI': 11.356323379568856, 'W_1KI': 0.8196293596285923, 'W_D': 71.0115, 'J_D': 983.8954012502431, 'W_D_1KI': 0.5449219199631662, 'J_D_1KI': 0.004181574799241578}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..d1fc578
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120860, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.835078716278076, "TIME_S_1KI": 0.08964983217175307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1468.0531423473358, "W": 105.19, "J_1KI": 12.146724659501372, "W_1KI": 0.8703458547079265, "W_D": 69.35275, "J_D": 967.9011556985379, "W_D_1KI": 0.5738271553863975, "J_D_1KI": 0.004747866584365361}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..4eeb54c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.033330678939819336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.1609, 0.2446, 0.3388,  ..., 0.7294, 0.1175, 0.4322])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.033330678939819336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31502', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 2.7367961406707764}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5708, 0.9990, 0.7261,  ..., 0.2817, 0.6295, 0.6453])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 2.7367961406707764 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120860', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.835078716278076}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6634, 0.1694, 0.2296,  ..., 0.0338, 0.8523, 0.3205])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.835078716278076 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6634, 0.1694, 0.2296,  ..., 0.0338, 0.8523, 0.3205])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.835078716278076 seconds
+
+[39.93, 39.96, 39.67, 39.83, 41.04, 39.69, 39.85, 39.54, 39.73, 39.63]
+[105.19]
+13.956204414367676
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120860, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.835078716278076, 'TIME_S_1KI': 0.08964983217175307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1468.0531423473358, 'W': 105.19}
+[39.93, 39.96, 39.67, 39.83, 41.04, 39.69, 39.85, 39.54, 39.73, 39.63, 41.61, 39.22, 39.41, 39.14, 39.63, 39.34, 39.68, 41.03, 39.64, 39.52]
+716.7449999999999
+35.83725
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120860, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.835078716278076, 'TIME_S_1KI': 0.08964983217175307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1468.0531423473358, 'W': 105.19, 'J_1KI': 12.146724659501372, 'W_1KI': 0.8703458547079265, 'W_D': 69.35275, 'J_D': 967.9011556985379, 'W_D_1KI': 0.5738271553863975, 'J_D_1KI': 0.004747866584365361}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..706d530
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122384, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164, "TIME_S_1KI": 0.08680102310549716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.5208738970757, "W": 106.31, "J_1KI": 11.223042831555397, "W_1KI": 0.8686593018695254, "W_D": 70.50425, "J_D": 910.9120409505963, "W_D_1KI": 0.5760904203163812, "J_D_1KI": 0.004707236406036584}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..4525251
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.029188871383666992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.1969, 0.7804, 0.2686,  ..., 0.5827, 0.0798, 0.7838])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.029188871383666992 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35972', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 3.086211681365967}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.5660, 0.8309, 0.9656,  ..., 0.1156, 0.8355, 0.1569])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 3.086211681365967 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122384', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.4590, 0.4390, 0.2483,  ..., 0.8018, 0.3092, 0.2454])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.623056411743164 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.4590, 0.4390, 0.2483,  ..., 0.8018, 0.3092, 0.2454])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.623056411743164 seconds
+
+[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32]
+[106.31]
+12.919959306716919
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31}
+[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32, 39.97, 39.29, 39.38, 39.24, 39.46, 39.22, 39.78, 41.14, 39.67, 39.16]
+716.115
+35.80575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31, 'J_1KI': 11.223042831555397, 'W_1KI': 0.8686593018695254, 'W_D': 70.50425, 'J_D': 910.9120409505963, 'W_D_1KI': 0.5760904203163812, 'J_D_1KI': 0.004707236406036584}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..f9c31d6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121667, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.714912414550781, "TIME_S_1KI": 0.08806753198937083, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1471.0119538068773, "W": 105.95, "J_1KI": 12.09047608477958, "W_1KI": 0.8708195320012823, "W_D": 70.13625000000002, "J_D": 973.7731207662823, "W_D_1KI": 0.5764607494226045, "J_D_1KI": 0.004738020576019828}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..b36f782
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.02883291244506836}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3732, 0.0574, 0.5467,  ..., 0.3088, 0.7435, 0.5865])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.02883291244506836 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36416', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 3.683297872543335}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3740, 0.4220, 0.4478,  ..., 0.4704, 0.3788, 0.3735])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 3.683297872543335 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '103811', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.958942651748657}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.8035, 0.5449, 0.4888,  ..., 0.6708, 0.9679, 0.8623])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.958942651748657 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121667', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.714912414550781}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3594, 0.3055, 0.9207,  ..., 0.9236, 0.8473, 0.9639])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.714912414550781 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3594, 0.3055, 0.9207,  ..., 0.9236, 0.8473, 0.9639])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.714912414550781 seconds
+
+[40.81, 39.31, 39.39, 39.28, 39.62, 39.64, 39.64, 39.98, 39.31, 39.76]
+[105.95]
+13.884020328521729
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121667, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.714912414550781, 'TIME_S_1KI': 0.08806753198937083, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1471.0119538068773, 'W': 105.95}
+[40.81, 39.31, 39.39, 39.28, 39.62, 39.64, 39.64, 39.98, 39.31, 39.76, 40.19, 39.69, 39.9, 40.2, 39.34, 39.26, 39.38, 39.89, 42.24, 39.65]
+716.2749999999999
+35.81374999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121667, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.714912414550781, 'TIME_S_1KI': 0.08806753198937083, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1471.0119538068773, 'W': 105.95, 'J_1KI': 12.09047608477958, 'W_1KI': 0.8708195320012823, 'W_D': 70.13625000000002, 'J_D': 973.7731207662823, 'W_D_1KI': 0.5764607494226045, 'J_D_1KI': 0.004738020576019828}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..253b8c7
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 118951, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.958328008651733, "TIME_S_1KI": 0.0921247236984282, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1244.7510617017745, "W": 85.66, "J_1KI": 10.46440182681755, "W_1KI": 0.7201284562550967, "W_D": 68.79275, "J_D": 999.64801073879, "W_D_1KI": 0.578328471387378, "J_D_1KI": 0.004861905081818379}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..cc25f85
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.02218484878540039}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.8422, 0.7050, 0.1082,  ..., 0.1730, 0.8671, 0.7264])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.02218484878540039 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '47329', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 4.177785634994507}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4803, 0.7198, 0.6367,  ..., 0.1841, 0.4829, 0.5995])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 4.177785634994507 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '118951', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.958328008651733}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4882, 0.5169, 0.2032,  ..., 0.7665, 0.8923, 0.8813])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.958328008651733 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4882, 0.5169, 0.2032,  ..., 0.7665, 0.8923, 0.8813])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.958328008651733 seconds
+
+[19.13, 18.64, 19.2, 18.61, 18.66, 18.55, 19.69, 18.7, 18.45, 18.58]
+[85.66]
+14.531298875808716
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 118951, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.958328008651733, 'TIME_S_1KI': 0.0921247236984282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.7510617017745, 'W': 85.66}
+[19.13, 18.64, 19.2, 18.61, 18.66, 18.55, 19.69, 18.7, 18.45, 18.58, 18.68, 18.33, 18.75, 18.56, 18.36, 18.54, 18.71, 18.72, 19.33, 18.7]
+337.345
+16.867250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 118951, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.958328008651733, 'TIME_S_1KI': 0.0921247236984282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.7510617017745, 'W': 85.66, 'J_1KI': 10.46440182681755, 'W_1KI': 0.7201284562550967, 'W_D': 68.79275, 'J_D': 999.64801073879, 'W_D_1KI': 0.578328471387378, 'J_D_1KI': 0.004861905081818379}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..fa96889
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111707, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.419404983520508, "TIME_S_1KI": 0.09327441416849891, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.4035894393921, "W": 84.72, "J_1KI": 10.52220173703879, "W_1KI": 0.7584126330489583, "W_D": 67.76225, "J_D": 940.1321043258905, "W_D_1KI": 0.6066070165701344, "J_D_1KI": 0.005430340234453834}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..db0d18c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_010.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.02460479736328125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.8975, 0.4571, 0.4276,  ..., 0.6385, 0.8727, 0.9048])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.02460479736328125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42674', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 4.011150360107422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4799, 0.4037, 0.8480,  ..., 0.6411, 0.9057, 0.7472])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 4.011150360107422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '111707', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.419404983520508}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4140, 0.1397, 0.7939,  ..., 0.4522, 0.3425, 0.6132])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.419404983520508 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4140, 0.1397, 0.7939,  ..., 0.4522, 0.3425, 0.6132])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.419404983520508 seconds
+
+[22.49, 18.54, 18.47, 18.72, 18.67, 18.52, 18.56, 18.68, 18.63, 18.54]
+[84.72]
+13.873980045318604
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111707, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.419404983520508, 'TIME_S_1KI': 0.09327441416849891, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.4035894393921, 'W': 84.72}
+[22.49, 18.54, 18.47, 18.72, 18.67, 18.52, 18.56, 18.68, 18.63, 18.54, 19.05, 19.03, 18.79, 18.5, 18.58, 18.82, 19.9, 18.74, 18.5, 18.93]
+339.155
+16.957749999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111707, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.419404983520508, 'TIME_S_1KI': 0.09327441416849891, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.4035894393921, 'W': 84.72, 'J_1KI': 10.52220173703879, 'W_1KI': 0.7584126330489583, 'W_D': 67.76225, 'J_D': 940.1321043258905, 'W_D_1KI': 0.6066070165701344, 'J_D_1KI': 0.005430340234453834}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..2fcbe89
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111343, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.612449407577515, "TIME_S_1KI": 0.09531312617387276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1276.1634995460508, "W": 85.24, "J_1KI": 11.46155123847975, "W_1KI": 0.7655622715392975, "W_D": 68.50299999999999, "J_D": 1025.5869100117682, "W_D_1KI": 0.6152429878842853, "J_D_1KI": 0.005525654849288103}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..88be5a2
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024092912673950195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.5683, 0.4138, 0.4073,  ..., 0.3902, 0.8427, 0.0854])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.024092912673950195 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43581', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.109806299209595}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.5410, 0.2199, 0.7901,  ..., 0.5082, 0.9299, 0.4005])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 4.109806299209595 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '111343', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.612449407577515}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.4631, 0.6798, 0.1713,  ..., 0.4196, 0.8931, 0.1487])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.612449407577515 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.4631, 0.6798, 0.1713,  ..., 0.4196, 0.8931, 0.1487])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.612449407577515 seconds
+
+[19.22, 18.22, 18.5, 18.78, 18.53, 18.22, 18.28, 18.61, 18.44, 18.23]
+[85.24]
+14.971415996551514
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111343, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.612449407577515, 'TIME_S_1KI': 0.09531312617387276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1276.1634995460508, 'W': 85.24}
+[19.22, 18.22, 18.5, 18.78, 18.53, 18.22, 18.28, 18.61, 18.44, 18.23, 18.99, 19.53, 18.43, 18.28, 18.91, 18.45, 18.67, 18.31, 19.19, 18.34]
+334.74
+16.737000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111343, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.612449407577515, 'TIME_S_1KI': 0.09531312617387276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1276.1634995460508, 'W': 85.24, 'J_1KI': 11.46155123847975, 'W_1KI': 0.7655622715392975, 'W_D': 68.50299999999999, 'J_D': 1025.5869100117682, 'W_D_1KI': 0.6152429878842853, 'J_D_1KI': 0.005525654849288103}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..0e3cd1c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109635, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.219510316848755, "TIME_S_1KI": 0.09321394004513846, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.2720153522491, "W": 85.28999999999999, "J_1KI": 10.984375567585618, "W_1KI": 0.7779449993159119, "W_D": 68.35624999999999, "J_D": 965.1719890892504, "W_D_1KI": 0.6234893054225383, "J_D_1KI": 0.005686954945250498}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..fc6daf0
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_020.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-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,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3774, 0.5113, 0.3919,  ..., 0.0394, 0.9652, 0.4736])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.024602413177490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42678', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 4.087339401245117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.1956, 0.6916, 0.1623,  ..., 0.7373, 0.9830, 0.4900])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 4.087339401245117 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '109635', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.219510316848755}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3714, 0.7405, 0.3620,  ..., 0.7668, 0.6589, 0.5395])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.219510316848755 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3714, 0.7405, 0.3620,  ..., 0.7668, 0.6589, 0.5395])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.219510316848755 seconds
+
+[19.32, 18.72, 18.65, 18.75, 18.85, 18.57, 18.5, 18.85, 19.1, 18.73]
+[85.29]
+14.119732856750488
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109635, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.219510316848755, 'TIME_S_1KI': 0.09321394004513846, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.2720153522491, 'W': 85.28999999999999}
+[19.32, 18.72, 18.65, 18.75, 18.85, 18.57, 18.5, 18.85, 19.1, 18.73, 19.41, 18.51, 19.64, 19.04, 18.72, 18.47, 18.9, 18.59, 18.86, 18.45]
+338.67499999999995
+16.933749999999996
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109635, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.219510316848755, 'TIME_S_1KI': 0.09321394004513846, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.2720153522491, 'W': 85.28999999999999, 'J_1KI': 10.984375567585618, 'W_1KI': 0.7779449993159119, 'W_D': 68.35624999999999, 'J_D': 965.1719890892504, 'W_D_1KI': 0.6234893054225383, 'J_D_1KI': 0.005686954945250498}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..e3e64e8
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104212, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.081001043319702, "TIME_S_1KI": 0.0967355107216031, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.1547214508057, "W": 85.36, "J_1KI": 11.334152702671533, "W_1KI": 0.8190995278854643, "W_D": 68.42425, "J_D": 946.8091137444973, "W_D_1KI": 0.6565870533143975, "J_D_1KI": 0.006300493736943898}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..f0ae3c3
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.02489328384399414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.3735, 0.0521, 0.5930,  ..., 0.9245, 0.5806, 0.1020])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.02489328384399414 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42180', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 4.24988055229187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0848, 0.9892, 0.3897,  ..., 0.8536, 0.7541, 0.3970])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 4.24988055229187 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104212', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.081001043319702}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7565, 0.9410, 0.8976,  ..., 0.0615, 0.0494, 0.4926])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.081001043319702 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7565, 0.9410, 0.8976,  ..., 0.0615, 0.0494, 0.4926])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.081001043319702 seconds
+
+[19.13, 19.56, 18.72, 18.55, 18.88, 18.69, 18.9, 18.45, 18.62, 18.63]
+[85.36]
+13.837332725524902
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.081001043319702, 'TIME_S_1KI': 0.0967355107216031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.1547214508057, 'W': 85.36}
+[19.13, 19.56, 18.72, 18.55, 18.88, 18.69, 18.9, 18.45, 18.62, 18.63, 19.24, 18.49, 18.65, 18.66, 20.06, 18.54, 18.65, 18.59, 18.9, 18.61]
+338.715
+16.93575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.081001043319702, 'TIME_S_1KI': 0.0967355107216031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.1547214508057, 'W': 85.36, 'J_1KI': 11.334152702671533, 'W_1KI': 0.8190995278854643, 'W_D': 68.42425, 'J_D': 946.8091137444973, 'W_D_1KI': 0.6565870533143975, 'J_D_1KI': 0.006300493736943898}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..556ac0e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 106001, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.203181266784668, "TIME_S_1KI": 0.09625551897420466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1212.5820958518982, "W": 85.13, "J_1KI": 11.439345816095113, "W_1KI": 0.803105631078952, "W_D": 68.24725, "J_D": 972.1061134867667, "W_D_1KI": 0.6438359072084224, "J_D_1KI": 0.006073866352283681}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..e7b9a92
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_030.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.023807287216186523}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.1166, 0.1476, 0.7268,  ..., 0.0692, 0.0271, 0.9622])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.023807287216186523 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44104', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 4.368737697601318}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.6663, 0.6707, 0.4953,  ..., 0.8936, 0.9646, 0.3183])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 4.368737697601318 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '106001', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.203181266784668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8661, 0.0850, 0.9228,  ..., 0.9945, 0.1677, 0.9296])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.203181266784668 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8661, 0.0850, 0.9228,  ..., 0.9945, 0.1677, 0.9296])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.203181266784668 seconds
+
+[19.16, 18.67, 18.69, 18.49, 18.55, 19.64, 19.21, 18.44, 18.74, 18.68]
+[85.13]
+14.243886947631836
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.203181266784668, 'TIME_S_1KI': 0.09625551897420466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.5820958518982, 'W': 85.13}
+[19.16, 18.67, 18.69, 18.49, 18.55, 19.64, 19.21, 18.44, 18.74, 18.68, 18.98, 18.59, 18.8, 18.46, 18.69, 18.39, 18.97, 18.4, 19.28, 18.47]
+337.65500000000003
+16.88275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.203181266784668, 'TIME_S_1KI': 0.09625551897420466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.5820958518982, 'W': 85.13, 'J_1KI': 11.439345816095113, 'W_1KI': 0.803105631078952, 'W_D': 68.24725, 'J_D': 972.1061134867667, 'W_D_1KI': 0.6438359072084224, 'J_D_1KI': 0.006073866352283681}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..96f01c7
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 108087, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.658639669418335, "TIME_S_1KI": 0.09861167087085713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.6392674064637, "W": 85.63000000000001, "J_1KI": 11.006312205968003, "W_1KI": 0.792232183333796, "W_D": 68.6405, "J_D": 953.6077792177201, "W_D_1KI": 0.635048618242712, "J_D_1KI": 0.005875346880223449}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..3e77f2c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.024932384490966797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.9497, 0.8791, 0.4095,  ..., 0.2660, 0.5237, 0.9335])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.024932384490966797 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42113', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 4.0910210609436035}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.3329, 0.1103, 0.0954,  ..., 0.6205, 0.8185, 0.8635])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 4.0910210609436035 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '108087', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.658639669418335}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4504, 0.2201, 0.9468,  ..., 0.4623, 0.1299, 0.0544])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.658639669418335 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4504, 0.2201, 0.9468,  ..., 0.4623, 0.1299, 0.0544])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.658639669418335 seconds
+
+[19.09, 18.49, 18.84, 18.58, 18.97, 18.67, 19.82, 19.26, 19.0, 18.71]
+[85.63]
+13.892786026000977
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.658639669418335, 'TIME_S_1KI': 0.09861167087085713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.6392674064637, 'W': 85.63000000000001}
+[19.09, 18.49, 18.84, 18.58, 18.97, 18.67, 19.82, 19.26, 19.0, 18.71, 18.89, 18.81, 18.96, 18.53, 18.69, 19.02, 18.99, 18.72, 18.87, 18.45]
+339.78999999999996
+16.9895
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.658639669418335, 'TIME_S_1KI': 0.09861167087085713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.6392674064637, 'W': 85.63000000000001, 'J_1KI': 11.006312205968003, 'W_1KI': 0.792232183333796, 'W_D': 68.6405, 'J_D': 953.6077792177201, 'W_D_1KI': 0.635048618242712, 'J_D_1KI': 0.005875346880223449}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..e247ba1
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104002, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.971255779266357, "TIME_S_1KI": 0.10549081536188108, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.7131151080132, "W": 85.65, "J_1KI": 11.766245986692692, "W_1KI": 0.8235418549643276, "W_D": 68.38650000000001, "J_D": 977.0631283868553, "W_D_1KI": 0.6575498548104846, "J_D_1KI": 0.006322473171770587}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..9ffc3d5
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_040.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.02410125732421875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.8971, 0.0893, 0.5029,  ..., 0.9701, 0.1617, 0.1863])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.02410125732421875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43566', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 4.398398399353027}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7030, 0.1744, 0.1811,  ..., 0.0201, 0.2116, 0.3812])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 4.398398399353027 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104002', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.971255779266357}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.9929, 0.5598, 0.6631,  ..., 0.2729, 0.4816, 0.4870])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.971255779266357 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.9929, 0.5598, 0.6631,  ..., 0.2729, 0.4816, 0.4870])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.971255779266357 seconds
+
+[19.36, 18.55, 18.56, 18.38, 19.15, 18.7, 18.38, 22.08, 18.9, 18.66]
+[85.65]
+14.287368535995483
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104002, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.971255779266357, 'TIME_S_1KI': 0.10549081536188108, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.7131151080132, 'W': 85.65}
+[19.36, 18.55, 18.56, 18.38, 19.15, 18.7, 18.38, 22.08, 18.9, 18.66, 19.25, 18.52, 19.24, 23.52, 18.57, 18.44, 19.03, 18.63, 18.73, 18.51]
+345.27
+17.2635
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104002, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.971255779266357, 'TIME_S_1KI': 0.10549081536188108, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.7131151080132, 'W': 85.65, 'J_1KI': 11.766245986692692, 'W_1KI': 0.8235418549643276, 'W_D': 68.38650000000001, 'J_D': 977.0631283868553, 'W_D_1KI': 0.6575498548104846, 'J_D_1KI': 0.006322473171770587}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..f0db0de
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100525, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.002086639404297, "TIME_S_1KI": 0.09949849927286046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1185.355191230774, "W": 85.6, "J_1KI": 11.791645772004715, "W_1KI": 0.8515294702810245, "W_D": 68.72399999999999, "J_D": 951.6629691839216, "W_D_1KI": 0.6836508331260879, "J_D_1KI": 0.0068008041096850325}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..351314e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.02538919448852539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.5332, 0.1841, 0.2305,  ..., 0.7034, 0.6077, 0.7798])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.02538919448852539 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41356', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.319673299789429}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.4945, 0.7975, 0.9129,  ..., 0.8365, 0.5676, 0.7345])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 4.319673299789429 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100525', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.002086639404297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.5847, 0.9197, 0.3856,  ..., 0.5045, 0.0806, 0.9239])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.002086639404297 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.5847, 0.9197, 0.3856,  ..., 0.5045, 0.0806, 0.9239])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.002086639404297 seconds
+
+[18.95, 18.66, 19.21, 18.65, 18.7, 18.61, 18.93, 18.45, 18.7, 18.82]
+[85.6]
+13.847607374191284
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100525, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.002086639404297, 'TIME_S_1KI': 0.09949849927286046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.355191230774, 'W': 85.6}
+[18.95, 18.66, 19.21, 18.65, 18.7, 18.61, 18.93, 18.45, 18.7, 18.82, 19.0, 18.49, 18.6, 18.39, 20.14, 18.43, 18.49, 18.66, 18.8, 18.45]
+337.5199999999999
+16.875999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100525, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.002086639404297, 'TIME_S_1KI': 0.09949849927286046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.355191230774, 'W': 85.6, 'J_1KI': 11.791645772004715, 'W_1KI': 0.8515294702810245, 'W_D': 68.72399999999999, 'J_D': 951.6629691839216, 'W_D_1KI': 0.6836508331260879, 'J_D_1KI': 0.0068008041096850325}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..4c67cf4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105868, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.721794366836548, "TIME_S_1KI": 0.10127511964745294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1193.5127460670471, "W": 86.12, "J_1KI": 11.273593022131779, "W_1KI": 0.8134658253674387, "W_D": 69.02475000000001, "J_D": 956.5945067242386, "W_D_1KI": 0.6519887973703103, "J_D_1KI": 0.006158506794974027}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..2061003
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.02422165870666504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8427, 0.1700, 0.2863,  ..., 0.6763, 0.6407, 0.6483])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.02422165870666504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43349', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 4.515822649002075}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8406, 0.8037, 0.7094,  ..., 0.6008, 0.6718, 0.0722])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 4.515822649002075 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100793', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 9.996608018875122}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.5107, 0.6674, 0.7206,  ..., 0.9225, 0.6282, 0.2151])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 9.996608018875122 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105868', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.721794366836548}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.2187, 0.5129, 0.0714,  ..., 0.9129, 0.4139, 0.6138])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.721794366836548 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.2187, 0.5129, 0.0714,  ..., 0.9129, 0.4139, 0.6138])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.721794366836548 seconds
+
+[19.42, 18.48, 18.58, 18.59, 22.39, 18.82, 18.89, 19.08, 18.96, 18.77]
+[86.12]
+13.858717441558838
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105868, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.721794366836548, 'TIME_S_1KI': 0.10127511964745294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1193.5127460670471, 'W': 86.12}
+[19.42, 18.48, 18.58, 18.59, 22.39, 18.82, 18.89, 19.08, 18.96, 18.77, 19.06, 18.83, 19.16, 18.65, 18.7, 18.95, 18.56, 18.55, 18.63, 18.92]
+341.90500000000003
+17.09525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105868, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.721794366836548, 'TIME_S_1KI': 0.10127511964745294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1193.5127460670471, 'W': 86.12, 'J_1KI': 11.273593022131779, 'W_1KI': 0.8134658253674387, 'W_D': 69.02475000000001, 'J_D': 956.5945067242386, 'W_D_1KI': 0.6519887973703103, 'J_D_1KI': 0.006158506794974027}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..dcdc997
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105102, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.51480507850647, "TIME_S_1KI": 0.10004381532707722, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1208.6983181548117, "W": 85.70999999999998, "J_1KI": 11.500240891275253, "W_1KI": 0.8154935205800078, "W_D": 68.93374999999997, "J_D": 972.1165288659927, "W_D_1KI": 0.6558747692717548, "J_D_1KI": 0.0062403643058339025}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..74c7c49
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.02582073211669922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.9834, 0.8900, 0.8704,  ..., 0.9063, 0.0304, 0.0345])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.02582073211669922 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '40664', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 4.062415599822998}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.3357, 0.2120, 0.4208,  ..., 0.4775, 0.1882, 0.4136])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 4.062415599822998 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105102', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.51480507850647}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1109, 0.0481, 0.9697,  ..., 0.8619, 0.5058, 0.3084])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.51480507850647 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1109, 0.0481, 0.9697,  ..., 0.8619, 0.5058, 0.3084])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.51480507850647 seconds
+
+[18.92, 18.5, 18.86, 19.4, 18.53, 18.55, 18.81, 18.88, 18.58, 19.05]
+[85.71]
+14.102185487747192
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105102, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.51480507850647, 'TIME_S_1KI': 0.10004381532707722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.6983181548117, 'W': 85.70999999999998}
+[18.92, 18.5, 18.86, 19.4, 18.53, 18.55, 18.81, 18.88, 18.58, 19.05, 18.71, 18.29, 18.49, 18.92, 18.32, 18.49, 18.42, 18.56, 18.48, 18.21]
+335.525
+16.776249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105102, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.51480507850647, 'TIME_S_1KI': 0.10004381532707722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.6983181548117, 'W': 85.70999999999998, 'J_1KI': 11.500240891275253, 'W_1KI': 0.8154935205800078, 'W_D': 68.93374999999997, 'J_D': 972.1165288659927, 'W_D_1KI': 0.6558747692717548, 'J_D_1KI': 0.0062403643058339025}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..eb47088
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104110, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.124482154846191, "TIME_S_1KI": 0.09724793156129279, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1188.1959572124483, "W": 85.93000000000002, "J_1KI": 11.412889801291406, "W_1KI": 0.8253770050907696, "W_D": 69.00575000000002, "J_D": 954.1761104900839, "W_D_1KI": 0.6628157717798484, "J_D_1KI": 0.006366494782248087}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..16079c7
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_060.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.024419784545898438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.9990, 0.8965, 0.4088,  ..., 0.1609, 0.8245, 0.2287])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.024419784545898438 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42997', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 4.3364293575286865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.3536, 0.2219, 0.4405,  ..., 0.9349, 0.9084, 0.5321])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 4.3364293575286865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104110', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.124482154846191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6115, 0.3616, 0.3804,  ..., 0.1177, 0.1645, 0.4359])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.124482154846191 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6115, 0.3616, 0.3804,  ..., 0.1177, 0.1645, 0.4359])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.124482154846191 seconds
+
+[18.89, 18.58, 18.58, 18.63, 19.33, 20.43, 18.58, 18.83, 18.93, 18.93]
+[85.93]
+13.827486991882324
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104110, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.124482154846191, 'TIME_S_1KI': 0.09724793156129279, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1188.1959572124483, 'W': 85.93000000000002}
+[18.89, 18.58, 18.58, 18.63, 19.33, 20.43, 18.58, 18.83, 18.93, 18.93, 19.06, 18.86, 18.53, 18.3, 18.52, 18.89, 18.53, 18.77, 18.5, 18.51]
+338.485
+16.92425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104110, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.124482154846191, 'TIME_S_1KI': 0.09724793156129279, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1188.1959572124483, 'W': 85.93000000000002, 'J_1KI': 11.412889801291406, 'W_1KI': 0.8253770050907696, 'W_D': 69.00575000000002, 'J_D': 954.1761104900839, 'W_D_1KI': 0.6628157717798484, 'J_D_1KI': 0.006366494782248087}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..92c3f69
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103844, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 11.030998468399048, "TIME_S_1KI": 0.10622663291474758, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.5098596334458, "W": 85.7, "J_1KI": 11.45477696962218, "W_1KI": 0.8252763761026155, "W_D": 68.47900000000001, "J_D": 950.4836135103704, "W_D_1KI": 0.6594410847039791, "J_D_1KI": 0.006350305118292623}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..1f7dd97
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.0243070125579834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.3510, 0.2711, 0.2123,  ..., 0.7532, 0.4454, 0.2960])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.0243070125579834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43197', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 4.367758750915527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.0208, 0.4742, 0.2939,  ..., 0.4314, 0.8353, 0.3494])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 4.367758750915527 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103844', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 11.030998468399048}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.6493, 0.5875, 0.0698,  ..., 0.4859, 0.5621, 0.3281])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 11.030998468399048 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.6493, 0.5875, 0.0698,  ..., 0.4859, 0.5621, 0.3281])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 11.030998468399048 seconds
+
+[18.89, 18.36, 18.45, 18.25, 18.55, 18.4, 23.39, 18.39, 19.05, 18.39]
+[85.7]
+13.879928350448608
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103844, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 11.030998468399048, 'TIME_S_1KI': 0.10622663291474758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.5098596334458, 'W': 85.7}
+[18.89, 18.36, 18.45, 18.25, 18.55, 18.4, 23.39, 18.39, 19.05, 18.39, 18.99, 18.45, 23.17, 18.68, 18.53, 19.54, 18.63, 18.63, 18.53, 18.57]
+344.41999999999996
+17.220999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103844, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 11.030998468399048, 'TIME_S_1KI': 0.10622663291474758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.5098596334458, 'W': 85.7, 'J_1KI': 11.45477696962218, 'W_1KI': 0.8252763761026155, 'W_D': 68.47900000000001, 'J_D': 950.4836135103704, 'W_D_1KI': 0.6594410847039791, 'J_D_1KI': 0.006350305118292623}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..5150edd
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105425, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.386318445205688, "TIME_S_1KI": 0.09851855295428681, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1161.448728044033, "W": 85.27, "J_1KI": 11.016824548674727, "W_1KI": 0.8088214370405502, "W_D": 68.29875, "J_D": 930.2861066552996, "W_D_1KI": 0.6478420678207256, "J_D_1KI": 0.006145051627419736}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..82f2e07
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_070.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.024958133697509766}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.0314, 0.9388, 0.4171,  ..., 0.1846, 0.5774, 0.6676])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.024958133697509766 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42070', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 4.190003395080566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.3135, 0.3963, 0.2557,  ..., 0.3607, 0.3756, 0.8350])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 4.190003395080566 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105425', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.386318445205688}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.9157, 0.9383, 0.4644,  ..., 0.3917, 0.2881, 0.5658])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.386318445205688 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.9157, 0.9383, 0.4644,  ..., 0.3917, 0.2881, 0.5658])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.386318445205688 seconds
+
+[18.67, 18.28, 18.48, 18.52, 20.47, 18.49, 18.74, 19.09, 18.5, 18.44]
+[85.27]
+13.62083649635315
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105425, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.386318445205688, 'TIME_S_1KI': 0.09851855295428681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.448728044033, 'W': 85.27}
+[18.67, 18.28, 18.48, 18.52, 20.47, 18.49, 18.74, 19.09, 18.5, 18.44, 21.95, 19.44, 18.99, 18.87, 18.65, 18.61, 18.49, 18.47, 18.5, 18.61]
+339.42499999999995
+16.971249999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105425, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.386318445205688, 'TIME_S_1KI': 0.09851855295428681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.448728044033, 'W': 85.27, 'J_1KI': 11.016824548674727, 'W_1KI': 0.8088214370405502, 'W_D': 68.29875, 'J_D': 930.2861066552996, 'W_D_1KI': 0.6478420678207256, 'J_D_1KI': 0.006145051627419736}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..52891fa
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105572, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.73673415184021, "TIME_S_1KI": 0.1017005849263082, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1231.2992347717284, "W": 86.3, "J_1KI": 11.663123127076577, "W_1KI": 0.8174515970143598, "W_D": 69.285, "J_D": 988.534965019226, "W_D_1KI": 0.6562819687038229, "J_D_1KI": 0.006216439668698357}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..cd9e97b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.024676084518432617}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.7484, 0.8878, 0.0538,  ..., 0.1893, 0.9984, 0.9508])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.024676084518432617 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42551', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 4.232024908065796}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9890, 0.2220, 0.6515,  ..., 0.9314, 0.4703, 0.0858])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 4.232024908065796 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105572', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.73673415184021}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.1882, 0.6590, 0.2723,  ..., 0.2031, 0.5459, 0.8199])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.73673415184021 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.1882, 0.6590, 0.2723,  ..., 0.2031, 0.5459, 0.8199])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.73673415184021 seconds
+
+[18.77, 18.76, 20.69, 18.88, 18.46, 19.3, 18.82, 18.52, 18.64, 18.63]
+[86.3]
+14.267662048339844
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105572, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.73673415184021, 'TIME_S_1KI': 0.1017005849263082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1231.2992347717284, 'W': 86.3}
+[18.77, 18.76, 20.69, 18.88, 18.46, 19.3, 18.82, 18.52, 18.64, 18.63, 19.02, 18.96, 18.67, 18.88, 18.59, 18.43, 18.73, 19.37, 19.23, 18.32]
+340.29999999999995
+17.014999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105572, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.73673415184021, 'TIME_S_1KI': 0.1017005849263082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1231.2992347717284, 'W': 86.3, 'J_1KI': 11.663123127076577, 'W_1KI': 0.8174515970143598, 'W_D': 69.285, 'J_D': 988.534965019226, 'W_D_1KI': 0.6562819687038229, 'J_D_1KI': 0.006216439668698357}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..58cdc46
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103369, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.633482694625854, "TIME_S_1KI": 0.10286916478466325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1187.6215696406364, "W": 86.11, "J_1KI": 11.489146355683392, "W_1KI": 0.833035049192698, "W_D": 68.8875, "J_D": 950.0903597563506, "W_D_1KI": 0.6664232023140401, "J_D_1KI": 0.0064470315308655405}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..fa68c24
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_080.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.024031400680541992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.4523, 0.5354, 0.9416,  ..., 0.8572, 0.6466, 0.5409])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.024031400680541992 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43692', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 4.438097715377808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6356, 0.7508, 0.7220,  ..., 0.5189, 0.1445, 0.5405])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 4.438097715377808 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103369', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.633482694625854}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6804, 0.8968, 0.8859,  ..., 0.4054, 0.8403, 0.6503])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.633482694625854 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6804, 0.8968, 0.8859,  ..., 0.4054, 0.8403, 0.6503])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.633482694625854 seconds
+
+[19.33, 21.07, 19.19, 18.61, 18.68, 18.58, 18.68, 18.58, 18.47, 20.96]
+[86.11]
+13.791912317276001
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103369, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.633482694625854, 'TIME_S_1KI': 0.10286916478466325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1187.6215696406364, 'W': 86.11}
+[19.33, 21.07, 19.19, 18.61, 18.68, 18.58, 18.68, 18.58, 18.47, 20.96, 18.98, 18.39, 18.66, 18.63, 18.76, 22.61, 18.93, 18.31, 19.42, 18.49]
+344.45
+17.2225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103369, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.633482694625854, 'TIME_S_1KI': 0.10286916478466325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1187.6215696406364, 'W': 86.11, 'J_1KI': 11.489146355683392, 'W_1KI': 0.833035049192698, 'W_D': 68.8875, 'J_D': 950.0903597563506, 'W_D_1KI': 0.6664232023140401, 'J_D_1KI': 0.0064470315308655405}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..a627580
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 106480, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.531679153442383, "TIME_S_1KI": 0.09890758032909826, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1190.395678498745, "W": 86.17, "J_1KI": 11.17952365231729, "W_1KI": 0.8092599549211119, "W_D": 69.328, "J_D": 957.7318277702332, "W_D_1KI": 0.6510894064613073, "J_D_1KI": 0.006114663847307544}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..07afbfa
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.02328658103942871}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.2863, 0.5847, 0.8482,  ..., 0.4284, 0.4105, 0.6616])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.02328658103942871 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45090', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 4.446286916732788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.5970, 0.5454, 0.1693,  ..., 0.9920, 0.1544, 0.9728])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 4.446286916732788 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '106480', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.531679153442383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7784, 0.7505, 0.3000,  ..., 0.8404, 0.5502, 0.8322])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.531679153442383 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7784, 0.7505, 0.3000,  ..., 0.8404, 0.5502, 0.8322])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.531679153442383 seconds
+
+[19.21, 19.37, 18.51, 18.62, 19.05, 18.73, 19.09, 18.38, 18.96, 19.04]
+[86.17]
+13.814502477645874
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.531679153442383, 'TIME_S_1KI': 0.09890758032909826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.395678498745, 'W': 86.17}
+[19.21, 19.37, 18.51, 18.62, 19.05, 18.73, 19.09, 18.38, 18.96, 19.04, 18.74, 18.5, 18.95, 18.29, 18.76, 18.43, 18.59, 18.5, 18.53, 18.17]
+336.8399999999999
+16.841999999999995
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.531679153442383, 'TIME_S_1KI': 0.09890758032909826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.395678498745, 'W': 86.17, 'J_1KI': 11.17952365231729, 'W_1KI': 0.8092599549211119, 'W_D': 69.328, 'J_D': 957.7318277702332, 'W_D_1KI': 0.6510894064613073, 'J_D_1KI': 0.006114663847307544}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..9e14329
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103357, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.82102108001709, "TIME_S_1KI": 0.10469558017373849, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1165.5955444908143, "W": 86.12, "J_1KI": 11.277373999736973, "W_1KI": 0.8332285186296042, "W_D": 68.928, "J_D": 932.909541229248, "W_D_1KI": 0.6668924214131602, "J_D_1KI": 0.006452319837196902}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..ff48eb2
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.026654481887817383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4473, 0.1813, 0.9148,  ..., 0.5676, 0.2645, 0.5159])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.026654481887817383 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '39392', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 4.001800060272217}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4068, 0.5875, 0.8805,  ..., 0.8282, 0.2635, 0.7162])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 4.001800060272217 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103357', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.82102108001709}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.2287, 0.6801, 0.3612,  ..., 0.8486, 0.9439, 0.0391])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.82102108001709 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.2287, 0.6801, 0.3612,  ..., 0.8486, 0.9439, 0.0391])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.82102108001709 seconds
+
+[18.77, 18.55, 18.35, 18.26, 18.44, 18.41, 19.89, 21.27, 18.68, 18.81]
+[86.12]
+13.53455114364624
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103357, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.82102108001709, 'TIME_S_1KI': 0.10469558017373849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1165.5955444908143, 'W': 86.12}
+[18.77, 18.55, 18.35, 18.26, 18.44, 18.41, 19.89, 21.27, 18.68, 18.81, 19.48, 18.52, 18.52, 22.78, 19.03, 18.67, 19.16, 18.54, 18.95, 18.58]
+343.84000000000003
+17.192
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103357, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.82102108001709, 'TIME_S_1KI': 0.10469558017373849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1165.5955444908143, 'W': 86.12, 'J_1KI': 11.277373999736973, 'W_1KI': 0.8332285186296042, 'W_D': 68.928, 'J_D': 932.909541229248, 'W_D_1KI': 0.6668924214131602, 'J_D_1KI': 0.006452319837196902}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..a3e924b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104065, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.379182815551758, "TIME_S_1KI": 0.09973749882815315, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1212.61552161932, "W": 85.99, "J_1KI": 11.652481829811366, "W_1KI": 0.8263104790275307, "W_D": 69.18924999999999, "J_D": 975.6943653820156, "W_D_1KI": 0.6648657089319174, "J_D_1KI": 0.006388946417449838}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..6d4b807
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.025043249130249023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2035, 0.4087, 0.6865,  ..., 0.9100, 0.1998, 0.7367])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.025043249130249023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41927', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 4.230342864990234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.5515, 0.9023, 0.2427,  ..., 0.4631, 0.3433, 0.5826])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 4.230342864990234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104065', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.379182815551758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6625, 0.9150, 0.9753,  ..., 0.4828, 0.8648, 0.9099])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.379182815551758 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6625, 0.9150, 0.9753,  ..., 0.4828, 0.8648, 0.9099])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.379182815551758 seconds
+
+[19.15, 18.78, 18.48, 18.51, 18.58, 18.54, 18.54, 18.39, 18.69, 18.84]
+[85.99]
+14.101820230484009
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.379182815551758, 'TIME_S_1KI': 0.09973749882815315, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.61552161932, 'W': 85.99}
+[19.15, 18.78, 18.48, 18.51, 18.58, 18.54, 18.54, 18.39, 18.69, 18.84, 20.02, 18.38, 18.49, 18.45, 18.71, 18.57, 18.85, 18.98, 18.94, 18.26]
+336.015
+16.80075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.379182815551758, 'TIME_S_1KI': 0.09973749882815315, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.61552161932, 'W': 85.99, 'J_1KI': 11.652481829811366, 'W_1KI': 0.8263104790275307, 'W_D': 69.18924999999999, 'J_D': 975.6943653820156, 'W_D_1KI': 0.6648657089319174, 'J_D_1KI': 0.006388946417449838}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..3b11a8a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 107668, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.457878112792969, "TIME_S_1KI": 0.09713079199755702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.6733793091773, "W": 86.39, "J_1KI": 11.365246677835358, "W_1KI": 0.8023739644091096, "W_D": 69.16725, "J_D": 979.7212934948801, "W_D_1KI": 0.6424123230672065, "J_D_1KI": 0.005966604033391597}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..9292d96
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.024544239044189453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.8023, 0.6490, 0.7411,  ..., 0.6759, 0.8267, 0.8015])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.024544239044189453 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42779', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 4.1718666553497314}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2253, 0.1663, 0.3556,  ..., 0.7685, 0.5605, 0.9320])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 4.1718666553497314 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '107668', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.457878112792969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.1921, 0.6623, 0.5630,  ..., 0.6521, 0.4855, 0.1447])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.457878112792969 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.1921, 0.6623, 0.5630,  ..., 0.6521, 0.4855, 0.1447])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.457878112792969 seconds
+
+[19.06, 18.55, 18.63, 19.89, 18.73, 18.92, 19.17, 18.68, 18.46, 18.62]
+[86.39]
+14.164525747299194
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107668, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.457878112792969, 'TIME_S_1KI': 0.09713079199755702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.6733793091773, 'W': 86.39}
+[19.06, 18.55, 18.63, 19.89, 18.73, 18.92, 19.17, 18.68, 18.46, 18.62, 19.39, 18.5, 18.97, 18.54, 18.81, 19.1, 23.55, 19.36, 18.78, 18.56]
+344.455
+17.222749999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107668, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.457878112792969, 'TIME_S_1KI': 0.09713079199755702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.6733793091773, 'W': 86.39, 'J_1KI': 11.365246677835358, 'W_1KI': 0.8023739644091096, 'W_D': 69.16725, 'J_D': 979.7212934948801, 'W_D_1KI': 0.6424123230672065, 'J_D_1KI': 0.005966604033391597}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..725bf61
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100814, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.194071531295776, "TIME_S_1KI": 0.10111761790322552, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.0577488923072, "W": 86.07, "J_1KI": 11.8640044923553, "W_1KI": 0.8537504711647191, "W_D": 69.22699999999999, "J_D": 962.001740241289, "W_D_1KI": 0.6866804213700477, "J_D_1KI": 0.006811359745373139}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..304c321
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.023812294006347656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.9211, 0.8071, 0.6204,  ..., 0.9847, 0.2575, 0.7368])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.023812294006347656 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44094', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 4.5924482345581055}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8718, 0.5640, 0.5184,  ..., 0.6471, 0.8654, 0.3406])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 4.5924482345581055 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100814', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.194071531295776}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.1208, 0.0816, 0.0441,  ..., 0.8026, 0.5634, 0.6977])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.194071531295776 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.1208, 0.0816, 0.0441,  ..., 0.8026, 0.5634, 0.6977])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.194071531295776 seconds
+
+[19.02, 18.57, 18.51, 18.59, 18.84, 18.86, 18.78, 18.52, 18.61, 18.92]
+[86.07]
+13.896337270736694
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100814, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.194071531295776, 'TIME_S_1KI': 0.10111761790322552, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0577488923072, 'W': 86.07}
+[19.02, 18.57, 18.51, 18.59, 18.84, 18.86, 18.78, 18.52, 18.61, 18.92, 19.41, 18.61, 18.61, 18.39, 18.76, 18.27, 18.39, 18.46, 18.45, 21.93]
+336.86
+16.843
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100814, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.194071531295776, 'TIME_S_1KI': 0.10111761790322552, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0577488923072, 'W': 86.07, 'J_1KI': 11.8640044923553, 'W_1KI': 0.8537504711647191, 'W_D': 69.22699999999999, 'J_D': 962.001740241289, 'W_D_1KI': 0.6866804213700477, 'J_D_1KI': 0.006811359745373139}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..9fca1e4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 99832, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.746523141860962, "TIME_S_1KI": 0.10764607682768013, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1178.0163116931915, "W": 86.14, "J_1KI": 11.799987095251938, "W_1KI": 0.8628495873066753, "W_D": 69.21549999999999, "J_D": 946.5635944044589, "W_D_1KI": 0.6933197772257391, "J_D_1KI": 0.006944865145702171}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..87a3dc2
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.02396559715270996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6081, 0.4592, 0.6099,  ..., 0.8678, 0.5581, 0.4023])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.02396559715270996 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43812', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 4.607961893081665}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.7996, 0.8388, 0.6441,  ..., 0.5184, 0.1157, 0.4266])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 4.607961893081665 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '99832', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.746523141860962}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6998, 0.8993, 0.4654,  ..., 0.6922, 0.9556, 0.3740])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.746523141860962 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6998, 0.8993, 0.4654,  ..., 0.6922, 0.9556, 0.3740])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.746523141860962 seconds
+
+[18.92, 19.14, 19.14, 18.67, 18.62, 18.55, 18.94, 19.65, 19.02, 18.56]
+[86.14]
+13.675601482391357
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 99832, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.746523141860962, 'TIME_S_1KI': 0.10764607682768013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1178.0163116931915, 'W': 86.14}
+[18.92, 19.14, 19.14, 18.67, 18.62, 18.55, 18.94, 19.65, 19.02, 18.56, 19.0, 18.77, 18.53, 18.49, 18.83, 18.74, 18.62, 18.6, 18.58, 18.72]
+338.49
+16.924500000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 99832, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.746523141860962, 'TIME_S_1KI': 0.10764607682768013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1178.0163116931915, 'W': 86.14, 'J_1KI': 11.799987095251938, 'W_1KI': 0.8628495873066753, 'W_D': 69.21549999999999, 'J_D': 946.5635944044589, 'W_D_1KI': 0.6933197772257391, 'J_D_1KI': 0.006944865145702171}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..efcda8e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103838, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.47437047958374, "TIME_S_1KI": 0.10087222865987153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.8774162769319, "W": 86.43, "J_1KI": 11.74789013922583, "W_1KI": 0.8323542441110191, "W_D": 69.44200000000001, "J_D": 980.1079201793672, "W_D_1KI": 0.6687532502552053, "J_D_1KI": 0.006440351800450753}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..6c56636
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.02495574951171875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7637, 0.0348, 0.3593,  ..., 0.5639, 0.9253, 0.8280])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.02495574951171875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42074', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 4.254471302032471}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.9777, 0.8326, 0.9278,  ..., 0.9597, 0.3522, 0.8734])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 4.254471302032471 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103838', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.47437047958374}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.3121, 0.4413, 0.1857,  ..., 0.7457, 0.4579, 0.8211])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.47437047958374 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.3121, 0.4413, 0.1857,  ..., 0.7457, 0.4579, 0.8211])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.47437047958374 seconds
+
+[19.18, 18.73, 18.88, 18.69, 19.03, 18.53, 18.6, 19.09, 18.57, 18.44]
+[86.43]
+14.11405086517334
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103838, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.47437047958374, 'TIME_S_1KI': 0.10087222865987153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.8774162769319, 'W': 86.43}
+[19.18, 18.73, 18.88, 18.69, 19.03, 18.53, 18.6, 19.09, 18.57, 18.44, 19.57, 18.51, 18.39, 18.4, 18.57, 18.52, 19.38, 21.21, 18.66, 18.81]
+339.76000000000005
+16.988000000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103838, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.47437047958374, 'TIME_S_1KI': 0.10087222865987153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.8774162769319, 'W': 86.43, 'J_1KI': 11.74789013922583, 'W_1KI': 0.8323542441110191, 'W_D': 69.44200000000001, 'J_D': 980.1079201793672, 'W_D_1KI': 0.6687532502552053, 'J_D_1KI': 0.006440351800450753}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..c182b6e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.657183170318604, "TIME_S_1KI": 0.10316830918323124, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1208.1050042152406, "W": 86.7, "J_1KI": 11.69522458315415, "W_1KI": 0.8393111259547528, "W_D": 69.72575, "J_D": 971.5804786350727, "W_D_1KI": 0.6749895933164891, "J_D_1KI": 0.006534328437995422}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..adc25bb
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.024408578872680664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7078, 0.4358, 0.9703,  ..., 0.2656, 0.8592, 0.0485])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.024408578872680664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43017', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 4.3724963665008545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1759, 0.2663, 0.9361,  ..., 0.8790, 0.5877, 0.6592])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 4.3724963665008545 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103299', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.657183170318604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.0464, 0.0817, 0.4775,  ..., 0.4488, 0.5737, 0.1574])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.657183170318604 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.0464, 0.0817, 0.4775,  ..., 0.4488, 0.5737, 0.1574])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.657183170318604 seconds
+
+[19.3, 18.64, 18.85, 18.35, 18.51, 18.32, 18.4, 19.27, 18.74, 20.34]
+[86.7]
+13.934313774108887
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.657183170318604, 'TIME_S_1KI': 0.10316830918323124, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.1050042152406, 'W': 86.7}
+[19.3, 18.64, 18.85, 18.35, 18.51, 18.32, 18.4, 19.27, 18.74, 20.34, 18.91, 18.54, 18.75, 18.5, 18.53, 21.15, 18.77, 18.63, 18.96, 18.6]
+339.485
+16.97425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.657183170318604, 'TIME_S_1KI': 0.10316830918323124, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.1050042152406, 'W': 86.7, 'J_1KI': 11.69522458315415, 'W_1KI': 0.8393111259547528, 'W_D': 69.72575, 'J_D': 971.5804786350727, 'W_D_1KI': 0.6749895933164891, 'J_D_1KI': 0.006534328437995422}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_005.json
similarity index 100%
rename from pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_005.json
rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_005.json
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_005.output
similarity index 100%
rename from pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_005.output
rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_005.output
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_010.json
similarity index 100%
rename from pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_010.json
rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_010.json
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_010.output
similarity index 100%
rename from pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_010.output
rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_010.output
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_015.json
similarity index 100%
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rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_015.json
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_015.output
similarity index 100%
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rename to pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_015.output
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_020.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_020.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_025.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_025.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_030.json
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_030.output
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_035.json
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_035.output
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_040.json
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_040.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_045.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_16core/altra_16_csr_20_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core_old/altra_16_csr_20_10_10_as-caida_G_045.output
similarity index 100%
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similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_065.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_065.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_065.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_065.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_065.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_070.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_070.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_070.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_070.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_070.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_070.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_075.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_075.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_075.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_075.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_075.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_075.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_080.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_080.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_080.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_080.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_080.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_080.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_085.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_085.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_085.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_085.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_085.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_085.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_090.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_090.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_090.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_090.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_090.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_090.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_095.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_095.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_095.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_095.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_095.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_095.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_100.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_100.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_100.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_100.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_100.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_100.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_105.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_105.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_105.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_105.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_105.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_105.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_110.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_110.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_110.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_110.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_110.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_110.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_115.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_115.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_115.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_115.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_115.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_115.output
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_120.json
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_120.json
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_120.json
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_120.output
similarity index 100%
rename from pytorch/output_as-caida_16core/xeon_4216_16_csr_20_10_10_as-caida_G_120.output
rename to pytorch/output_as-caida_16core_old/xeon_4216_16_csr_20_10_10_as-caida_G_120.output
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..fba9bea
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6124, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.514057636260986, "TIME_S_1KI": 1.7168611424332114, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.7508854293823, "W": 22.266094017640313, "J_1KI": 52.865918587423636, "W_1KI": 3.635874268066674, "W_D": 3.974094017640315, "J_D": 57.78366227912903, "W_D_1KI": 0.6489376253494963, "J_D_1KI": 0.1059663006775794}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..3a8f57d
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.18268108367919922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.1439, 0.3000, 0.8170,  ..., 0.9615, 0.9230, 0.5483])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.18268108367919922 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 suitesparse csr 5747 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 9.852523803710938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.0400, 0.8950, 0.6861,  ..., 0.6705, 0.3750, 0.3934])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 9.852523803710938 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 suitesparse csr 6124 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.514057636260986}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4293, 0.3058, 0.4645,  ..., 0.7892, 0.5701, 0.7940])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.514057636260986 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4293, 0.3058, 0.4645,  ..., 0.7892, 0.5701, 0.7940])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.514057636260986 seconds
+
+[20.6, 20.36, 20.44, 20.36, 20.24, 20.24, 20.12, 20.12, 20.12, 20.2]
+[20.4, 20.4, 20.6, 21.52, 23.12, 24.8, 25.76, 26.28, 25.56, 24.96, 25.04, 24.88, 24.92, 24.72]
+14.54008436203003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.514057636260986, 'TIME_S_1KI': 1.7168611424332114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.7508854293823, 'W': 22.266094017640313}
+[20.6, 20.36, 20.44, 20.36, 20.24, 20.24, 20.12, 20.12, 20.12, 20.2, 20.36, 20.32, 20.16, 20.2, 20.2, 20.2, 20.36, 20.52, 20.84, 20.92]
+365.84
+18.291999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.514057636260986, 'TIME_S_1KI': 1.7168611424332114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.7508854293823, 'W': 22.266094017640313, 'J_1KI': 52.865918587423636, 'W_1KI': 3.635874268066674, 'W_D': 3.974094017640315, 'J_D': 57.78366227912903, 'W_D_1KI': 0.6489376253494963, 'J_D_1KI': 0.1059663006775794}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..a67b0c1
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5480, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.255046367645264, "TIME_S_1KI": 1.8713588262126393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 307.9347532272339, "W": 22.64422166740688, "J_1KI": 56.192473216648516, "W_1KI": 4.132157238577898, "W_D": 4.176221667406882, "J_D": 56.7916975669861, "W_D_1KI": 0.7620842458771683, "J_D_1KI": 0.13906646822576063}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..1fb8a8a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_010.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.19157886505126953}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.6977, 0.7304, 0.0790,  ..., 0.5465, 0.4755, 0.6430])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.19157886505126953 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 suitesparse csr 5480 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.255046367645264}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.7718, 0.8860, 0.3631,  ..., 0.2089, 0.8455, 0.9819])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.255046367645264 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.7718, 0.8860, 0.3631,  ..., 0.2089, 0.8455, 0.9819])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.255046367645264 seconds
+
+[20.4, 20.4, 20.2, 20.32, 20.28, 20.16, 20.4, 20.4, 20.64, 20.92]
+[20.88, 20.84, 21.16, 23.16, 25.16, 25.92, 26.72, 26.84, 25.8, 24.84, 24.96, 24.8, 24.84]
+13.598822593688965
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.255046367645264, 'TIME_S_1KI': 1.8713588262126393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.9347532272339, 'W': 22.64422166740688}
+[20.4, 20.4, 20.2, 20.32, 20.28, 20.16, 20.4, 20.4, 20.64, 20.92, 20.84, 20.8, 20.52, 20.52, 20.76, 20.8, 20.56, 20.64, 20.56, 20.64]
+369.36
+18.468
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.255046367645264, 'TIME_S_1KI': 1.8713588262126393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.9347532272339, 'W': 22.64422166740688, 'J_1KI': 56.192473216648516, 'W_1KI': 4.132157238577898, 'W_D': 4.176221667406882, 'J_D': 56.7916975669861, 'W_D_1KI': 0.7620842458771683, 'J_D_1KI': 0.13906646822576063}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..0d7a637
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5301, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.19014310836792, "TIME_S_1KI": 1.9223058118030407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 311.1123313903809, "W": 22.88424377272302, "J_1KI": 58.68936641961534, "W_1KI": 4.31696732177382, "W_D": 4.340243772723021, "J_D": 59.00581082534796, "W_D_1KI": 0.8187594364691607, "J_D_1KI": 0.1544537703205359}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..d4b8de4
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.19806742668151855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8497, 0.2593, 0.7660,  ..., 0.4860, 0.0646, 0.1972])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.19806742668151855 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 suitesparse csr 5301 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.19014310836792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.9506, 0.6483, 0.4255,  ..., 0.7600, 0.5524, 0.6427])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.19014310836792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.9506, 0.6483, 0.4255,  ..., 0.7600, 0.5524, 0.6427])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.19014310836792 seconds
+
+[20.32, 20.32, 20.32, 20.56, 20.56, 20.64, 20.56, 20.36, 20.28, 20.12]
+[19.96, 20.12, 21.44, 23.84, 25.76, 25.76, 26.84, 27.28, 26.2, 26.24, 25.04, 25.28, 24.96]
+13.595045328140259
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5301, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.19014310836792, 'TIME_S_1KI': 1.9223058118030407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.1123313903809, 'W': 22.88424377272302}
+[20.32, 20.32, 20.32, 20.56, 20.56, 20.64, 20.56, 20.36, 20.28, 20.12, 19.96, 20.04, 20.44, 20.4, 21.0, 21.24, 21.2, 21.32, 20.96, 20.96]
+370.88
+18.544
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5301, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.19014310836792, 'TIME_S_1KI': 1.9223058118030407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.1123313903809, 'W': 22.88424377272302, 'J_1KI': 58.68936641961534, 'W_1KI': 4.31696732177382, 'W_D': 4.340243772723021, 'J_D': 59.00581082534796, 'W_D_1KI': 0.8187594364691607, 'J_D_1KI': 0.1544537703205359}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..abbb1c6
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5363, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 11.021668434143066, "TIME_S_1KI": 2.0551311643004038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.42602870941164, "W": 22.36004504966129, "J_1KI": 60.866311525156, "W_1KI": 4.169316623095523, "W_D": 4.038045049661292, "J_D": 58.94992637014394, "W_D_1KI": 0.7529451891965864, "J_D_1KI": 0.1403962687295518}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..159303c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.21234583854675293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.2224, 0.6591, 0.0761,  ..., 0.0411, 0.7513, 0.9454])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.21234583854675293 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 suitesparse csr 4944 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 9.678400754928589}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.0067, 0.7959, 0.9821,  ..., 0.5123, 0.6842, 0.8147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 9.678400754928589 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 suitesparse csr 5363 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 11.021668434143066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3594, 0.6812, 0.2708,  ..., 0.6467, 0.1195, 0.3147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 11.021668434143066 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3594, 0.6812, 0.2708,  ..., 0.6467, 0.1195, 0.3147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 11.021668434143066 seconds
+
+[20.28, 20.4, 20.36, 20.48, 20.28, 20.24, 20.4, 20.4, 20.36, 20.44]
+[20.44, 20.52, 21.8, 21.8, 22.68, 24.28, 25.4, 26.36, 26.08, 25.4, 25.04, 24.8, 24.8, 24.8]
+14.59863018989563
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5363, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 11.021668434143066, 'TIME_S_1KI': 2.0551311643004038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.42602870941164, 'W': 22.36004504966129}
+[20.28, 20.4, 20.36, 20.48, 20.28, 20.24, 20.4, 20.4, 20.36, 20.44, 20.56, 20.64, 20.52, 20.36, 20.12, 20.12, 20.2, 20.2, 20.44, 20.56]
+366.44
+18.322
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5363, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 11.021668434143066, 'TIME_S_1KI': 2.0551311643004038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.42602870941164, 'W': 22.36004504966129, 'J_1KI': 60.866311525156, 'W_1KI': 4.169316623095523, 'W_D': 4.038045049661292, 'J_D': 58.94992637014394, 'W_D_1KI': 0.7529451891965864, 'J_D_1KI': 0.1403962687295518}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..893b380
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4892, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.115711212158203, "TIME_S_1KI": 2.067806870841824, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 307.40472288131707, "W": 22.646829011678925, "J_1KI": 62.838250793400874, "W_1KI": 4.629359977857507, "W_D": 4.094829011678925, "J_D": 55.58260615348808, "W_D_1KI": 0.8370459958460599, "J_D_1KI": 0.1711050686520973}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..b73490e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.2146143913269043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.5233, 0.6684, 0.0720,  ..., 0.8096, 0.6371, 0.4657])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.2146143913269043 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 suitesparse csr 4892 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.115711212158203}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2946, 0.5991, 0.8521,  ..., 0.8859, 0.0862, 0.1832])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.115711212158203 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2946, 0.5991, 0.8521,  ..., 0.8859, 0.0862, 0.1832])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.115711212158203 seconds
+
+[20.32, 20.6, 20.76, 20.72, 20.72, 20.72, 20.8, 20.6, 20.56, 20.76]
+[20.72, 20.68, 21.0, 22.84, 24.56, 25.56, 26.44, 26.76, 25.56, 25.2, 25.52, 25.52, 25.52]
+13.573852777481079
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4892, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.115711212158203, 'TIME_S_1KI': 2.067806870841824, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.40472288131707, 'W': 22.646829011678925}
+[20.32, 20.6, 20.76, 20.72, 20.72, 20.72, 20.8, 20.6, 20.56, 20.76, 20.0, 20.0, 20.16, 20.32, 20.28, 20.64, 21.0, 21.04, 21.0, 21.16]
+371.03999999999996
+18.552
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4892, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.115711212158203, 'TIME_S_1KI': 2.067806870841824, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.40472288131707, 'W': 22.646829011678925, 'J_1KI': 62.838250793400874, 'W_1KI': 4.629359977857507, 'W_D': 4.094829011678925, 'J_D': 55.58260615348808, 'W_D_1KI': 0.8370459958460599, 'J_D_1KI': 0.1711050686520973}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..c81d262
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4785, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.012799263000488, "TIME_S_1KI": 2.0925390309300913, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.7179060649872, "W": 23.477211164294282, "J_1KI": 71.62338684743725, "W_1KI": 4.906418216153455, "W_D": 4.948211164294278, "J_D": 72.23347599196431, "W_D_1KI": 1.0341089162579475, "J_D_1KI": 0.21611471604136834}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..12d5447
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_030.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.2193920612335205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.6281, 0.7192, 0.2549,  ..., 0.4059, 0.6023, 0.4168])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.2193920612335205 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 suitesparse csr 4785 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.012799263000488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.4601, 0.6880, 0.2010,  ..., 0.6827, 0.3431, 0.1510])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.012799263000488 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.4601, 0.6880, 0.2010,  ..., 0.6827, 0.3431, 0.1510])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.012799263000488 seconds
+
+[20.76, 20.36, 20.48, 20.48, 20.6, 20.76, 20.84, 21.16, 21.2, 21.08]
+[20.72, 20.52, 23.16, 24.04, 25.6, 26.72, 28.16, 26.2, 26.2, 26.32, 25.88, 25.56, 25.76, 25.72]
+14.597896814346313
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4785, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.012799263000488, 'TIME_S_1KI': 2.0925390309300913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.7179060649872, 'W': 23.477211164294282}
+[20.76, 20.36, 20.48, 20.48, 20.6, 20.76, 20.84, 21.16, 21.2, 21.08, 20.24, 20.28, 20.28, 20.48, 20.32, 20.44, 20.48, 20.56, 20.6, 20.44]
+370.58000000000004
+18.529000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4785, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.012799263000488, 'TIME_S_1KI': 2.0925390309300913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.7179060649872, 'W': 23.477211164294282, 'J_1KI': 71.62338684743725, 'W_1KI': 4.906418216153455, 'W_D': 4.948211164294278, 'J_D': 72.23347599196431, 'W_D_1KI': 1.0341089162579475, 'J_D_1KI': 0.21611471604136834}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..1ccb8e4
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4974, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.731950759887695, "TIME_S_1KI": 2.1576097225347195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.8746109008789, "W": 23.52079658107547, "J_1KI": 68.93337573399253, "W_1KI": 4.728748810027236, "W_D": 4.868796581075472, "J_D": 70.97492329978948, "W_D_1KI": 0.9788493327453702, "J_D_1KI": 0.19679319114301774}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..5ba179d
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.22348880767822266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7766, 0.4782, 0.2449,  ..., 0.0046, 0.9056, 0.4984])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.22348880767822266 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 suitesparse csr 4698 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 9.915407419204712}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.3469, 0.1883, 0.3355,  ..., 0.8764, 0.8263, 0.9306])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 9.915407419204712 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 suitesparse csr 4974 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.731950759887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.3179, 0.6318, 0.5104,  ..., 0.0206, 0.4518, 0.3308])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.731950759887695 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.3179, 0.6318, 0.5104,  ..., 0.0206, 0.4518, 0.3308])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.731950759887695 seconds
+
+[20.48, 20.6, 20.6, 20.36, 20.4, 20.56, 20.6, 20.88, 21.16, 20.88]
+[20.76, 20.76, 20.76, 23.6, 25.72, 27.76, 28.8, 29.72, 26.76, 25.88, 25.56, 25.32, 24.96, 24.96]
+14.577508449554443
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4974, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.731950759887695, 'TIME_S_1KI': 2.1576097225347195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.8746109008789, 'W': 23.52079658107547}
+[20.48, 20.6, 20.6, 20.36, 20.4, 20.56, 20.6, 20.88, 21.16, 20.88, 20.6, 20.68, 20.72, 20.76, 20.96, 20.96, 21.04, 20.88, 20.64, 20.52]
+373.03999999999996
+18.651999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4974, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.731950759887695, 'TIME_S_1KI': 2.1576097225347195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.8746109008789, 'W': 23.52079658107547, 'J_1KI': 68.93337573399253, 'W_1KI': 4.728748810027236, 'W_D': 4.868796581075472, 'J_D': 70.97492329978948, 'W_D_1KI': 0.9788493327453702, 'J_D_1KI': 0.19679319114301774}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..9b35246
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4664, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.289891719818115, "TIME_S_1KI": 2.206237504249167, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 338.59072193145755, "W": 23.221823086359233, "J_1KI": 72.59663849302262, "W_1KI": 4.978950061397778, "W_D": 4.787823086359236, "J_D": 69.80987105369573, "W_D_1KI": 1.0265486891850848, "J_D_1KI": 0.22010049082012967}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..620bda2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_040.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.2251129150390625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.5202, 0.9033, 0.7201,  ..., 0.5869, 0.3755, 0.9767])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.2251129150390625 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 suitesparse csr 4664 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.289891719818115}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.8537, 0.9441, 0.4082,  ..., 0.4625, 0.9345, 0.7663])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.289891719818115 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.8537, 0.9441, 0.4082,  ..., 0.4625, 0.9345, 0.7663])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.289891719818115 seconds
+
+[20.52, 20.32, 20.48, 20.44, 20.72, 20.84, 20.84, 20.64, 20.68, 20.52]
+[20.72, 20.96, 21.52, 23.76, 25.6, 26.64, 27.64, 26.64, 26.68, 25.72, 25.28, 25.44, 25.16, 25.16]
+14.58071231842041
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4664, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.289891719818115, 'TIME_S_1KI': 2.206237504249167, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.59072193145755, 'W': 23.221823086359233}
+[20.52, 20.32, 20.48, 20.44, 20.72, 20.84, 20.84, 20.64, 20.68, 20.52, 20.12, 20.12, 20.04, 20.36, 20.4, 20.24, 20.4, 20.76, 20.52, 20.6]
+368.67999999999995
+18.433999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4664, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.289891719818115, 'TIME_S_1KI': 2.206237504249167, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.59072193145755, 'W': 23.221823086359233, 'J_1KI': 72.59663849302262, 'W_1KI': 4.978950061397778, 'W_D': 4.787823086359236, 'J_D': 69.80987105369573, 'W_D_1KI': 1.0265486891850848, 'J_D_1KI': 0.22010049082012967}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..66cd4e3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4843, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.373887062072754, "TIME_S_1KI": 2.1420373863458093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.9727068328857, "W": 22.843118658084368, "J_1KI": 68.75339806584466, "W_1KI": 4.716729022937098, "W_D": 4.402118658084369, "J_D": 64.16748025178906, "W_D_1KI": 0.9089652401578296, "J_D_1KI": 0.18768640102371045}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..f96d685
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.23405838012695312}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.2593, 0.7313, 0.0475,  ..., 0.6609, 0.0319, 0.0461])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.23405838012695312 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 suitesparse csr 4486 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.72571873664856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.8910, 0.8980, 0.3385,  ..., 0.3949, 0.7661, 0.7330])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 9.72571873664856 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 suitesparse csr 4843 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.373887062072754}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.4834, 0.2409, 0.6592,  ..., 0.5071, 0.3977, 0.4858])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.373887062072754 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.4834, 0.2409, 0.6592,  ..., 0.5071, 0.3977, 0.4858])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.373887062072754 seconds
+
+[20.88, 21.0, 21.08, 21.08, 20.96, 20.8, 20.68, 20.28, 20.6, 20.8]
+[20.6, 20.88, 21.36, 22.44, 24.12, 25.6, 26.4, 26.48, 26.48, 26.88, 25.56, 25.12, 25.0, 24.48]
+14.576499462127686
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4843, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.373887062072754, 'TIME_S_1KI': 2.1420373863458093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.9727068328857, 'W': 22.843118658084368}
+[20.88, 21.0, 21.08, 21.08, 20.96, 20.8, 20.68, 20.28, 20.6, 20.8, 19.96, 19.92, 20.2, 20.2, 20.36, 20.36, 20.16, 20.0, 20.24, 20.16]
+368.82
+18.441
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4843, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.373887062072754, 'TIME_S_1KI': 2.1420373863458093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.9727068328857, 'W': 22.843118658084368, 'J_1KI': 68.75339806584466, 'W_1KI': 4.716729022937098, 'W_D': 4.402118658084369, 'J_D': 64.16748025178906, 'W_D_1KI': 0.9089652401578296, 'J_D_1KI': 0.18768640102371045}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..bc73d7c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4371, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.007423162460327, "TIME_S_1KI": 2.28950426960886, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 306.5846672821045, "W": 22.642894158978486, "J_1KI": 70.14062394923461, "W_1KI": 5.180254897958931, "W_D": 4.411894158978484, "J_D": 59.73702360296246, "W_D_1KI": 1.0093557902032677, "J_D_1KI": 0.23092102269578305}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..45b0c7b
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_050.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.2402174472808838}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.7925, 0.9015, 0.9203,  ..., 0.3146, 0.5683, 0.2443])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.2402174472808838 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 suitesparse csr 4371 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.007423162460327}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8034, 0.0913, 0.4082,  ..., 0.5649, 0.1218, 0.1550])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.007423162460327 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8034, 0.0913, 0.4082,  ..., 0.5649, 0.1218, 0.1550])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.007423162460327 seconds
+
+[20.52, 20.32, 20.44, 20.44, 20.28, 20.44, 20.32, 20.32, 20.28, 20.24]
+[20.36, 20.4, 20.88, 22.92, 25.04, 25.88, 26.56, 26.8, 25.84, 25.84, 24.76, 25.36, 25.12]
+13.539994716644287
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4371, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.007423162460327, 'TIME_S_1KI': 2.28950426960886, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 306.5846672821045, 'W': 22.642894158978486}
+[20.52, 20.32, 20.44, 20.44, 20.28, 20.44, 20.32, 20.32, 20.28, 20.24, 19.88, 19.92, 20.08, 19.96, 20.08, 20.24, 20.2, 20.28, 20.44, 20.52]
+364.62
+18.231
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4371, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.007423162460327, 'TIME_S_1KI': 2.28950426960886, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 306.5846672821045, 'W': 22.642894158978486, 'J_1KI': 70.14062394923461, 'W_1KI': 5.180254897958931, 'W_D': 4.411894158978484, 'J_D': 59.73702360296246, 'W_D_1KI': 1.0093557902032677, 'J_D_1KI': 0.23092102269578305}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..77336b8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.816685914993286, "TIME_S_1KI": 2.35298801718366, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.7557634544372, "W": 22.27027274930315, "J_1KI": 70.64515193701048, "W_1KI": 4.84452311274813, "W_D": 3.9532727493031494, "J_D": 57.64851307821269, "W_D_1KI": 0.8599679680885685, "J_D_1KI": 0.18707156147238815}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..8734f9a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.22839808464050293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.2187, 0.5179, 0.0914,  ..., 0.1693, 0.7207, 0.1990])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.22839808464050293 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 suitesparse csr 4597 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.816685914993286}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.0686, 0.3478, 0.1639,  ..., 0.3403, 0.2083, 0.0411])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.816685914993286 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.0686, 0.3478, 0.1639,  ..., 0.3403, 0.2083, 0.0411])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.816685914993286 seconds
+
+[20.2, 20.36, 20.48, 20.52, 20.32, 20.32, 20.28, 19.92, 20.0, 20.08]
+[20.12, 20.12, 20.6, 21.84, 23.64, 25.04, 25.96, 26.0, 25.96, 25.04, 24.68, 24.68, 24.68, 24.44]
+14.582478046417236
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.816685914993286, 'TIME_S_1KI': 2.35298801718366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.7557634544372, 'W': 22.27027274930315}
+[20.2, 20.36, 20.48, 20.52, 20.32, 20.32, 20.28, 19.92, 20.0, 20.08, 20.56, 20.32, 20.36, 20.4, 20.28, 20.44, 20.6, 20.48, 20.52, 20.64]
+366.34000000000003
+18.317
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.816685914993286, 'TIME_S_1KI': 2.35298801718366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.7557634544372, 'W': 22.27027274930315, 'J_1KI': 70.64515193701048, 'W_1KI': 4.84452311274813, 'W_D': 3.9532727493031494, 'J_D': 57.64851307821269, 'W_D_1KI': 0.8599679680885685, 'J_D_1KI': 0.18707156147238815}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..19e4a37
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4444, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.11352276802063, "TIME_S_1KI": 2.275770199824624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 309.03930358886726, "W": 22.807641379756916, "J_1KI": 69.54079738723385, "W_1KI": 5.132232533698676, "W_D": 4.1336413797569165, "J_D": 56.01007276535041, "W_D_1KI": 0.9301623266779739, "J_D_1KI": 0.20930745424796893}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..5250c5a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_060.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.23622441291809082}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.8866, 0.2692, 0.0159,  ..., 0.7771, 0.5658, 0.3712])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.23622441291809082 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 suitesparse csr 4444 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.11352276802063}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.4509, 0.3422, 0.7764,  ..., 0.6501, 0.3337, 0.5928])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.11352276802063 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.4509, 0.3422, 0.7764,  ..., 0.6501, 0.3337, 0.5928])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.11352276802063 seconds
+
+[20.4, 20.52, 20.48, 20.72, 20.72, 20.96, 21.08, 20.88, 20.88, 20.72]
+[20.84, 20.48, 20.6, 24.04, 25.68, 26.64, 27.84, 25.76, 25.84, 25.08, 25.04, 25.08, 25.28]
+13.549814224243164
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.11352276802063, 'TIME_S_1KI': 2.275770199824624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 309.03930358886726, 'W': 22.807641379756916}
+[20.4, 20.52, 20.48, 20.72, 20.72, 20.96, 21.08, 20.88, 20.88, 20.72, 20.88, 20.76, 20.8, 20.8, 21.12, 20.92, 20.64, 20.64, 20.4, 20.32]
+373.48
+18.674
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.11352276802063, 'TIME_S_1KI': 2.275770199824624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 309.03930358886726, 'W': 22.807641379756916, 'J_1KI': 69.54079738723385, 'W_1KI': 5.132232533698676, 'W_D': 4.1336413797569165, 'J_D': 56.01007276535041, 'W_D_1KI': 0.9301623266779739, 'J_D_1KI': 0.20930745424796893}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..6cfe633
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4457, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40576696395874, "TIME_S_1KI": 2.3347020336456676, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 304.0924919033051, "W": 22.47261995332125, "J_1KI": 68.22806639068995, "W_1KI": 5.042095569513406, "W_D": 3.977619953321252, "J_D": 53.82391398787505, "W_D_1KI": 0.8924433370700587, "J_D_1KI": 0.20023408953781888}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..2383595
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.23553967475891113}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1485, 0.8406, 0.3211,  ..., 0.8758, 0.2934, 0.6273])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.23553967475891113 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 suitesparse csr 4457 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40576696395874}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.0947, 0.4563, 0.0701,  ..., 0.1127, 0.0889, 0.4578])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.40576696395874 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.0947, 0.4563, 0.0701,  ..., 0.1127, 0.0889, 0.4578])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.40576696395874 seconds
+
+[20.16, 20.24, 20.52, 20.72, 20.92, 20.84, 20.84, 21.04, 20.6, 20.44]
+[20.16, 20.12, 20.84, 22.08, 24.0, 25.48, 26.48, 26.04, 26.28, 25.32, 25.36, 25.56, 25.64]
+13.531688451766968
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4457, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40576696395874, 'TIME_S_1KI': 2.3347020336456676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 304.0924919033051, 'W': 22.47261995332125}
+[20.16, 20.24, 20.52, 20.72, 20.92, 20.84, 20.84, 21.04, 20.6, 20.44, 20.24, 20.24, 20.6, 20.56, 20.52, 20.56, 20.48, 20.28, 20.32, 20.4]
+369.9
+18.494999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4457, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40576696395874, 'TIME_S_1KI': 2.3347020336456676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 304.0924919033051, 'W': 22.47261995332125, 'J_1KI': 68.22806639068995, 'W_1KI': 5.042095569513406, 'W_D': 3.977619953321252, 'J_D': 53.82391398787505, 'W_D_1KI': 0.8924433370700587, 'J_D_1KI': 0.20023408953781888}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..a964706
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5402, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.492238998413086, "TIME_S_1KI": 1.9422878560557362, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.3698135662079, "W": 23.03779267605632, "J_1KI": 62.08252750207476, "W_1KI": 4.264678392457667, "W_D": 4.4307926760563205, "J_D": 64.50071561169625, "W_D_1KI": 0.8202133794995039, "J_D_1KI": 0.15183513134015253}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..dd78c49
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.20566105842590332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.6943, 0.0260, 0.7137,  ..., 0.0161, 0.4052, 0.0132])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.20566105842590332 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 suitesparse csr 5105 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 9.920999765396118}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.0673, 0.9022, 0.4268,  ..., 0.6697, 0.6841, 0.7176])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 9.920999765396118 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 suitesparse csr 5402 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.492238998413086}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8873, 0.1563, 0.4708,  ..., 0.0366, 0.7316, 0.3985])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.492238998413086 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8873, 0.1563, 0.4708,  ..., 0.0366, 0.7316, 0.3985])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.492238998413086 seconds
+
+[20.68, 20.52, 20.36, 20.4, 20.24, 20.32, 20.32, 20.4, 20.36, 20.4]
+[20.44, 20.68, 21.16, 24.48, 26.16, 26.68, 27.92, 25.48, 25.44, 25.04, 25.2, 25.28, 25.28, 24.92]
+14.557376146316528
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.492238998413086, 'TIME_S_1KI': 1.9422878560557362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3698135662079, 'W': 23.03779267605632}
+[20.68, 20.52, 20.36, 20.4, 20.24, 20.32, 20.32, 20.4, 20.36, 20.4, 20.2, 20.4, 20.44, 20.72, 20.96, 20.96, 21.56, 21.64, 21.28, 21.24]
+372.14
+18.607
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.492238998413086, 'TIME_S_1KI': 1.9422878560557362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3698135662079, 'W': 23.03779267605632, 'J_1KI': 62.08252750207476, 'W_1KI': 4.264678392457667, 'W_D': 4.4307926760563205, 'J_D': 64.50071561169625, 'W_D_1KI': 0.8202133794995039, 'J_D_1KI': 0.15183513134015253}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..2e451d6
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4241, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.165872573852539, "TIME_S_1KI": 2.397046115032431, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 308.4233190155029, "W": 22.773842448783192, "J_1KI": 72.724196891182, "W_1KI": 5.369922765570194, "W_D": 4.36484244878319, "J_D": 59.1125190253257, "W_D_1KI": 1.0292012376286699, "J_D_1KI": 0.2426789053592714}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..15086a5
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.24754810333251953}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2352, 0.9461, 0.6829,  ..., 0.6578, 0.9838, 0.5986])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.24754810333251953 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 suitesparse csr 4241 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.165872573852539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.1680, 0.3295, 0.5975,  ..., 0.6100, 0.6760, 0.0664])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.165872573852539 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.1680, 0.3295, 0.5975,  ..., 0.6100, 0.6760, 0.0664])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.165872573852539 seconds
+
+[20.8, 20.76, 20.64, 20.64, 20.48, 20.68, 20.6, 20.56, 20.68, 20.52]
+[20.32, 20.32, 20.36, 23.6, 24.68, 27.04, 27.88, 28.48, 25.8, 24.88, 24.64, 24.72, 24.8]
+13.54287576675415
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4241, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.165872573852539, 'TIME_S_1KI': 2.397046115032431, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.4233190155029, 'W': 22.773842448783192}
+[20.8, 20.76, 20.64, 20.64, 20.48, 20.68, 20.6, 20.56, 20.68, 20.52, 20.24, 20.32, 20.2, 20.2, 20.2, 20.16, 20.44, 20.44, 20.24, 20.32]
+368.18000000000006
+18.409000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4241, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.165872573852539, 'TIME_S_1KI': 2.397046115032431, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.4233190155029, 'W': 22.773842448783192, 'J_1KI': 72.724196891182, 'W_1KI': 5.369922765570194, 'W_D': 4.36484244878319, 'J_D': 59.1125190253257, 'W_D_1KI': 1.0292012376286699, 'J_D_1KI': 0.2426789053592714}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..14fc109
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4228, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.372806549072266, "TIME_S_1KI": 2.4533601109442444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.92140563964847, "W": 22.280655463214174, "J_1KI": 76.84990672650153, "W_1KI": 5.269786060362861, "W_D": 3.414655463214178, "J_D": 49.79632016277324, "W_D_1KI": 0.8076290121130979, "J_D_1KI": 0.19101916085929466}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..ac13c2a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_080.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.24834132194519043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.6433, 0.2236, 0.6943,  ..., 0.4168, 0.4788, 0.3616])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.24834132194519043 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 suitesparse csr 4228 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.372806549072266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1556, 0.9083, 0.2247,  ..., 0.3738, 0.3114, 0.9064])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.372806549072266 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1556, 0.9083, 0.2247,  ..., 0.3738, 0.3114, 0.9064])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.372806549072266 seconds
+
+[20.56, 20.4, 20.48, 20.32, 20.24, 20.52, 20.48, 20.36, 20.4, 20.4]
+[20.28, 20.32, 20.6, 21.56, 23.56, 24.96, 26.16, 26.48, 25.68, 25.04, 24.8, 24.8, 24.48, 24.32]
+14.583117008209229
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4228, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.372806549072266, 'TIME_S_1KI': 2.4533601109442444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.92140563964847, 'W': 22.280655463214174}
+[20.56, 20.4, 20.48, 20.32, 20.24, 20.52, 20.48, 20.36, 20.4, 20.4, 19.84, 20.32, 21.32, 21.32, 22.24, 22.24, 22.6, 22.28, 20.92, 20.96]
+377.31999999999994
+18.865999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4228, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.372806549072266, 'TIME_S_1KI': 2.4533601109442444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.92140563964847, 'W': 22.280655463214174, 'J_1KI': 76.84990672650153, 'W_1KI': 5.269786060362861, 'W_D': 3.414655463214178, 'J_D': 49.79632016277324, 'W_D_1KI': 0.8076290121130979, 'J_D_1KI': 0.19101916085929466}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..80844ab
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4258, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.52000379562378, "TIME_S_1KI": 2.470644386008403, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 340.48363485336307, "W": 23.399428779350817, "J_1KI": 79.96327732582505, "W_1KI": 5.495403658842371, "W_D": 4.919428779350817, "J_D": 71.58230262756351, "W_D_1KI": 1.1553379002702717, "J_D_1KI": 0.2713334664796317}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..e5dc514
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.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 suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.246551513671875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.0320, 0.2964, 0.1884,  ..., 0.9782, 0.1676, 0.8891])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.246551513671875 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 suitesparse csr 4258 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.52000379562378}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.0888, 0.3161, 0.3875,  ..., 0.4649, 0.9695, 0.2632])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.52000379562378 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.0888, 0.3161, 0.3875,  ..., 0.4649, 0.9695, 0.2632])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.52000379562378 seconds
+
+[20.24, 20.4, 20.28, 20.32, 20.52, 20.48, 20.6, 20.72, 20.68, 20.76]
+[20.8, 20.68, 23.92, 24.68, 24.68, 26.56, 27.48, 28.76, 26.44, 25.24, 25.28, 24.8, 25.08, 25.2]
+14.55093789100647
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4258, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.52000379562378, 'TIME_S_1KI': 2.470644386008403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.48363485336307, 'W': 23.399428779350817}
+[20.24, 20.4, 20.28, 20.32, 20.52, 20.48, 20.6, 20.72, 20.68, 20.76, 20.28, 20.36, 20.32, 20.44, 20.64, 20.64, 20.76, 20.76, 20.72, 20.64]
+369.6
+18.48
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4258, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.52000379562378, 'TIME_S_1KI': 2.470644386008403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.48363485336307, 'W': 23.399428779350817, 'J_1KI': 79.96327732582505, 'W_1KI': 5.495403658842371, 'W_D': 4.919428779350817, 'J_D': 71.58230262756351, 'W_D_1KI': 1.1553379002702717, 'J_D_1KI': 0.2713334664796317}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..968ec6a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4201, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.13870620727539, "TIME_S_1KI": 2.4134030486254203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.1249903202057, "W": 24.252696538375847, "J_1KI": 78.10640093316013, "W_1KI": 5.773077014609818, "W_D": 5.884696538375845, "J_D": 79.61654868507382, "W_D_1KI": 1.4007847032553786, "J_D_1KI": 0.33344077678061856}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..5e16430
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.24991941452026367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.0352, 0.3545, 0.3565,  ..., 0.8541, 0.4523, 0.5980])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.24991941452026367 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 suitesparse csr 4201 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.13870620727539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8206, 0.1412, 0.3467,  ..., 0.7428, 0.4742, 0.9860])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.13870620727539 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8206, 0.1412, 0.3467,  ..., 0.7428, 0.4742, 0.9860])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.13870620727539 seconds
+
+[20.2, 20.2, 20.2, 20.24, 20.16, 20.52, 20.76, 20.56, 20.68, 20.6]
+[20.6, 20.6, 24.04, 26.2, 28.2, 29.44, 29.44, 30.4, 26.52, 26.44, 25.16, 25.32, 25.32]
+13.529422998428345
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4201, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.13870620727539, 'TIME_S_1KI': 2.4134030486254203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.1249903202057, 'W': 24.252696538375847}
+[20.2, 20.2, 20.2, 20.24, 20.16, 20.52, 20.76, 20.56, 20.68, 20.6, 20.24, 20.32, 20.44, 20.48, 20.28, 20.52, 20.4, 20.36, 20.48, 20.48]
+367.36
+18.368000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4201, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.13870620727539, 'TIME_S_1KI': 2.4134030486254203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.1249903202057, 'W': 24.252696538375847, 'J_1KI': 78.10640093316013, 'W_1KI': 5.773077014609818, 'W_D': 5.884696538375845, 'J_D': 79.61654868507382, 'W_D_1KI': 1.4007847032553786, 'J_D_1KI': 0.33344077678061856}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..73a712c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.539864301681519, "TIME_S_1KI": 2.451701396064554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 330.04471103668214, "W": 22.651642406245635, "J_1KI": 76.77243801737198, "W_1KI": 5.269049175679375, "W_D": 4.1926424062456356, "J_D": 61.08870282483105, "W_D_1KI": 0.9752599223646513, "J_D_1KI": 0.2268573906407656}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..0dcb914
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.2615513801574707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.3439, 0.2522, 0.8155,  ..., 0.9546, 0.8272, 0.9957])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.2615513801574707 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 suitesparse csr 4014 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.802011013031006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.1432, 0.6569, 0.7193,  ..., 0.9653, 0.9751, 0.3574])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 9.802011013031006 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 suitesparse csr 4299 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.539864301681519}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.7221, 0.0635, 0.9129,  ..., 0.8366, 0.9671, 0.5596])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.539864301681519 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.7221, 0.0635, 0.9129,  ..., 0.8366, 0.9671, 0.5596])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.539864301681519 seconds
+
+[20.28, 20.32, 20.44, 20.64, 20.88, 20.92, 20.76, 20.6, 20.36, 20.2]
+[20.08, 20.04, 21.68, 23.8, 23.8, 26.2, 26.88, 27.4, 25.72, 24.76, 24.44, 24.48, 24.6, 24.48]
+14.570453882217407
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.539864301681519, 'TIME_S_1KI': 2.451701396064554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.04471103668214, 'W': 22.651642406245635}
+[20.28, 20.32, 20.44, 20.64, 20.88, 20.92, 20.76, 20.6, 20.36, 20.2, 20.56, 20.4, 20.28, 20.48, 20.4, 20.4, 20.4, 20.4, 20.64, 20.68]
+369.18
+18.459
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.539864301681519, 'TIME_S_1KI': 2.451701396064554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.04471103668214, 'W': 22.651642406245635, 'J_1KI': 76.77243801737198, 'W_1KI': 5.269049175679375, 'W_D': 4.1926424062456356, 'J_D': 61.08870282483105, 'W_D_1KI': 0.9752599223646513, 'J_D_1KI': 0.2268573906407656}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..aef883a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4098, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.15280270576477, "TIME_S_1KI": 2.4775018803720767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.1695436763763, "W": 22.302738835560042, "J_1KI": 79.34835131195126, "W_1KI": 5.442347202430464, "W_D": 4.092738835560041, "J_D": 59.67132688760752, "W_D_1KI": 0.9987161628989852, "J_D_1KI": 0.2437081900680784}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..fdac70d
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.25622129440307617}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2213, 0.7003, 0.4118,  ..., 0.8978, 0.6909, 0.0795])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.25622129440307617 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 suitesparse csr 4098 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.15280270576477}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2094, 0.0576, 0.1136,  ..., 0.6630, 0.3113, 0.6739])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.15280270576477 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2094, 0.0576, 0.1136,  ..., 0.6630, 0.3113, 0.6739])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.15280270576477 seconds
+
+[20.0, 20.0, 20.12, 20.2, 20.16, 20.16, 20.2, 20.32, 20.28, 20.36]
+[20.32, 20.32, 20.88, 22.0, 23.72, 23.72, 24.44, 25.6, 25.84, 25.24, 25.8, 25.6, 25.2, 24.68]
+14.579803228378296
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.15280270576477, 'TIME_S_1KI': 2.4775018803720767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.1695436763763, 'W': 22.302738835560042}
+[20.0, 20.0, 20.12, 20.2, 20.16, 20.16, 20.2, 20.32, 20.28, 20.36, 20.12, 20.2, 20.28, 20.52, 20.52, 20.36, 20.28, 20.12, 20.12, 20.24]
+364.20000000000005
+18.21
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.15280270576477, 'TIME_S_1KI': 2.4775018803720767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.1695436763763, 'W': 22.302738835560042, 'J_1KI': 79.34835131195126, 'W_1KI': 5.442347202430464, 'W_D': 4.092738835560041, 'J_D': 59.67132688760752, 'W_D_1KI': 0.9987161628989852, 'J_D_1KI': 0.2437081900680784}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..2b479f7
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4049, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.376229763031006, "TIME_S_1KI": 2.562664796994568, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.42938493728633, "W": 22.891819526408486, "J_1KI": 76.66816125890006, "W_1KI": 5.653697092222397, "W_D": 4.550819526408485, "J_D": 61.7123555824756, "W_D_1KI": 1.1239366575471685, "J_D_1KI": 0.2775837632865321}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..5514581
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.2592613697052002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.4913, 0.3479, 0.5546,  ..., 0.3694, 0.0850, 0.9788])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.2592613697052002 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 suitesparse csr 4049 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.376229763031006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.3564, 0.9920, 0.3563,  ..., 0.1738, 0.9526, 0.3099])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.376229763031006 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.3564, 0.9920, 0.3563,  ..., 0.1738, 0.9526, 0.3099])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.376229763031006 seconds
+
+[20.6, 20.4, 20.48, 20.56, 20.64, 20.36, 20.32, 20.32, 20.32, 20.24]
+[20.32, 20.52, 20.76, 22.96, 23.72, 26.2, 27.36, 27.96, 27.24, 25.8, 25.52, 25.16, 25.52]
+13.560712575912476
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4049, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.376229763031006, 'TIME_S_1KI': 2.562664796994568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.42938493728633, 'W': 22.891819526408486}
+[20.6, 20.4, 20.48, 20.56, 20.64, 20.36, 20.32, 20.32, 20.32, 20.24, 20.56, 20.32, 20.24, 20.24, 20.12, 20.28, 20.44, 20.44, 20.48, 20.32]
+366.82
+18.341
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4049, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.376229763031006, 'TIME_S_1KI': 2.562664796994568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.42938493728633, 'W': 22.891819526408486, 'J_1KI': 76.66816125890006, 'W_1KI': 5.653697092222397, 'W_D': 4.550819526408485, 'J_D': 61.7123555824756, 'W_D_1KI': 1.1239366575471685, 'J_D_1KI': 0.2775837632865321}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..79e9b75
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4058, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.23972225189209, "TIME_S_1KI": 2.5233421024869616, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.52500621795656, "W": 22.316341787391842, "J_1KI": 80.218089260216, "W_1KI": 5.499344945143382, "W_D": 3.949341787391841, "J_D": 57.60843430995939, "W_D_1KI": 0.9732237031522526, "J_D_1KI": 0.2398284137881352}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..1c6fd42
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.2586948871612549}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.9783, 0.1337, 0.6073,  ..., 0.9455, 0.0477, 0.7593])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.2586948871612549 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 suitesparse csr 4058 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.23972225189209}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.4840, 0.5686, 0.9147,  ..., 0.1218, 0.6764, 0.3185])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.23972225189209 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.4840, 0.5686, 0.9147,  ..., 0.1218, 0.6764, 0.3185])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.23972225189209 seconds
+
+[20.52, 20.48, 20.48, 20.6, 20.56, 20.36, 20.64, 20.48, 20.44, 20.56]
+[20.6, 20.6, 20.36, 21.56, 22.76, 24.88, 25.76, 26.32, 25.88, 25.12, 24.88, 24.8, 24.96, 25.16]
+14.586844444274902
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4058, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.23972225189209, 'TIME_S_1KI': 2.5233421024869616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.52500621795656, 'W': 22.316341787391842}
+[20.52, 20.48, 20.48, 20.6, 20.56, 20.36, 20.64, 20.48, 20.44, 20.56, 20.4, 20.4, 20.28, 20.24, 20.16, 20.16, 20.28, 20.32, 20.48, 20.48]
+367.34000000000003
+18.367
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4058, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.23972225189209, 'TIME_S_1KI': 2.5233421024869616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.52500621795656, 'W': 22.316341787391842, 'J_1KI': 80.218089260216, 'W_1KI': 5.499344945143382, 'W_D': 3.949341787391841, 'J_D': 57.60843430995939, 'W_D_1KI': 0.9732237031522526, 'J_D_1KI': 0.2398284137881352}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..4a8e502
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4098, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.480108499526978, "TIME_S_1KI": 2.5573715225785696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.20645687103274, "W": 23.049017782427377, "J_1KI": 82.28561661079374, "W_1KI": 5.624455290977886, "W_D": 4.55101778242738, "J_D": 66.58125721693045, "W_D_1KI": 1.1105460669661738, "J_D_1KI": 0.2709970880834977}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..261c286
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.2717466354370117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.2910, 0.1313, 0.5488,  ..., 0.2360, 0.4747, 0.6889])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.2717466354370117 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 suitesparse csr 3863 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 9.896336078643799}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7215, 0.6948, 0.2553,  ..., 0.1689, 0.6463, 0.2591])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 9.896336078643799 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 suitesparse csr 4098 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.480108499526978}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7345, 0.2555, 0.9166,  ..., 0.8790, 0.4016, 0.3913])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.480108499526978 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7345, 0.2555, 0.9166,  ..., 0.8790, 0.4016, 0.3913])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.480108499526978 seconds
+
+[20.52, 20.56, 20.72, 20.72, 20.8, 20.08, 20.16, 20.24, 20.2, 20.52]
+[20.84, 20.84, 21.28, 22.72, 24.36, 25.48, 26.68, 26.36, 26.36, 26.44, 26.0, 25.88, 25.64, 25.44]
+14.629970788955688
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.480108499526978, 'TIME_S_1KI': 2.5573715225785696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.20645687103274, 'W': 23.049017782427377}
+[20.52, 20.56, 20.72, 20.72, 20.8, 20.08, 20.16, 20.24, 20.2, 20.52, 20.68, 20.64, 20.44, 20.4, 20.44, 20.36, 20.52, 20.84, 21.36, 21.24]
+369.96
+18.497999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.480108499526978, 'TIME_S_1KI': 2.5573715225785696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.20645687103274, 'W': 23.049017782427377, 'J_1KI': 82.28561661079374, 'W_1KI': 5.624455290977886, 'W_D': 4.55101778242738, 'J_D': 66.58125721693045, 'W_D_1KI': 1.1105460669661738, 'J_D_1KI': 0.2709970880834977}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..516dede
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4157, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.633260726928711, "TIME_S_1KI": 2.557916941767792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.1796061038971, "W": 23.312957380746372, "J_1KI": 81.5923998325468, "W_1KI": 5.608120611197106, "W_D": 4.876957380746372, "J_D": 70.95472515010833, "W_D_1KI": 1.1731915758350668, "J_D_1KI": 0.28222073029469974}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..b871235
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.2795755863189697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.0284, 0.4133, 0.5098,  ..., 0.2031, 0.9951, 0.4322])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.2795755863189697 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 suitesparse csr 3755 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 9.483984231948853}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7053, 0.2006, 0.9966,  ..., 0.7749, 0.9349, 0.1565])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 9.483984231948853 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 suitesparse csr 4157 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.633260726928711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3785, 0.1670, 0.5681,  ..., 0.0463, 0.1615, 0.4715])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.633260726928711 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.3785, 0.1670, 0.5681,  ..., 0.0463, 0.1615, 0.4715])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.633260726928711 seconds
+
+[20.36, 20.52, 20.4, 20.4, 20.72, 20.52, 20.56, 20.48, 20.32, 20.24]
+[20.32, 20.36, 21.44, 23.52, 25.28, 26.32, 27.28, 27.28, 27.44, 26.44, 25.64, 25.64, 25.4, 25.72]
+14.548973798751831
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.633260726928711, 'TIME_S_1KI': 2.557916941767792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.1796061038971, 'W': 23.312957380746372}
+[20.36, 20.52, 20.4, 20.4, 20.72, 20.52, 20.56, 20.48, 20.32, 20.24, 20.6, 20.44, 20.4, 20.2, 20.28, 20.52, 20.68, 20.72, 20.64, 20.64]
+368.72
+18.436
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.633260726928711, 'TIME_S_1KI': 2.557916941767792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.1796061038971, 'W': 23.312957380746372, 'J_1KI': 81.5923998325468, 'W_1KI': 5.608120611197106, 'W_D': 4.876957380746372, 'J_D': 70.95472515010833, 'W_D_1KI': 1.1731915758350668, 'J_D_1KI': 0.28222073029469974}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..2cc2723
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 43083, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.477100133895874, "TIME_S_1KI": 0.2431840896385088, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 851.2608031511307, "W": 65.15, "J_1KI": 19.758624124390845, "W_1KI": 1.5121973864401275, "W_D": 29.482000000000006, "J_D": 385.2167459478379, "W_D_1KI": 0.6843070352575262, "J_D_1KI": 0.01588345833060665}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..ba290cf
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.03452706336975098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.7546, 0.5295, 0.0524,  ..., 0.5498, 0.1309, 0.9141])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.03452706336975098 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30410', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.411336183547974}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.6311, 0.0149, 0.4270,  ..., 0.8172, 0.7151, 0.0022])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 7.411336183547974 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '43083', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.477100133895874}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5533, 0.0502, 0.2758,  ..., 0.2070, 0.2491, 0.2171])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.477100133895874 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5533, 0.0502, 0.2758,  ..., 0.2070, 0.2491, 0.2171])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.477100133895874 seconds
+
+[40.15, 38.92, 39.51, 39.69, 38.96, 39.49, 39.05, 39.12, 39.72, 39.3]
+[65.15]
+13.06616735458374
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 43083, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.477100133895874, 'TIME_S_1KI': 0.2431840896385088, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 851.2608031511307, 'W': 65.15}
+[40.15, 38.92, 39.51, 39.69, 38.96, 39.49, 39.05, 39.12, 39.72, 39.3, 39.55, 39.14, 39.79, 38.87, 39.85, 39.3, 43.36, 40.57, 39.04, 38.96]
+713.36
+35.668
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 43083, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.477100133895874, 'TIME_S_1KI': 0.2431840896385088, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 851.2608031511307, 'W': 65.15, 'J_1KI': 19.758624124390845, 'W_1KI': 1.5121973864401275, 'W_D': 29.482000000000006, 'J_D': 385.2167459478379, 'W_D_1KI': 0.6843070352575262, 'J_D_1KI': 0.01588345833060665}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..f4400a3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 40969, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.524323463439941, "TIME_S_1KI": 0.2568850463384496, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.6081127786636, "W": 65.77, "J_1KI": 21.12836810219101, "W_1KI": 1.6053601503575874, "W_D": 30.476749999999996, "J_D": 401.1087433651685, "W_D_1KI": 0.7438978251848958, "J_D_1KI": 0.018157578295415946}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..ecd47d2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.03581523895263672}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.0344, 0.9687, 0.7566,  ..., 0.6552, 0.3662, 0.3237])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.03581523895263672 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '29317', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 7.513562440872192}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.8284, 0.2524, 0.6252,  ..., 0.4037, 0.1251, 0.4057])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 7.513562440872192 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '40969', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.524323463439941}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4861, 0.9697, 0.2121,  ..., 0.4428, 0.8610, 0.8241])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.524323463439941 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4861, 0.9697, 0.2121,  ..., 0.4428, 0.8610, 0.8241])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.524323463439941 seconds
+
+[39.52, 39.61, 38.88, 38.95, 38.86, 39.18, 39.3, 39.98, 39.45, 38.82]
+[65.77]
+13.161139011383057
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 40969, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.524323463439941, 'TIME_S_1KI': 0.2568850463384496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.6081127786636, 'W': 65.77}
+[39.52, 39.61, 38.88, 38.95, 38.86, 39.18, 39.3, 39.98, 39.45, 38.82, 39.48, 39.6, 39.03, 38.94, 38.92, 39.03, 39.2, 38.88, 39.41, 39.47]
+705.865
+35.29325
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 40969, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.524323463439941, 'TIME_S_1KI': 0.2568850463384496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.6081127786636, 'W': 65.77, 'J_1KI': 21.12836810219101, 'W_1KI': 1.6053601503575874, 'W_D': 30.476749999999996, 'J_D': 401.1087433651685, 'W_D_1KI': 0.7438978251848958, 'J_D_1KI': 0.018157578295415946}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..61da900
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 38858, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.485401391983032, "TIME_S_1KI": 0.269838936434789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.5578077483177, "W": 65.17, "J_1KI": 22.069015588767247, "W_1KI": 1.6771321220855422, "W_D": 29.260250000000006, "J_D": 385.0292441946865, "W_D_1KI": 0.7530045293118536, "J_D_1KI": 0.019378365569814544}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..7a0d3ec
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.03696393966674805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.7337, 0.4673, 0.2553,  ..., 0.7979, 0.4172, 0.5121])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.03696393966674805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28406', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 7.675543546676636}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1361, 0.1751, 0.5794,  ..., 0.9159, 0.1555, 0.6066])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 7.675543546676636 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38858', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.485401391983032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.0846, 0.1902, 0.2817,  ..., 0.3581, 0.4321, 0.1894])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.485401391983032 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.0846, 0.1902, 0.2817,  ..., 0.3581, 0.4321, 0.1894])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.485401391983032 seconds
+
+[40.2, 38.88, 39.37, 39.26, 39.72, 44.47, 39.38, 38.79, 39.26, 39.05]
+[65.17]
+13.15878176689148
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 38858, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.485401391983032, 'TIME_S_1KI': 0.269838936434789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5578077483177, 'W': 65.17}
+[40.2, 38.88, 39.37, 39.26, 39.72, 44.47, 39.38, 38.79, 39.26, 39.05, 39.64, 39.12, 44.98, 38.93, 39.41, 39.85, 39.14, 39.66, 39.04, 38.98]
+718.1949999999999
+35.909749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 38858, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.485401391983032, 'TIME_S_1KI': 0.269838936434789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5578077483177, 'W': 65.17, 'J_1KI': 22.069015588767247, 'W_1KI': 1.6771321220855422, 'W_D': 29.260250000000006, 'J_D': 385.0292441946865, 'W_D_1KI': 0.7530045293118536, 'J_D_1KI': 0.019378365569814544}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..b00300c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 37528, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.504584074020386, "TIME_S_1KI": 0.2799132400879446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.7810602784157, "W": 65.25, "J_1KI": 22.8304482060972, "W_1KI": 1.7387017693455553, "W_D": 29.265749999999997, "J_D": 384.28107762211556, "W_D_1KI": 0.7798377211681943, "J_D_1KI": 0.020780156714138624}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..8caa78f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.038069963455200195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.7577, 0.4294, 0.6676,  ..., 0.3864, 0.3290, 0.6442])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.038069963455200195 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '27580', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 7.716523170471191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4888, 0.6628, 0.4200,  ..., 0.1971, 0.5545, 0.6701])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 7.716523170471191 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37528', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.504584074020386}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3669, 0.2164, 0.4457,  ..., 0.8223, 0.7437, 0.0729])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.504584074020386 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3669, 0.2164, 0.4457,  ..., 0.8223, 0.7437, 0.0729])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.504584074020386 seconds
+
+[39.45, 39.11, 38.89, 40.83, 38.96, 38.82, 38.8, 53.65, 39.56, 39.42]
+[65.25]
+13.130744218826294
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 37528, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.504584074020386, 'TIME_S_1KI': 0.2799132400879446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7810602784157, 'W': 65.25}
+[39.45, 39.11, 38.89, 40.83, 38.96, 38.82, 38.8, 53.65, 39.56, 39.42, 39.55, 38.87, 39.26, 38.79, 38.89, 39.08, 39.11, 38.87, 39.16, 39.65]
+719.6850000000001
+35.98425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 37528, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.504584074020386, 'TIME_S_1KI': 0.2799132400879446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7810602784157, 'W': 65.25, 'J_1KI': 22.8304482060972, 'W_1KI': 1.7387017693455553, 'W_D': 29.265749999999997, 'J_D': 384.28107762211556, 'W_D_1KI': 0.7798377211681943, 'J_D_1KI': 0.020780156714138624}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..4eb5957
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 36057, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.52190113067627, "TIME_S_1KI": 0.29181299416690987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 870.8484698581696, "W": 65.88, "J_1KI": 24.151994615696523, "W_1KI": 1.8271070804559446, "W_D": 30.036999999999992, "J_D": 397.05032618594157, "W_D_1KI": 0.8330421277421858, "J_D_1KI": 0.02310347859617233}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..4921121
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.03890419006347656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.5918, 0.7842, 0.2922,  ..., 0.7811, 0.4187, 0.7857])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.03890419006347656 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26989', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 7.8591344356536865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.4944, 0.9993, 0.3541,  ..., 0.8129, 0.1587, 0.8643])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 7.8591344356536865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36057', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.52190113067627}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.3640, 0.1663, 0.5631,  ..., 0.5487, 0.9918, 0.2869])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.52190113067627 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.3640, 0.1663, 0.5631,  ..., 0.5487, 0.9918, 0.2869])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.52190113067627 seconds
+
+[40.01, 39.03, 39.43, 39.2, 39.48, 39.28, 44.38, 38.91, 39.07, 39.26]
+[65.88]
+13.218707799911499
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 36057, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.52190113067627, 'TIME_S_1KI': 0.29181299416690987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.8484698581696, 'W': 65.88}
+[40.01, 39.03, 39.43, 39.2, 39.48, 39.28, 44.38, 38.91, 39.07, 39.26, 40.2, 38.93, 39.03, 44.7, 39.08, 39.28, 39.65, 38.85, 39.34, 38.97]
+716.8600000000001
+35.843
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 36057, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.52190113067627, 'TIME_S_1KI': 0.29181299416690987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.8484698581696, 'W': 65.88, 'J_1KI': 24.151994615696523, 'W_1KI': 1.8271070804559446, 'W_D': 30.036999999999992, 'J_D': 397.05032618594157, 'W_D_1KI': 0.8330421277421858, 'J_D_1KI': 0.02310347859617233}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..ca92693
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35521, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.493959188461304, "TIME_S_1KI": 0.29542972293745395, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.9640459084511, "W": 65.31, "J_1KI": 24.181865541748575, "W_1KI": 1.838630669181611, "W_D": 29.498000000000005, "J_D": 387.96082416486746, "W_D_1KI": 0.8304383322541596, "J_D_1KI": 0.023378799365281373}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..c641e9e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.03909754753112793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.9249, 0.4269, 0.6415,  ..., 0.8972, 0.4910, 0.7842])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.03909754753112793 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26855', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 7.938306093215942}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.2676, 0.4893, 0.6704,  ..., 0.5113, 0.7611, 0.8978])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 7.938306093215942 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35521', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.493959188461304}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.9055, 0.7860, 0.6711,  ..., 0.5522, 0.9145, 0.1181])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.493959188461304 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.9055, 0.7860, 0.6711,  ..., 0.5522, 0.9145, 0.1181])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.493959188461304 seconds
+
+[40.65, 39.34, 39.0, 39.01, 39.5, 39.34, 39.48, 38.85, 44.18, 39.33]
+[65.31]
+13.152106046676636
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35521, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.493959188461304, 'TIME_S_1KI': 0.29542972293745395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9640459084511, 'W': 65.31}
+[40.65, 39.34, 39.0, 39.01, 39.5, 39.34, 39.48, 38.85, 44.18, 39.33, 40.58, 38.87, 39.12, 39.04, 38.99, 44.1, 39.39, 38.88, 39.2, 39.34]
+716.24
+35.812
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35521, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.493959188461304, 'TIME_S_1KI': 0.29542972293745395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9640459084511, 'W': 65.31, 'J_1KI': 24.181865541748575, 'W_1KI': 1.838630669181611, 'W_D': 29.498000000000005, 'J_D': 387.96082416486746, 'W_D_1KI': 0.8304383322541596, 'J_D_1KI': 0.023378799365281373}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..c0ad406
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35173, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.447309255599976, "TIME_S_1KI": 0.29702639114093127, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 861.1301214361191, "W": 65.59, "J_1KI": 24.48270325067862, "W_1KI": 1.8647826457794332, "W_D": 29.845750000000002, "J_D": 391.84440191876894, "W_D_1KI": 0.8485414948966538, "J_D_1KI": 0.024124797284754036}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..bf75d87
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.03972125053405762}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7495, 0.7125, 0.1641,  ..., 0.3237, 0.6914, 0.4632])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.03972125053405762 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26434', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.891004800796509}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4191, 0.3155, 0.4177,  ..., 0.6606, 0.8993, 0.7459])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 7.891004800796509 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35173', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.447309255599976}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.1483, 0.5699, 0.1162,  ..., 0.9827, 0.6104, 0.1631])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.447309255599976 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.1483, 0.5699, 0.1162,  ..., 0.9827, 0.6104, 0.1631])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.447309255599976 seconds
+
+[39.68, 44.32, 39.73, 38.97, 39.63, 39.5, 39.25, 39.37, 39.5, 39.02]
+[65.59]
+13.128984928131104
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35173, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.447309255599976, 'TIME_S_1KI': 0.29702639114093127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.1301214361191, 'W': 65.59}
+[39.68, 44.32, 39.73, 38.97, 39.63, 39.5, 39.25, 39.37, 39.5, 39.02, 39.68, 38.91, 39.15, 39.15, 41.28, 39.51, 39.35, 39.72, 38.94, 38.83]
+714.885
+35.74425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35173, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.447309255599976, 'TIME_S_1KI': 0.29702639114093127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.1301214361191, 'W': 65.59, 'J_1KI': 24.48270325067862, 'W_1KI': 1.8647826457794332, 'W_D': 29.845750000000002, 'J_D': 391.84440191876894, 'W_D_1KI': 0.8485414948966538, 'J_D_1KI': 0.024124797284754036}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..605e666
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 34640, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.49686861038208, "TIME_S_1KI": 0.3030273848262725, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.7586107826234, "W": 65.64, "J_1KI": 24.906426408274346, "W_1KI": 1.894919168591224, "W_D": 29.927249999999994, "J_D": 393.35759650433056, "W_D_1KI": 0.8639506351039259, "J_D_1KI": 0.02494083819584082}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..ccf8536
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_040.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.04068922996520996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.2414, 0.2921, 0.6087,  ..., 0.3322, 0.2571, 0.0046])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.04068922996520996 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25805', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 7.821873188018799}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.5981, 0.0468, 0.4788,  ..., 0.5461, 0.0602, 0.7262])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 7.821873188018799 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '34640', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.49686861038208}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.4047, 0.6069, 0.9492,  ..., 0.3591, 0.7700, 0.1279])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.49686861038208 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.4047, 0.6069, 0.9492,  ..., 0.3591, 0.7700, 0.1279])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.49686861038208 seconds
+
+[40.13, 39.08, 39.18, 38.83, 39.15, 39.23, 39.3, 43.93, 39.04, 39.67]
+[65.64]
+13.14379358291626
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34640, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.49686861038208, 'TIME_S_1KI': 0.3030273848262725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.7586107826234, 'W': 65.64}
+[40.13, 39.08, 39.18, 38.83, 39.15, 39.23, 39.3, 43.93, 39.04, 39.67, 40.65, 38.97, 38.89, 42.01, 41.52, 38.84, 38.88, 38.74, 38.96, 38.96]
+714.2550000000001
+35.71275000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34640, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.49686861038208, 'TIME_S_1KI': 0.3030273848262725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.7586107826234, 'W': 65.64, 'J_1KI': 24.906426408274346, 'W_1KI': 1.894919168591224, 'W_D': 29.927249999999994, 'J_D': 393.35759650433056, 'W_D_1KI': 0.8639506351039259, 'J_D_1KI': 0.02494083819584082}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..cb38459
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 34316, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.484640121459961, "TIME_S_1KI": 0.3055321168393741, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.8062327885629, "W": 65.67, "J_1KI": 25.142972164254658, "W_1KI": 1.9136845786222172, "W_D": 30.424499999999995, "J_D": 399.73272772157185, "W_D_1KI": 0.8865980883552861, "J_D_1KI": 0.025836288855207078}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..95736a8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.04044318199157715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9025, 0.6353, 0.0498,  ..., 0.1623, 0.8280, 0.3887])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.04044318199157715 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25962', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.462014198303223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.0457, 0.1982, 0.5371,  ..., 0.8451, 0.9072, 0.4847])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 8.462014198303223 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32214', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.856708288192749}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9221, 0.7340, 0.0148,  ..., 0.4303, 0.3457, 0.2080])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 9.856708288192749 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '34316', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.484640121459961}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9270, 0.0174, 0.7942,  ..., 0.2212, 0.8209, 0.0128])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.484640121459961 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9270, 0.0174, 0.7942,  ..., 0.2212, 0.8209, 0.0128])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.484640121459961 seconds
+
+[40.45, 39.27, 39.34, 38.8, 39.26, 39.86, 39.18, 39.18, 38.87, 39.35]
+[65.67]
+13.138514280319214
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34316, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.484640121459961, 'TIME_S_1KI': 0.3055321168393741, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8062327885629, 'W': 65.67}
+[40.45, 39.27, 39.34, 38.8, 39.26, 39.86, 39.18, 39.18, 38.87, 39.35, 39.9, 39.2, 38.96, 38.82, 39.04, 38.79, 39.3, 38.81, 38.94, 38.88]
+704.9100000000001
+35.24550000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34316, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.484640121459961, 'TIME_S_1KI': 0.3055321168393741, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8062327885629, 'W': 65.67, 'J_1KI': 25.142972164254658, 'W_1KI': 1.9136845786222172, 'W_D': 30.424499999999995, 'J_D': 399.73272772157185, 'W_D_1KI': 0.8865980883552861, 'J_D_1KI': 0.025836288855207078}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..2e8bd06
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 33846, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.476279973983765, "TIME_S_1KI": 0.30952786072161453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 854.3117296695709, "W": 65.0, "J_1KI": 25.241143109069636, "W_1KI": 1.9204632748330674, "W_D": 29.845749999999995, "J_D": 392.270373935163, "W_D_1KI": 0.8818102582284464, "J_D_1KI": 0.026053603327673768}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..c782870
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.05826926231384277}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.3208, 0.3763, 0.0982,  ..., 0.0304, 0.4098, 0.3411])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.05826926231384277 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18019', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 5.589941501617432}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.5685, 0.4594, 0.6717,  ..., 0.1414, 0.1087, 0.0383])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 5.589941501617432 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '33846', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.476279973983765}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.0244, 0.4569, 0.3060,  ..., 0.8582, 0.4080, 0.4859])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.476279973983765 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.0244, 0.4569, 0.3060,  ..., 0.8582, 0.4080, 0.4859])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.476279973983765 seconds
+
+[39.28, 39.38, 38.81, 38.74, 39.12, 38.69, 39.22, 39.05, 39.16, 38.7]
+[65.0]
+13.14325737953186
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33846, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.476279973983765, 'TIME_S_1KI': 0.30952786072161453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.3117296695709, 'W': 65.0}
+[39.28, 39.38, 38.81, 38.74, 39.12, 38.69, 39.22, 39.05, 39.16, 38.7, 40.2, 38.98, 38.89, 38.78, 39.85, 38.75, 38.88, 39.08, 39.11, 39.01]
+703.085
+35.154250000000005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33846, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.476279973983765, 'TIME_S_1KI': 0.30952786072161453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.3117296695709, 'W': 65.0, 'J_1KI': 25.241143109069636, 'W_1KI': 1.9204632748330674, 'W_D': 29.845749999999995, 'J_D': 392.270373935163, 'W_D_1KI': 0.8818102582284464, 'J_D_1KI': 0.026053603327673768}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..c15af60
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 33510, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.483581304550171, "TIME_S_1KI": 0.31284933764697614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.1138673710824, "W": 65.23, "J_1KI": 25.60769523637966, "W_1KI": 1.9465831095195465, "W_D": 29.08325, "J_D": 382.59604680699107, "W_D_1KI": 0.8678976424947777, "J_D_1KI": 0.02589966107116615}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..df4afb4
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.04136204719543457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.2193, 0.1796, 0.4399,  ..., 0.4451, 0.3006, 0.2875])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.04136204719543457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25385', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.95404314994812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.9926, 0.3031, 0.5874,  ..., 0.7443, 0.4408, 0.2995])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 7.95404314994812 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '33510', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.483581304550171}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.7506, 0.9011, 0.4947,  ..., 0.5418, 0.4388, 0.4192])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.483581304550171 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.7506, 0.9011, 0.4947,  ..., 0.5418, 0.4388, 0.4192])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.483581304550171 seconds
+
+[40.21, 39.23, 39.1, 39.38, 39.37, 39.09, 39.81, 39.02, 40.1, 38.85]
+[65.23]
+13.155202627182007
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33510, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.483581304550171, 'TIME_S_1KI': 0.31284933764697614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.1138673710824, 'W': 65.23}
+[40.21, 39.23, 39.1, 39.38, 39.37, 39.09, 39.81, 39.02, 40.1, 38.85, 39.87, 39.56, 40.29, 39.1, 50.99, 41.72, 39.11, 38.85, 39.27, 38.96]
+722.9350000000001
+36.146750000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33510, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.483581304550171, 'TIME_S_1KI': 0.31284933764697614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.1138673710824, 'W': 65.23, 'J_1KI': 25.60769523637966, 'W_1KI': 1.9465831095195465, 'W_D': 29.08325, 'J_D': 382.59604680699107, 'W_D_1KI': 0.8678976424947777, 'J_D_1KI': 0.02589966107116615}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..0e903f2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 33249, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.519275426864624, "TIME_S_1KI": 0.31637870091926446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 863.3752274990081, "W": 65.32, "J_1KI": 25.966953216608264, "W_1KI": 1.9645703630184366, "W_D": 28.43549999999999, "J_D": 375.84975936233985, "W_D_1KI": 0.8552287286835691, "J_D_1KI": 0.025721938364569433}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..4075bff
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.04165911674499512}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.4821, 0.9808, 0.4682,  ..., 0.6820, 0.0258, 0.9641])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.04165911674499512 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25204', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 7.959317207336426}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6328, 0.1079, 0.7367,  ..., 0.9728, 0.3924, 0.7858])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 7.959317207336426 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '33249', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.519275426864624}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6548, 0.2328, 0.3318,  ..., 0.9362, 0.2713, 0.7539])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.519275426864624 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6548, 0.2328, 0.3318,  ..., 0.9362, 0.2713, 0.7539])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.519275426864624 seconds
+
+[45.93, 38.91, 39.0, 39.34, 38.98, 39.71, 38.93, 38.95, 38.97, 39.26]
+[65.32]
+13.217624425888062
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33249, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.519275426864624, 'TIME_S_1KI': 0.31637870091926446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 863.3752274990081, 'W': 65.32}
+[45.93, 38.91, 39.0, 39.34, 38.98, 39.71, 38.93, 38.95, 38.97, 39.26, 46.57, 45.46, 39.63, 38.96, 57.45, 39.39, 39.07, 38.93, 40.0, 40.26]
+737.69
+36.8845
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33249, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.519275426864624, 'TIME_S_1KI': 0.31637870091926446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 863.3752274990081, 'W': 65.32, 'J_1KI': 25.966953216608264, 'W_1KI': 1.9645703630184366, 'W_D': 28.43549999999999, 'J_D': 375.84975936233985, 'W_D_1KI': 0.8552287286835691, 'J_D_1KI': 0.025721938364569433}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..2ed5a03
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 32796, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.488871335983276, "TIME_S_1KI": 0.3198216653245297, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.9457808732986, "W": 64.95, "J_1KI": 26.190565339471235, "W_1KI": 1.9804244420051227, "W_D": 29.534750000000003, "J_D": 390.588897638917, "W_D_1KI": 0.900559519453592, "J_D_1KI": 0.027459431621343823}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..4e4ed45
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.042150020599365234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1926, 0.4247, 0.2336,  ..., 0.9542, 0.9130, 0.4279])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.042150020599365234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24911', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 7.975389719009399}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.7725, 0.0253, 0.1316,  ..., 0.7396, 0.9784, 0.7056])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 7.975389719009399 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32796', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.488871335983276}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1802, 0.5827, 0.7701,  ..., 0.8741, 0.2809, 0.8210])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.488871335983276 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1802, 0.5827, 0.7701,  ..., 0.8741, 0.2809, 0.8210])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.488871335983276 seconds
+
+[39.86, 39.29, 39.08, 38.93, 39.59, 39.04, 39.49, 40.53, 39.33, 39.32]
+[64.95]
+13.22472333908081
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32796, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.488871335983276, 'TIME_S_1KI': 0.3198216653245297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9457808732986, 'W': 64.95}
+[39.86, 39.29, 39.08, 38.93, 39.59, 39.04, 39.49, 40.53, 39.33, 39.32, 40.13, 40.37, 38.95, 38.92, 39.06, 38.93, 38.92, 38.89, 39.88, 38.9]
+708.305
+35.41525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32796, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.488871335983276, 'TIME_S_1KI': 0.3198216653245297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9457808732986, 'W': 64.95, 'J_1KI': 26.190565339471235, 'W_1KI': 1.9804244420051227, 'W_D': 29.534750000000003, 'J_D': 390.588897638917, 'W_D_1KI': 0.900559519453592, 'J_D_1KI': 0.027459431621343823}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..101856c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35112, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.489492654800415, "TIME_S_1KI": 0.2987438099453297, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.1631603813172, "W": 65.18, "J_1KI": 24.412256789169437, "W_1KI": 1.856345408976988, "W_D": 29.770500000000013, "J_D": 391.50315842485446, "W_D_1KI": 0.8478725222146278, "J_D_1KI": 0.024147656704677254}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..c61fda8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.040030717849731445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.8345, 0.4837, 0.6160,  ..., 0.4768, 0.8959, 0.2381])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.040030717849731445 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26229', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 7.84349513053894}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.1702, 0.2800, 0.6333,  ..., 0.2420, 0.0611, 0.2988])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 7.84349513053894 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35112', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.489492654800415}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.2642, 0.3297, 0.3254,  ..., 0.7339, 0.6304, 0.9990])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.489492654800415 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.2642, 0.3297, 0.3254,  ..., 0.7339, 0.6304, 0.9990])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.489492654800415 seconds
+
+[40.36, 39.64, 39.47, 39.55, 38.99, 39.0, 38.96, 38.98, 38.95, 40.52]
+[65.18]
+13.150708198547363
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35112, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.489492654800415, 'TIME_S_1KI': 0.2987438099453297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1631603813172, 'W': 65.18}
+[40.36, 39.64, 39.47, 39.55, 38.99, 39.0, 38.96, 38.98, 38.95, 40.52, 40.81, 39.56, 39.34, 39.29, 39.58, 39.2, 39.03, 39.04, 39.09, 39.35]
+708.1899999999999
+35.409499999999994
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35112, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.489492654800415, 'TIME_S_1KI': 0.2987438099453297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1631603813172, 'W': 65.18, 'J_1KI': 24.412256789169437, 'W_1KI': 1.856345408976988, 'W_D': 29.770500000000013, 'J_D': 391.50315842485446, 'W_D_1KI': 0.8478725222146278, 'J_D_1KI': 0.024147656704677254}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..bc43cfa
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 32317, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.516169548034668, "TIME_S_1KI": 0.32540673787896984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 867.264186372757, "W": 65.48, "J_1KI": 26.836160113028964, "W_1KI": 2.026178172478881, "W_D": 19.231750000000005, "J_D": 254.71912059062726, "W_D_1KI": 0.5950970077668103, "J_D_1KI": 0.018414364197382502}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..419c65f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.04397153854370117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.8835, 0.2905, 0.1746,  ..., 0.2508, 0.5375, 0.9105])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.04397153854370117 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23879', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 7.75836181640625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2327, 0.1142, 0.1910,  ..., 0.9478, 0.2863, 0.7780])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 7.75836181640625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32317', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.516169548034668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.8802, 0.8846, 0.9410,  ..., 0.6286, 0.1450, 0.7013])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.516169548034668 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.8802, 0.8846, 0.9410,  ..., 0.6286, 0.1450, 0.7013])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.516169548034668 seconds
+
+[39.64, 38.82, 40.18, 38.79, 38.93, 39.23, 39.2, 38.83, 40.26, 39.15]
+[65.48]
+13.244718790054321
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.516169548034668, 'TIME_S_1KI': 0.32540673787896984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.264186372757, 'W': 65.48}
+[39.64, 38.82, 40.18, 38.79, 38.93, 39.23, 39.2, 38.83, 40.26, 39.15, 68.29, 72.7, 70.94, 63.35, 66.33, 64.79, 60.51, 60.49, 58.48, 39.19]
+924.965
+46.24825
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.516169548034668, 'TIME_S_1KI': 0.32540673787896984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.264186372757, 'W': 65.48, 'J_1KI': 26.836160113028964, 'W_1KI': 2.026178172478881, 'W_D': 19.231750000000005, 'J_D': 254.71912059062726, 'W_D_1KI': 0.5950970077668103, 'J_D_1KI': 0.018414364197382502}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..789ee6a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 32087, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.519771337509155, "TIME_S_1KI": 0.3278515080097596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.6138118386268, "W": 65.35, "J_1KI": 26.758930776907373, "W_1KI": 2.0366503568423346, "W_D": 29.187999999999995, "J_D": 383.49227146053306, "W_D_1KI": 0.9096518839405364, "J_D_1KI": 0.028349546044832377}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..1488ee3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_080.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.04278707504272461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1787, 0.3660, 0.7014,  ..., 0.8083, 0.9428, 0.1369])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.04278707504272461 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24540', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 8.030221223831177}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.8407, 0.6177, 0.3691,  ..., 0.8590, 0.0307, 0.6041])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 8.030221223831177 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32087', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.519771337509155}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.4587, 0.9274, 0.4609,  ..., 0.2903, 0.3738, 0.9730])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.519771337509155 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.4587, 0.9274, 0.4609,  ..., 0.2903, 0.3738, 0.9730])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.519771337509155 seconds
+
+[40.27, 38.88, 39.08, 38.91, 38.93, 52.62, 41.62, 39.05, 39.08, 39.3]
+[65.35]
+13.138696432113647
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.519771337509155, 'TIME_S_1KI': 0.3278515080097596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.6138118386268, 'W': 65.35}
+[40.27, 38.88, 39.08, 38.91, 38.93, 52.62, 41.62, 39.05, 39.08, 39.3, 40.69, 39.31, 39.39, 39.39, 39.71, 39.26, 39.57, 40.06, 38.81, 38.88]
+723.24
+36.162
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.519771337509155, 'TIME_S_1KI': 0.3278515080097596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.6138118386268, 'W': 65.35, 'J_1KI': 26.758930776907373, 'W_1KI': 2.0366503568423346, 'W_D': 29.187999999999995, 'J_D': 383.49227146053306, 'W_D_1KI': 0.9096518839405364, 'J_D_1KI': 0.028349546044832377}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..b427c33
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 31855, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.502902507781982, "TIME_S_1KI": 0.3297097004483435, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.4069772052765, "W": 65.08, "J_1KI": 26.85314635709548, "W_1KI": 2.043007377177837, "W_D": 29.676000000000002, "J_D": 390.0592725191117, "W_D_1KI": 0.9315962957149585, "J_D_1KI": 0.029244900195101505}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..ea5504f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.04311323165893555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.1764, 0.0789, 0.0500,  ..., 0.7871, 0.4459, 0.1044])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.04311323165893555 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24354', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.02746057510376}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.4941, 0.1029, 0.9153,  ..., 0.6749, 0.6031, 0.7456])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 8.02746057510376 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31855', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.502902507781982}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.8603, 0.6409, 0.8738,  ..., 0.6986, 0.4715, 0.6839])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.502902507781982 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.8603, 0.6409, 0.8738,  ..., 0.6986, 0.4715, 0.6839])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.502902507781982 seconds
+
+[39.81, 38.99, 39.18, 39.42, 39.32, 38.97, 39.04, 38.84, 38.98, 38.98]
+[65.08]
+13.143930196762085
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31855, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.502902507781982, 'TIME_S_1KI': 0.3297097004483435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.4069772052765, 'W': 65.08}
+[39.81, 38.99, 39.18, 39.42, 39.32, 38.97, 39.04, 38.84, 38.98, 38.98, 40.72, 41.01, 39.07, 38.92, 39.38, 39.46, 39.36, 39.9, 39.05, 38.87]
+708.0799999999999
+35.403999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31855, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.502902507781982, 'TIME_S_1KI': 0.3297097004483435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.4069772052765, 'W': 65.08, 'J_1KI': 26.85314635709548, 'W_1KI': 2.043007377177837, 'W_D': 29.676000000000002, 'J_D': 390.0592725191117, 'W_D_1KI': 0.9315962957149585, 'J_D_1KI': 0.029244900195101505}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..bcdad54
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 31385, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.492711782455444, "TIME_S_1KI": 0.3343225038220629, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 859.8343842601777, "W": 65.48, "J_1KI": 27.39634807265183, "W_1KI": 2.086346981041899, "W_D": 30.13275000000001, "J_D": 395.6807352216841, "W_D_1KI": 0.9601003664170785, "J_D_1KI": 0.03059105835326043}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..7bd0a28
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,91 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.04356789588928223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8351, 0.3440, 0.1738,  ..., 0.1625, 0.2091, 0.2639])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.04356789588928223 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 8.062743186950684}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.9872, 0.5634, 0.4255,  ..., 0.8197, 0.2556, 0.8412])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 8.062743186950684 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31385', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.492711782455444}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([2.4607e-01, 6.8716e-01, 8.5167e-01,  ..., 8.4214e-01, 1.2308e-04,
+        3.6772e-01])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.492711782455444 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([2.4607e-01, 6.8716e-01, 8.5167e-01,  ..., 8.4214e-01, 1.2308e-04,
+        3.6772e-01])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.492711782455444 seconds
+
+[39.64, 39.21, 39.38, 39.38, 39.38, 39.42, 40.08, 38.98, 38.98, 38.88]
+[65.48]
+13.13125205039978
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31385, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.492711782455444, 'TIME_S_1KI': 0.3343225038220629, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 859.8343842601777, 'W': 65.48}
+[39.64, 39.21, 39.38, 39.38, 39.38, 39.42, 40.08, 38.98, 38.98, 38.88, 40.36, 38.86, 39.76, 38.91, 38.97, 38.87, 39.11, 39.45, 38.89, 39.75]
+706.9449999999999
+35.347249999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31385, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.492711782455444, 'TIME_S_1KI': 0.3343225038220629, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 859.8343842601777, 'W': 65.48, 'J_1KI': 27.39634807265183, 'W_1KI': 2.086346981041899, 'W_D': 30.13275000000001, 'J_D': 395.6807352216841, 'W_D_1KI': 0.9601003664170785, 'J_D_1KI': 0.03059105835326043}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..c7609a2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30992, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.461604595184326, "TIME_S_1KI": 0.3375582277744039, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3391364908218, "W": 65.31, "J_1KI": 27.598707295134933, "W_1KI": 2.107318017552917, "W_D": 30.06725, "J_D": 393.77883404767516, "W_D_1KI": 0.9701616546205473, "J_D_1KI": 0.03130361559823655}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..4dc3c3e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,90 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.04361534118652344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([1.2265e-01, 9.2792e-01, 5.3994e-01,  ..., 3.2830e-04, 7.8885e-01,
+        4.0460e-01])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.04361534118652344 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24074', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.15611457824707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.1199, 0.5589, 0.0427,  ..., 0.8467, 0.0895, 0.6403])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 8.15611457824707 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30992', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.461604595184326}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.8237, 0.6815, 0.7303,  ..., 0.8961, 0.9057, 0.1353])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.461604595184326 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.8237, 0.6815, 0.7303,  ..., 0.8961, 0.9057, 0.1353])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.461604595184326 seconds
+
+[39.49, 38.75, 39.8, 39.15, 38.92, 38.75, 39.12, 39.0, 39.17, 39.28]
+[65.31]
+13.09660291671753
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30992, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.461604595184326, 'TIME_S_1KI': 0.3375582277744039, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3391364908218, 'W': 65.31}
+[39.49, 38.75, 39.8, 39.15, 38.92, 38.75, 39.12, 39.0, 39.17, 39.28, 39.59, 39.49, 39.32, 39.13, 39.22, 39.92, 38.97, 38.72, 38.76, 38.97]
+704.855
+35.24275
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30992, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.461604595184326, 'TIME_S_1KI': 0.3375582277744039, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3391364908218, 'W': 65.31, 'J_1KI': 27.598707295134933, 'W_1KI': 2.107318017552917, 'W_D': 30.06725, 'J_D': 393.77883404767516, 'W_D_1KI': 0.9701616546205473, 'J_D_1KI': 0.03130361559823655}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..1daf65f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30854, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.476550340652466, "TIME_S_1KI": 0.3395524191564292, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.3580177187919, "W": 65.57, "J_1KI": 27.94963433327257, "W_1KI": 2.125170156219615, "W_D": 29.695499999999996, "J_D": 390.54678229629985, "W_D_1KI": 0.9624521942049652, "J_D_1KI": 0.031193757509722083}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..37bdcfc
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.04397273063659668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2431, 0.7715, 0.2563,  ..., 0.6208, 0.6009, 0.1052])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.04397273063659668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23878', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 8.125934362411499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2974, 0.3462, 0.9575,  ..., 0.4439, 0.5203, 0.5123])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 8.125934362411499 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30854', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.476550340652466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.3681, 0.4770, 0.3424,  ..., 0.0512, 0.2641, 0.6984])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.476550340652466 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.3681, 0.4770, 0.3424,  ..., 0.0512, 0.2641, 0.6984])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.476550340652466 seconds
+
+[40.58, 38.93, 39.34, 39.26, 39.14, 38.97, 38.95, 44.73, 39.56, 39.35]
+[65.57]
+13.151715993881226
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30854, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.476550340652466, 'TIME_S_1KI': 0.3395524191564292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.3580177187919, 'W': 65.57}
+[40.58, 38.93, 39.34, 39.26, 39.14, 38.97, 38.95, 44.73, 39.56, 39.35, 40.02, 39.33, 39.02, 38.97, 44.44, 39.29, 39.1, 39.34, 39.5, 39.29]
+717.49
+35.8745
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30854, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.476550340652466, 'TIME_S_1KI': 0.3395524191564292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.3580177187919, 'W': 65.57, 'J_1KI': 27.94963433327257, 'W_1KI': 2.125170156219615, 'W_D': 29.695499999999996, 'J_D': 390.54678229629985, 'W_D_1KI': 0.9624521942049652, 'J_D_1KI': 0.031193757509722083}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..45b4823
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30536, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.540117979049683, "TIME_S_1KI": 0.3451702246217475, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 861.2643821239473, "W": 65.4, "J_1KI": 28.204885450744932, "W_1KI": 2.141734346345298, "W_D": 29.81275, "J_D": 392.6094756600261, "W_D_1KI": 0.9763148414985592, "J_D_1KI": 0.031972584539512676}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..6c9a46f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.044251441955566406}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.6090, 0.7079, 0.0401,  ..., 0.3126, 0.5822, 0.6098])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.044251441955566406 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23728', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.158772230148315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.2992, 0.2859, 0.3611,  ..., 0.2485, 0.8593, 0.7485])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 8.158772230148315 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30536', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.540117979049683}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.7959, 0.6851, 0.8740,  ..., 0.0031, 0.3114, 0.8796])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.540117979049683 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.7959, 0.6851, 0.8740,  ..., 0.0031, 0.3114, 0.8796])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.540117979049683 seconds
+
+[40.59, 39.08, 39.45, 39.57, 39.25, 39.0, 39.96, 39.75, 39.54, 39.19]
+[65.4]
+13.169180154800415
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30536, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.540117979049683, 'TIME_S_1KI': 0.3451702246217475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.2643821239473, 'W': 65.4}
+[40.59, 39.08, 39.45, 39.57, 39.25, 39.0, 39.96, 39.75, 39.54, 39.19, 40.78, 41.19, 39.05, 39.13, 39.19, 39.09, 39.45, 39.45, 39.13, 40.37]
+711.7450000000001
+35.587250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30536, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.540117979049683, 'TIME_S_1KI': 0.3451702246217475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.2643821239473, 'W': 65.4, 'J_1KI': 28.204885450744932, 'W_1KI': 2.141734346345298, 'W_D': 29.81275, 'J_D': 392.6094756600261, 'W_D_1KI': 0.9763148414985592, 'J_D_1KI': 0.031972584539512676}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..2aaa9df
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30504, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.496288299560547, "TIME_S_1KI": 0.34409547271048213, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.5311336517334, "W": 65.2, "J_1KI": 28.112088042608622, "W_1KI": 2.1374246000524524, "W_D": 29.644750000000002, "J_D": 389.8971790540219, "W_D_1KI": 0.9718315630736953, "J_D_1KI": 0.03185915168744084}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..10eea86
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.04464125633239746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.4586, 0.6062, 0.8975,  ..., 0.3940, 0.6004, 0.1344])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.04464125633239746 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23520', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 8.095872640609741}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5706, 0.0682, 0.8978,  ..., 0.8999, 0.4499, 0.9129])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 8.095872640609741 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30504', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.496288299560547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.3226, 0.6718, 0.8788,  ..., 0.8065, 0.0580, 0.8956])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.496288299560547 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.3226, 0.6718, 0.8788,  ..., 0.8065, 0.0580, 0.8956])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.496288299560547 seconds
+
+[40.34, 38.95, 39.34, 39.15, 39.19, 39.6, 39.18, 39.07, 39.25, 38.94]
+[65.2]
+13.152318000793457
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30504, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.496288299560547, 'TIME_S_1KI': 0.34409547271048213, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5311336517334, 'W': 65.2}
+[40.34, 38.95, 39.34, 39.15, 39.19, 39.6, 39.18, 39.07, 39.25, 38.94, 40.76, 39.32, 39.14, 39.04, 38.96, 38.91, 39.05, 44.03, 39.47, 38.87]
+711.105
+35.55525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30504, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.496288299560547, 'TIME_S_1KI': 0.34409547271048213, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5311336517334, 'W': 65.2, 'J_1KI': 28.112088042608622, 'W_1KI': 2.1374246000524524, 'W_D': 29.644750000000002, 'J_D': 389.8971790540219, 'W_D_1KI': 0.9718315630736953, 'J_D_1KI': 0.03185915168744084}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..b5a6d38
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30237, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.500168800354004, "TIME_S_1KI": 0.34726225486503304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.1604746675492, "W": 65.26, "J_1KI": 28.34806610006116, "W_1KI": 2.1582828984356914, "W_D": 29.86950000000001, "J_D": 392.3223229862453, "W_D_1KI": 0.9878460164698883, "J_D_1KI": 0.032670106706018734}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..5bbb6c5
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.04454636573791504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7341, 0.3777, 0.6942,  ..., 0.5169, 0.0997, 0.0147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.04454636573791504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23570', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 8.184704303741455}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.5580, 0.0255, 0.1246,  ..., 0.1598, 0.8729, 0.3337])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 8.184704303741455 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30237', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.500168800354004}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.2420, 0.6857, 0.8364,  ..., 0.8458, 0.0047, 0.6450])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.500168800354004 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.2420, 0.6857, 0.8364,  ..., 0.8458, 0.0047, 0.6450])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.500168800354004 seconds
+
+[40.88, 39.7, 39.32, 38.97, 39.02, 38.99, 39.0, 38.96, 38.99, 39.43]
+[65.26]
+13.134546041488647
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30237, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.500168800354004, 'TIME_S_1KI': 0.34726225486503304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1604746675492, 'W': 65.26}
+[40.88, 39.7, 39.32, 38.97, 39.02, 38.99, 39.0, 38.96, 38.99, 39.43, 42.02, 39.37, 39.48, 38.85, 39.01, 39.6, 39.28, 38.9, 39.76, 38.89]
+707.81
+35.390499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30237, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.500168800354004, 'TIME_S_1KI': 0.34726225486503304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1604746675492, 'W': 65.26, 'J_1KI': 28.34806610006116, 'W_1KI': 2.1582828984356914, 'W_D': 29.86950000000001, 'J_D': 392.3223229862453, 'W_D_1KI': 0.9878460164698883, 'J_D_1KI': 0.032670106706018734}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..328b912
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30130, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.474882364273071, "TIME_S_1KI": 0.34765623512356697, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 860.819589614868, "W": 65.6, "J_1KI": 28.570182197639166, "W_1KI": 2.1772319946896777, "W_D": 30.3315, "J_D": 398.01752107322216, "W_D_1KI": 1.0066876866910055, "J_D_1KI": 0.03341147317261883}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..1ff1b95
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.0447077751159668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.9325, 0.1379, 0.5007,  ..., 0.7095, 0.1075, 0.4424])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.0447077751159668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23485', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.184077501296997}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7524, 0.5484, 0.5700,  ..., 0.5640, 0.9906, 0.2544])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.184077501296997 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30130', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.474882364273071}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.8672, 0.4513, 0.7533,  ..., 0.2006, 0.9909, 0.4519])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.474882364273071 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.8672, 0.4513, 0.7533,  ..., 0.2006, 0.9909, 0.4519])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.474882364273071 seconds
+
+[39.98, 38.82, 38.95, 39.24, 38.92, 38.83, 40.77, 39.05, 39.0, 38.81]
+[65.6]
+13.122249841690063
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30130, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.474882364273071, 'TIME_S_1KI': 0.34765623512356697, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 860.819589614868, 'W': 65.6}
+[39.98, 38.82, 38.95, 39.24, 38.92, 38.83, 40.77, 39.05, 39.0, 38.81, 39.66, 39.19, 39.47, 39.32, 39.25, 39.31, 38.91, 38.82, 38.91, 38.77]
+705.3699999999999
+35.268499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30130, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.474882364273071, 'TIME_S_1KI': 0.34765623512356697, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 860.819589614868, 'W': 65.6, 'J_1KI': 28.570182197639166, 'W_1KI': 2.1772319946896777, 'W_D': 30.3315, 'J_D': 398.01752107322216, 'W_D_1KI': 1.0066876866910055, 'J_D_1KI': 0.03341147317261883}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..d27eaee
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 24585, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.531149864196777, "TIME_S_1KI": 0.4283567160543737, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 791.8324437737465, "W": 51.25, "J_1KI": 32.20794971623943, "W_1KI": 2.0846044335977223, "W_D": 34.32525, "J_D": 530.3384700613617, "W_D_1KI": 1.3961866992068332, "J_D_1KI": 0.05679018503993627}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..e758720
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.05796647071838379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.1404, 0.8835, 0.3196,  ..., 0.0994, 0.4882, 0.6406])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.05796647071838379 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18113', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.735689640045166}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5576, 0.2413, 0.8850,  ..., 0.5956, 0.8314, 0.8126])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 7.735689640045166 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '24585', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.531149864196777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5694, 0.0146, 0.7356,  ..., 0.9762, 0.7728, 0.4476])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.531149864196777 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5694, 0.0146, 0.7356,  ..., 0.9762, 0.7728, 0.4476])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.531149864196777 seconds
+
+[18.73, 18.22, 18.48, 18.73, 18.67, 18.5, 18.68, 18.44, 18.46, 18.66]
+[51.25]
+15.45038914680481
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 24585, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.531149864196777, 'TIME_S_1KI': 0.4283567160543737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.8324437737465, 'W': 51.25}
+[18.73, 18.22, 18.48, 18.73, 18.67, 18.5, 18.68, 18.44, 18.46, 18.66, 19.19, 18.59, 18.77, 18.6, 18.48, 18.55, 18.66, 18.93, 22.2, 18.49]
+338.495
+16.92475
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 24585, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.531149864196777, 'TIME_S_1KI': 0.4283567160543737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.8324437737465, 'W': 51.25, 'J_1KI': 32.20794971623943, 'W_1KI': 2.0846044335977223, 'W_D': 34.32525, 'J_D': 530.3384700613617, 'W_D_1KI': 1.3961866992068332, 'J_D_1KI': 0.05679018503993627}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..8dea0ce
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 23190, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.537458419799805, "TIME_S_1KI": 0.4543966545838639, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.656807744503, "W": 51.35, "J_1KI": 31.550530734993664, "W_1KI": 2.2143165157395432, "W_D": 34.42475, "J_D": 490.49859186762575, "W_D_1KI": 1.4844652867615353, "J_D_1KI": 0.06401316458652588}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..409bed8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,87 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.06488251686096191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5598, 0.0433, 0.9660,  ..., 0.7333, 0.3278, 0.3741])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.06488251686096191 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '16183', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 7.32713508605957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.9241, 0.2083, 0.8545,  ..., 0.8887, 0.7744, 0.4953])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 7.32713508605957 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '23190', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.537458419799805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([9.1482e-01, 8.3703e-04, 6.5969e-01,  ..., 4.1935e-01, 6.5973e-01,
+        2.2768e-03])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.537458419799805 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([9.1482e-01, 8.3703e-04, 6.5969e-01,  ..., 4.1935e-01, 6.5973e-01,
+        2.2768e-03])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.537458419799805 seconds
+
+[18.53, 18.62, 18.6, 18.47, 18.67, 18.83, 18.44, 18.95, 19.08, 18.42]
+[51.35]
+14.248428583145142
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 23190, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.537458419799805, 'TIME_S_1KI': 0.4543966545838639, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.656807744503, 'W': 51.35}
+[18.53, 18.62, 18.6, 18.47, 18.67, 18.83, 18.44, 18.95, 19.08, 18.42, 19.5, 19.11, 18.73, 19.2, 18.65, 18.87, 18.58, 18.81, 18.73, 19.88]
+338.505
+16.92525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 23190, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.537458419799805, 'TIME_S_1KI': 0.4543966545838639, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.656807744503, 'W': 51.35, 'J_1KI': 31.550530734993664, 'W_1KI': 2.2143165157395432, 'W_D': 34.42475, 'J_D': 490.49859186762575, 'W_D_1KI': 1.4844652867615353, 'J_D_1KI': 0.06401316458652588}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..d7993e2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22326, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.536637306213379, "TIME_S_1KI": 0.5167355238830682, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 786.4002640724183, "W": 51.46000000000001, "J_1KI": 35.22351805394689, "W_1KI": 2.304935949117621, "W_D": 34.605250000000005, "J_D": 528.8297267448903, "W_D_1KI": 1.5499977604586583, "J_D_1KI": 0.06942568128901991}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..1bc2ade
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.06119132041931152}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.5762, 0.9295, 0.5427,  ..., 0.4978, 0.3271, 0.2927])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.06119132041931152 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17159', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.069887161254883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.6269, 0.3774, 0.5755,  ..., 0.2711, 0.6777, 0.1164])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 8.069887161254883 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '22326', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.536637306213379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1977, 0.1619, 0.4572,  ..., 0.7258, 0.1988, 0.9324])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 11.536637306213379 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1977, 0.1619, 0.4572,  ..., 0.7258, 0.1988, 0.9324])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 11.536637306213379 seconds
+
+[19.37, 18.48, 18.69, 18.78, 18.91, 18.43, 18.91, 18.58, 19.73, 18.48]
+[51.46]
+15.281777381896973
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22326, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.536637306213379, 'TIME_S_1KI': 0.5167355238830682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.4002640724183, 'W': 51.46000000000001}
+[19.37, 18.48, 18.69, 18.78, 18.91, 18.43, 18.91, 18.58, 19.73, 18.48, 19.21, 18.86, 18.55, 18.48, 18.51, 18.54, 18.44, 18.39, 18.75, 19.07]
+337.095
+16.854750000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22326, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.536637306213379, 'TIME_S_1KI': 0.5167355238830682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.4002640724183, 'W': 51.46000000000001, 'J_1KI': 35.22351805394689, 'W_1KI': 2.304935949117621, 'W_D': 34.605250000000005, 'J_D': 528.8297267448903, 'W_D_1KI': 1.5499977604586583, 'J_D_1KI': 0.06942568128901991}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..eaf3d04
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21579, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.492535829544067, "TIME_S_1KI": 0.4862382793245316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.4754486441612, "W": 51.63, "J_1KI": 33.89756006507073, "W_1KI": 2.3926039204782428, "W_D": 34.75425, "J_D": 492.3858340314031, "W_D_1KI": 1.6105588766856664, "J_D_1KI": 0.07463547322330351}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..6fc5a5d
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_020.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.06222724914550781}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4797, 0.4440, 0.1471,  ..., 0.6101, 0.2731, 0.3399])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.06222724914550781 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '16873', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 8.209935903549194}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.9662, 0.2657, 0.6495,  ..., 0.1087, 0.7033, 0.7403])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 8.209935903549194 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '21579', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.492535829544067}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3951, 0.1073, 0.3279,  ..., 0.3499, 0.2789, 0.6338])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.492535829544067 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3951, 0.1073, 0.3279,  ..., 0.3499, 0.2789, 0.6338])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.492535829544067 seconds
+
+[19.29, 18.6, 18.57, 18.59, 19.05, 18.85, 18.65, 18.48, 18.9, 18.56]
+[51.63]
+14.167643785476685
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21579, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.492535829544067, 'TIME_S_1KI': 0.4862382793245316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.4754486441612, 'W': 51.63}
+[19.29, 18.6, 18.57, 18.59, 19.05, 18.85, 18.65, 18.48, 18.9, 18.56, 19.37, 18.75, 18.72, 19.48, 18.71, 18.42, 18.78, 18.58, 18.61, 18.33]
+337.515
+16.87575
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21579, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.492535829544067, 'TIME_S_1KI': 0.4862382793245316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.4754486441612, 'W': 51.63, 'J_1KI': 33.89756006507073, 'W_1KI': 2.3926039204782428, 'W_D': 34.75425, 'J_D': 492.3858340314031, 'W_D_1KI': 1.6105588766856664, 'J_D_1KI': 0.07463547322330351}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..b2b7d85
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18821, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.504743814468384, "TIME_S_1KI": 0.5581395151409799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 734.5338620448113, "W": 51.230000000000004, "J_1KI": 39.027355722055745, "W_1KI": 2.7219595133096015, "W_D": 34.108250000000005, "J_D": 489.042838182509, "W_D_1KI": 1.812244301578025, "J_D_1KI": 0.09628841727740423}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..90f0d29
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.0701456069946289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.9196, 0.4027, 0.9458,  ..., 0.2281, 0.7234, 0.8003])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.0701456069946289 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14968', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 8.350371360778809}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.8651, 0.5048, 0.3484,  ..., 0.4454, 0.2746, 0.9370])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 8.350371360778809 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18821', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.504743814468384}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.6340, 0.0545, 0.8586,  ..., 0.8211, 0.3677, 0.7072])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.504743814468384 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.6340, 0.0545, 0.8586,  ..., 0.8211, 0.3677, 0.7072])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.504743814468384 seconds
+
+[19.75, 18.72, 18.91, 18.56, 18.68, 18.41, 18.8, 18.57, 19.13, 22.25]
+[51.23]
+14.337963342666626
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.504743814468384, 'TIME_S_1KI': 0.5581395151409799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.5338620448113, 'W': 51.230000000000004}
+[19.75, 18.72, 18.91, 18.56, 18.68, 18.41, 18.8, 18.57, 19.13, 22.25, 19.1, 18.76, 18.51, 18.55, 18.62, 22.18, 18.61, 18.88, 18.75, 18.49]
+342.435
+17.12175
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.504743814468384, 'TIME_S_1KI': 0.5581395151409799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.5338620448113, 'W': 51.230000000000004, 'J_1KI': 39.027355722055745, 'W_1KI': 2.7219595133096015, 'W_D': 34.108250000000005, 'J_D': 489.042838182509, 'W_D_1KI': 1.812244301578025, 'J_D_1KI': 0.09628841727740423}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..93f1b9c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20501, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 11.608879327774048, "TIME_S_1KI": 0.5662591740780473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.1999763011933, "W": 51.4, "J_1KI": 35.764107911867384, "W_1KI": 2.5071947709867812, "W_D": 34.42375, "J_D": 491.04071370035405, "W_D_1KI": 1.6791254085166576, "J_D_1KI": 0.08190456116856044}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..2e63609
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_030.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.06486272811889648}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.5699, 0.5706, 0.6477,  ..., 0.2861, 0.8239, 0.5110])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.06486272811889648 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '16188', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 8.290924310684204}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8509, 0.1048, 0.3174,  ..., 0.0533, 0.9086, 0.6632])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 8.290924310684204 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20501', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 11.608879327774048}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.1826, 0.0803, 0.6528,  ..., 0.4920, 0.3584, 0.2754])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 11.608879327774048 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.1826, 0.0803, 0.6528,  ..., 0.4920, 0.3584, 0.2754])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 11.608879327774048 seconds
+
+[19.02, 18.94, 18.79, 18.86, 18.52, 18.61, 18.64, 18.54, 18.59, 18.64]
+[51.4]
+14.264590978622437
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20501, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 11.608879327774048, 'TIME_S_1KI': 0.5662591740780473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.1999763011933, 'W': 51.4}
+[19.02, 18.94, 18.79, 18.86, 18.52, 18.61, 18.64, 18.54, 18.59, 18.64, 18.73, 18.37, 18.4, 18.59, 18.35, 18.4, 22.96, 18.94, 18.58, 18.5]
+339.525
+16.97625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20501, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 11.608879327774048, 'TIME_S_1KI': 0.5662591740780473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.1999763011933, 'W': 51.4, 'J_1KI': 35.764107911867384, 'W_1KI': 2.5071947709867812, 'W_D': 34.42375, 'J_D': 491.04071370035405, 'W_D_1KI': 1.6791254085166576, 'J_D_1KI': 0.08190456116856044}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..8823eba
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20462, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.523966312408447, "TIME_S_1KI": 0.5143175795332053, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 815.268922715187, "W": 51.17, "J_1KI": 39.84307119124167, "W_1KI": 2.5007330661714398, "W_D": 33.975, "J_D": 541.3086114764213, "W_D_1KI": 1.6603948783110156, "J_D_1KI": 0.0811452877681075}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..75c581b
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.07111167907714844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4364, 0.4295, 0.9701,  ..., 0.8211, 0.4909, 0.8334])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.07111167907714844 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14765', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.576246500015259}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4654, 0.9230, 0.1647,  ..., 0.9259, 0.4832, 0.5316])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 7.576246500015259 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20462', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.523966312408447}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.9802, 0.9301, 0.1785,  ..., 0.2797, 0.8123, 0.6307])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.523966312408447 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.9802, 0.9301, 0.1785,  ..., 0.2797, 0.8123, 0.6307])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.523966312408447 seconds
+
+[19.45, 18.69, 18.94, 18.43, 18.57, 18.71, 18.77, 18.49, 18.55, 22.44]
+[51.17]
+15.932556629180908
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20462, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.523966312408447, 'TIME_S_1KI': 0.5143175795332053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.268922715187, 'W': 51.17}
+[19.45, 18.69, 18.94, 18.43, 18.57, 18.71, 18.77, 18.49, 18.55, 22.44, 18.92, 18.51, 18.96, 23.11, 18.62, 18.6, 19.39, 18.51, 19.19, 18.91]
+343.90000000000003
+17.195
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20462, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.523966312408447, 'TIME_S_1KI': 0.5143175795332053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.268922715187, 'W': 51.17, 'J_1KI': 39.84307119124167, 'W_1KI': 2.5007330661714398, 'W_D': 33.975, 'J_D': 541.3086114764213, 'W_D_1KI': 1.6603948783110156, 'J_D_1KI': 0.0811452877681075}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..25fce68
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20021, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.447348594665527, "TIME_S_1KI": 0.5218195192380763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.0454747200013, "W": 51.6, "J_1KI": 36.613829215323975, "W_1KI": 2.57729384146646, "W_D": 34.59825000000001, "J_D": 491.51338363820327, "W_D_1KI": 1.7280979971030421, "J_D_1KI": 0.08631426987178673}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..d47439e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_040.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.0667262077331543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.2770, 0.9807, 0.0023,  ..., 0.4354, 0.4042, 0.0979])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.0667262077331543 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15735', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 8.25218152999878}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.8158, 0.1580, 0.6476,  ..., 0.2432, 0.8617, 0.9259])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 8.25218152999878 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20021', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.447348594665527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7996, 0.7674, 0.8103,  ..., 0.4428, 0.7332, 0.2125])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.447348594665527 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7996, 0.7674, 0.8103,  ..., 0.4428, 0.7332, 0.2125])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.447348594665527 seconds
+
+[19.11, 18.37, 18.46, 22.11, 19.82, 18.41, 18.66, 18.64, 18.95, 18.5]
+[51.6]
+14.206307649612427
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20021, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.447348594665527, 'TIME_S_1KI': 0.5218195192380763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.0454747200013, 'W': 51.6}
+[19.11, 18.37, 18.46, 22.11, 19.82, 18.41, 18.66, 18.64, 18.95, 18.5, 18.94, 18.58, 19.04, 18.41, 18.63, 18.66, 18.83, 18.42, 18.41, 18.72]
+340.03499999999997
+17.001749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20021, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.447348594665527, 'TIME_S_1KI': 0.5218195192380763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.0454747200013, 'W': 51.6, 'J_1KI': 36.613829215323975, 'W_1KI': 2.57729384146646, 'W_D': 34.59825000000001, 'J_D': 491.51338363820327, 'W_D_1KI': 1.7280979971030421, 'J_D_1KI': 0.08631426987178673}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..076f312
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20010, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.59175419807434, "TIME_S_1KI": 0.5792980608732804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.6207900404929, "W": 51.54999999999999, "J_1KI": 36.61273313545692, "W_1KI": 2.576211894052973, "W_D": 34.619249999999994, "J_D": 492.00353609329454, "W_D_1KI": 1.7300974512743625, "J_D_1KI": 0.0864616417428467}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..641af44
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.06652593612670898}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6643, 0.8543, 0.2565,  ..., 0.9055, 0.9867, 0.8474])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.06652593612670898 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15783', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.281726121902466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.0337, 0.0275, 0.6347,  ..., 0.6984, 0.9483, 0.5691])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 8.281726121902466 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20010', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.59175419807434}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.8084, 0.5325, 0.2590,  ..., 0.3310, 0.8155, 0.3301])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.59175419807434 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.8084, 0.5325, 0.2590,  ..., 0.3310, 0.8155, 0.3301])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.59175419807434 seconds
+
+[19.13, 18.4, 18.97, 18.7, 19.22, 18.41, 18.83, 18.48, 18.52, 18.45]
+[51.55]
+14.211848497390747
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20010, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.59175419807434, 'TIME_S_1KI': 0.5792980608732804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.6207900404929, 'W': 51.54999999999999}
+[19.13, 18.4, 18.97, 18.7, 19.22, 18.41, 18.83, 18.48, 18.52, 18.45, 19.53, 18.8, 18.54, 18.5, 19.03, 18.53, 18.43, 18.45, 18.93, 22.64]
+338.615
+16.93075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20010, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.59175419807434, 'TIME_S_1KI': 0.5792980608732804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.6207900404929, 'W': 51.54999999999999, 'J_1KI': 36.61273313545692, 'W_1KI': 2.576211894052973, 'W_D': 34.619249999999994, 'J_D': 492.00353609329454, 'W_D_1KI': 1.7300974512743625, 'J_D_1KI': 0.0864616417428467}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..1f9e9dc
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19766, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 11.618280410766602, "TIME_S_1KI": 0.5877911773128909, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 795.2748688459396, "W": 51.5, "J_1KI": 40.234486939489, "W_1KI": 2.6054841647273093, "W_D": 34.605000000000004, "J_D": 534.3783851730824, "W_D_1KI": 1.750733582920166, "J_D_1KI": 0.08857298304766599}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..844dd0f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.07622480392456055}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.9152, 0.6485, 0.8678,  ..., 0.6295, 0.8712, 0.4589])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.07622480392456055 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13775', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 8.094318866729736}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.4898, 0.7114, 0.1163,  ..., 0.8851, 0.7433, 0.1518])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 8.094318866729736 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17869', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 9.491950750350952}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.7810, 0.8703, 0.0083,  ..., 0.3969, 0.4254, 0.1142])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 9.491950750350952 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19766', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 11.618280410766602}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8341, 0.5523, 0.5858,  ..., 0.1560, 0.1140, 0.3229])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 11.618280410766602 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8341, 0.5523, 0.5858,  ..., 0.1560, 0.1140, 0.3229])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 11.618280410766602 seconds
+
+[19.38, 18.4, 18.83, 19.57, 18.58, 18.52, 18.89, 18.53, 18.57, 18.43]
+[51.5]
+15.442230463027954
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19766, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 11.618280410766602, 'TIME_S_1KI': 0.5877911773128909, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 795.2748688459396, 'W': 51.5}
+[19.38, 18.4, 18.83, 19.57, 18.58, 18.52, 18.89, 18.53, 18.57, 18.43, 19.16, 18.56, 18.52, 19.7, 18.48, 18.43, 18.69, 18.66, 18.96, 19.05]
+337.9
+16.895
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19766, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 11.618280410766602, 'TIME_S_1KI': 0.5877911773128909, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 795.2748688459396, 'W': 51.5, 'J_1KI': 40.234486939489, 'W_1KI': 2.6054841647273093, 'W_D': 34.605000000000004, 'J_D': 534.3783851730824, 'W_D_1KI': 1.750733582920166, 'J_D_1KI': 0.08857298304766599}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..48cbf8f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19548, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.464083194732666, "TIME_S_1KI": 0.5353019845883296, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.7007483005524, "W": 51.11, "J_1KI": 37.48213363518275, "W_1KI": 2.6145897278493964, "W_D": 33.930499999999995, "J_D": 486.4195409941673, "W_D_1KI": 1.7357530182115817, "J_D_1KI": 0.08879440445117565}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..badbc0b
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.07336711883544922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.0799, 0.9526, 0.9637,  ..., 0.9474, 0.7458, 0.3085])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.07336711883544922 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14311', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.686976194381714}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.8753, 0.6638, 0.8748,  ..., 0.2712, 0.9678, 0.3069])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 7.686976194381714 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19548', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.464083194732666}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.4610, 0.8810, 0.6667,  ..., 0.9302, 0.2771, 0.3767])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.464083194732666 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.4610, 0.8810, 0.6667,  ..., 0.9302, 0.2771, 0.3767])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.464083194732666 seconds
+
+[19.14, 18.52, 19.06, 18.57, 18.57, 18.64, 19.64, 22.11, 19.05, 18.43]
+[51.11]
+14.335761070251465
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19548, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.464083194732666, 'TIME_S_1KI': 0.5353019845883296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.7007483005524, 'W': 51.11}
+[19.14, 18.52, 19.06, 18.57, 18.57, 18.64, 19.64, 22.11, 19.05, 18.43, 19.35, 18.43, 18.57, 21.26, 18.97, 18.59, 18.67, 18.39, 18.78, 18.62]
+343.59000000000003
+17.1795
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19548, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.464083194732666, 'TIME_S_1KI': 0.5353019845883296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.7007483005524, 'W': 51.11, 'J_1KI': 37.48213363518275, 'W_1KI': 2.6145897278493964, 'W_D': 33.930499999999995, 'J_D': 486.4195409941673, 'W_D_1KI': 1.7357530182115817, 'J_D_1KI': 0.08879440445117565}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..dbca710
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19312, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.480382204055786, "TIME_S_1KI": 0.5426875623475449, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 788.9869772815705, "W": 51.42, "J_1KI": 40.8547523447375, "W_1KI": 2.662593206296603, "W_D": 32.432, "J_D": 497.635660194397, "W_D_1KI": 1.6793703396851698, "J_D_1KI": 0.08695993888179214}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..ad43421
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_060.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.07373046875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.4758, 0.7579, 0.8307,  ..., 0.1866, 0.4952, 0.9867])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.07373046875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14241', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 7.742620468139648}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.8931, 0.4680, 0.6788,  ..., 0.6336, 0.2060, 0.1475])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 7.742620468139648 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19312', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.480382204055786}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.0094, 0.6659, 0.8757,  ..., 0.2191, 0.4218, 0.3276])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.480382204055786 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.0094, 0.6659, 0.8757,  ..., 0.2191, 0.4218, 0.3276])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.480382204055786 seconds
+
+[43.6, 39.37, 26.79, 18.58, 19.25, 18.92, 18.98, 18.85, 18.98, 19.3]
+[51.42]
+15.343970775604248
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19312, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.480382204055786, 'TIME_S_1KI': 0.5426875623475449, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.9869772815705, 'W': 51.42}
+[43.6, 39.37, 26.79, 18.58, 19.25, 18.92, 18.98, 18.85, 18.98, 19.3, 18.85, 19.14, 18.69, 18.52, 18.5, 18.67, 18.73, 18.62, 18.76, 19.07]
+379.76
+18.988
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19312, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.480382204055786, 'TIME_S_1KI': 0.5426875623475449, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.9869772815705, 'W': 51.42, 'J_1KI': 40.8547523447375, 'W_1KI': 2.662593206296603, 'W_D': 32.432, 'J_D': 497.635660194397, 'W_D_1KI': 1.6793703396851698, 'J_D_1KI': 0.08695993888179214}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..9daa366
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19229, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.47281002998352, "TIME_S_1KI": 0.5446362280921275, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.5914888072015, "W": 51.27, "J_1KI": 38.25427681144112, "W_1KI": 2.666285298247439, "W_D": 34.261500000000005, "J_D": 491.5636394337416, "W_D_1KI": 1.781761922096833, "J_D_1KI": 0.0926601446823461}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..72ee088
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.06865715980529785}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.4901, 0.0666, 0.4753,  ..., 0.5898, 0.7621, 0.4426])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.06865715980529785 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15293', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 9.309351921081543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9797, 0.1862, 0.4685,  ..., 0.3877, 0.4753, 0.7598])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 9.309351921081543 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17248', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 9.417802572250366}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2025, 0.5486, 0.8359,  ..., 0.6633, 0.0587, 0.9608])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 9.417802572250366 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19229', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.47281002998352}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9709, 0.1511, 0.4432,  ..., 0.8939, 0.4938, 0.1652])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.47281002998352 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.9709, 0.1511, 0.4432,  ..., 0.8939, 0.4938, 0.1652])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.47281002998352 seconds
+
+[18.91, 22.63, 19.24, 18.52, 18.84, 18.59, 18.72, 18.36, 18.85, 18.37]
+[51.27]
+14.347405672073364
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19229, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.47281002998352, 'TIME_S_1KI': 0.5446362280921275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.5914888072015, 'W': 51.27}
+[18.91, 22.63, 19.24, 18.52, 18.84, 18.59, 18.72, 18.36, 18.85, 18.37, 19.24, 18.47, 18.46, 18.62, 19.16, 18.39, 18.45, 18.47, 19.01, 18.26]
+340.16999999999996
+17.008499999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19229, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.47281002998352, 'TIME_S_1KI': 0.5446362280921275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.5914888072015, 'W': 51.27, 'J_1KI': 38.25427681144112, 'W_1KI': 2.666285298247439, 'W_D': 34.261500000000005, 'J_D': 491.5636394337416, 'W_D_1KI': 1.781761922096833, 'J_D_1KI': 0.0926601446823461}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..ca75c17
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20682, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.486200332641602, "TIME_S_1KI": 0.5070206137047483, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.8839039802551, "W": 51.1, "J_1KI": 35.38748206074147, "W_1KI": 2.470747509912001, "W_D": 33.986000000000004, "J_D": 486.7672477626801, "W_D_1KI": 1.6432646745962676, "J_D_1KI": 0.07945385719931668}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..b016c60
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_070.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.0700075626373291}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.7625, 0.7383, 0.3888,  ..., 0.6449, 0.9530, 0.4527])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.0700075626373291 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14998', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 7.614161968231201}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.7679, 0.4113, 0.2356,  ..., 0.7826, 0.3637, 0.7058])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 7.614161968231201 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20682', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.486200332641602}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.6593, 0.3884, 0.8656,  ..., 0.8738, 0.1469, 0.2763])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.486200332641602 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.6593, 0.3884, 0.8656,  ..., 0.8738, 0.1469, 0.2763])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.486200332641602 seconds
+
+[19.0, 22.58, 18.61, 18.63, 18.92, 18.53, 18.58, 18.8, 18.93, 18.49]
+[51.1]
+14.32258129119873
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20682, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.486200332641602, 'TIME_S_1KI': 0.5070206137047483, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8839039802551, 'W': 51.1}
+[19.0, 22.58, 18.61, 18.63, 18.92, 18.53, 18.58, 18.8, 18.93, 18.49, 19.2, 18.85, 18.79, 18.93, 18.84, 18.74, 19.8, 18.36, 18.63, 18.83]
+342.28
+17.113999999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20682, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.486200332641602, 'TIME_S_1KI': 0.5070206137047483, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8839039802551, 'W': 51.1, 'J_1KI': 35.38748206074147, 'W_1KI': 2.470747509912001, 'W_D': 33.986000000000004, 'J_D': 486.7672477626801, 'W_D_1KI': 1.6432646745962676, 'J_D_1KI': 0.07945385719931668}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..b670e50
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18895, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.52974534034729, "TIME_S_1KI": 0.557276810814887, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.8454258918762, "W": 51.4, "J_1KI": 38.73222682677302, "W_1KI": 2.7202963747023023, "W_D": 34.51575, "J_D": 491.44345834100244, "W_D_1KI": 1.8267134162476844, "J_D_1KI": 0.09667707945211348}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..4542b1a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.06986713409423828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.6412, 0.9170, 0.6451,  ..., 0.6226, 0.2917, 0.6640])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.06986713409423828 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15028', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 8.351078987121582}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.1989, 0.7767, 0.9282,  ..., 0.6106, 0.4662, 0.7304])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 8.351078987121582 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18895', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.52974534034729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2342, 0.7865, 0.1763,  ..., 0.6603, 0.8473, 0.6206])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.52974534034729 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.2342, 0.7865, 0.1763,  ..., 0.6603, 0.8473, 0.6206])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.52974534034729 seconds
+
+[19.04, 18.64, 18.67, 18.52, 18.72, 19.12, 19.11, 18.73, 18.51, 19.11]
+[51.4]
+14.238237857818604
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18895, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.52974534034729, 'TIME_S_1KI': 0.557276810814887, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8454258918762, 'W': 51.4}
+[19.04, 18.64, 18.67, 18.52, 18.72, 19.12, 19.11, 18.73, 18.51, 19.11, 19.23, 18.93, 18.61, 19.01, 18.59, 18.49, 18.73, 18.71, 18.62, 18.57]
+337.685
+16.88425
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18895, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.52974534034729, 'TIME_S_1KI': 0.557276810814887, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8454258918762, 'W': 51.4, 'J_1KI': 38.73222682677302, 'W_1KI': 2.7202963747023023, 'W_D': 34.51575, 'J_D': 491.44345834100244, 'W_D_1KI': 1.8267134162476844, 'J_D_1KI': 0.09667707945211348}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..ed20bec
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18783, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.479514598846436, "TIME_S_1KI": 0.5579254963981491, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 737.4236928224564, "W": 51.19, "J_1KI": 39.26016572552076, "W_1KI": 2.7253367406697544, "W_D": 34.19025, "J_D": 492.53175255954267, "W_D_1KI": 1.8202763136879092, "J_D_1KI": 0.09691084031772929}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..81ff940
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_080.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.07011771202087402}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5918, 0.6489, 0.1637,  ..., 0.8691, 0.6194, 0.9067])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.07011771202087402 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14974', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 8.370388984680176}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1211, 0.1666, 0.6833,  ..., 0.8657, 0.4619, 0.6942])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 8.370388984680176 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18783', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.479514598846436}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.7543, 0.5555, 0.7525,  ..., 0.8398, 0.5600, 0.9119])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.479514598846436 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.7543, 0.5555, 0.7525,  ..., 0.8398, 0.5600, 0.9119])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.479514598846436 seconds
+
+[18.8, 18.76, 18.51, 18.35, 18.77, 18.58, 18.54, 18.33, 18.56, 19.0]
+[51.19]
+14.405620098114014
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18783, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.479514598846436, 'TIME_S_1KI': 0.5579254963981491, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.4236928224564, 'W': 51.19}
+[18.8, 18.76, 18.51, 18.35, 18.77, 18.58, 18.54, 18.33, 18.56, 19.0, 18.92, 18.38, 22.77, 18.61, 18.89, 18.77, 19.01, 18.83, 18.59, 18.77]
+339.995
+16.99975
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18783, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.479514598846436, 'TIME_S_1KI': 0.5579254963981491, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.4236928224564, 'W': 51.19, 'J_1KI': 39.26016572552076, 'W_1KI': 2.7253367406697544, 'W_D': 34.19025, 'J_D': 492.53175255954267, 'W_D_1KI': 1.8202763136879092, 'J_D_1KI': 0.09691084031772929}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..3ea5426
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18645, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.518952131271362, "TIME_S_1KI": 0.5641701330797191, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 737.6119203472138, "W": 51.34, "J_1KI": 39.56084314010264, "W_1KI": 2.7535532314293376, "W_D": 34.097500000000004, "J_D": 489.8855172193051, "W_D_1KI": 1.828774470367391, "J_D_1KI": 0.09808390830610839}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..9d8d636
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.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', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.0699915885925293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7446, 0.2717, 0.8801,  ..., 0.1140, 0.4332, 0.2882])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.0699915885925293 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15001', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.447453022003174}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7520, 0.7891, 0.1920,  ..., 0.3124, 0.0972, 0.0468])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 8.447453022003174 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18645', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.518952131271362}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3323, 0.6853, 0.9554,  ..., 0.7735, 0.0729, 0.3877])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.518952131271362 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3323, 0.6853, 0.9554,  ..., 0.7735, 0.0729, 0.3877])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.518952131271362 seconds
+
+[18.83, 18.36, 18.45, 18.83, 18.31, 18.39, 23.19, 18.95, 18.51, 18.47]
+[51.34]
+14.367197513580322
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.518952131271362, 'TIME_S_1KI': 0.5641701330797191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.6119203472138, 'W': 51.34}
+[18.83, 18.36, 18.45, 18.83, 18.31, 18.39, 23.19, 18.95, 18.51, 18.47, 19.38, 21.16, 20.25, 18.57, 19.26, 18.53, 18.9, 18.58, 19.02, 18.5]
+344.84999999999997
+17.2425
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.518952131271362, 'TIME_S_1KI': 0.5641701330797191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.6119203472138, 'W': 51.34, 'J_1KI': 39.56084314010264, 'W_1KI': 2.7535532314293376, 'W_D': 34.097500000000004, 'J_D': 489.8855172193051, 'W_D_1KI': 1.828774470367391, 'J_D_1KI': 0.09808390830610839}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..fcbac3a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.417639970779419, "TIME_S_1KI": 0.5693010531056024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.991283402443, "W": 51.62, "J_1KI": 40.11100515888535, "W_1KI": 2.820919175911252, "W_D": 25.4165, "J_D": 361.4004156256914, "W_D_1KI": 1.3889556806382863, "J_D_1KI": 0.07590336524609466}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..9650440
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.0713958740234375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4631, 0.0681, 0.9688,  ..., 0.8966, 0.1214, 0.8353])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.0713958740234375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14706', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 8.438250064849854}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.3743, 0.7144, 0.4740,  ..., 0.0301, 0.7452, 0.2311])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 8.438250064849854 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18299', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.417639970779419}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.2486, 0.9533, 0.3057,  ..., 0.2400, 0.4728, 0.6103])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.417639970779419 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.2486, 0.9533, 0.3057,  ..., 0.2400, 0.4728, 0.6103])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.417639970779419 seconds
+
+[19.22, 18.56, 18.54, 18.45, 18.6, 18.63, 18.52, 19.09, 19.14, 18.38]
+[51.62]
+14.219125986099243
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.417639970779419, 'TIME_S_1KI': 0.5693010531056024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.991283402443, 'W': 51.62}
+[19.22, 18.56, 18.54, 18.45, 18.6, 18.63, 18.52, 19.09, 19.14, 18.38, 40.5, 47.58, 46.41, 42.34, 42.44, 25.3, 25.65, 41.74, 42.88, 42.3]
+524.0699999999999
+26.2035
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.417639970779419, 'TIME_S_1KI': 0.5693010531056024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.991283402443, 'W': 51.62, 'J_1KI': 40.11100515888535, 'W_1KI': 2.820919175911252, 'W_D': 25.4165, 'J_D': 361.4004156256914, 'W_D_1KI': 1.3889556806382863, 'J_D_1KI': 0.07590336524609466}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..3926144
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18160, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.447108745574951, "TIME_S_1KI": 0.5752813185889291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 728.1026530575753, "W": 51.47, "J_1KI": 40.09375842828058, "W_1KI": 2.834251101321586, "W_D": 34.3515, "J_D": 485.94168032848836, "W_D_1KI": 1.891602422907489, "J_D_1KI": 0.10416312901472957}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..c40d387
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.07135295867919922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2816, 0.0519, 0.4526,  ..., 0.4658, 0.0063, 0.4440])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.07135295867919922 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14715', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.507978677749634}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2864, 0.5383, 0.4718,  ..., 0.4578, 0.9727, 0.9566])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 8.507978677749634 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18160', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.447108745574951}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.7032, 0.3468, 0.7464,  ..., 0.5468, 0.4215, 0.7103])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.447108745574951 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.7032, 0.3468, 0.7464,  ..., 0.5468, 0.4215, 0.7103])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.447108745574951 seconds
+
+[19.54, 18.52, 18.53, 18.56, 18.7, 18.39, 18.57, 19.3, 18.89, 22.85]
+[51.47]
+14.146156072616577
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.447108745574951, 'TIME_S_1KI': 0.5752813185889291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 728.1026530575753, 'W': 51.47}
+[19.54, 18.52, 18.53, 18.56, 18.7, 18.39, 18.57, 19.3, 18.89, 22.85, 18.87, 18.66, 18.64, 18.39, 22.24, 19.23, 18.56, 18.41, 18.82, 18.66]
+342.37
+17.1185
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.447108745574951, 'TIME_S_1KI': 0.5752813185889291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 728.1026530575753, 'W': 51.47, 'J_1KI': 40.09375842828058, 'W_1KI': 2.834251101321586, 'W_D': 34.3515, 'J_D': 485.94168032848836, 'W_D_1KI': 1.891602422907489, 'J_D_1KI': 0.10416312901472957}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..25b817c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18222, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.587164402008057, "TIME_S_1KI": 0.5810100099883688, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 739.9078921508789, "W": 51.459999999999994, "J_1KI": 40.605196583848034, "W_1KI": 2.824058829985731, "W_D": 34.51474999999999, "J_D": 496.2638150138854, "W_D_1KI": 1.8941252332345513, "J_D_1KI": 0.10394716459414725}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..6bc6704
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.07163095474243164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2728, 0.8364, 0.7916,  ..., 0.5200, 0.7565, 0.9514])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.07163095474243164 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14658', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 8.446149110794067}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.9957, 0.6502, 0.5083,  ..., 0.3303, 0.6525, 0.3359])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 8.446149110794067 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18222', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.587164402008057}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.9738, 0.2454, 0.0495,  ..., 0.3411, 0.1063, 0.1509])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.587164402008057 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.9738, 0.2454, 0.0495,  ..., 0.3411, 0.1063, 0.1509])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.587164402008057 seconds
+
+[19.12, 18.7, 18.91, 18.88, 19.55, 18.7, 18.66, 19.0, 18.98, 18.44]
+[51.46]
+14.378311157226562
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18222, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.587164402008057, 'TIME_S_1KI': 0.5810100099883688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 739.9078921508789, 'W': 51.459999999999994}
+[19.12, 18.7, 18.91, 18.88, 19.55, 18.7, 18.66, 19.0, 18.98, 18.44, 19.08, 18.9, 18.59, 18.5, 18.71, 18.67, 19.52, 18.37, 18.59, 18.71]
+338.905
+16.945249999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18222, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.587164402008057, 'TIME_S_1KI': 0.5810100099883688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 739.9078921508789, 'W': 51.459999999999994, 'J_1KI': 40.605196583848034, 'W_1KI': 2.824058829985731, 'W_D': 34.51474999999999, 'J_D': 496.2638150138854, 'W_D_1KI': 1.8941252332345513, 'J_D_1KI': 0.10394716459414725}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..ef2ea78
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17816, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.415440320968628, "TIME_S_1KI": 0.5846116031078036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.0956484270096, "W": 51.53, "J_1KI": 41.31654964228837, "W_1KI": 2.8923439604849577, "W_D": 34.6645, "J_D": 495.17538530755036, "W_D_1KI": 1.9456948810058372, "J_D_1KI": 0.109210534407602}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..8d2a378
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.07289290428161621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.2212, 0.1777, 0.5151,  ..., 0.3390, 0.7939, 0.3077])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.07289290428161621 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14404', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.488957166671753}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.1035, 0.0189, 0.8464,  ..., 0.3615, 0.8119, 0.4494])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 8.488957166671753 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17816', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.415440320968628}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8605, 0.5482, 0.2234,  ..., 0.4827, 0.0878, 0.0084])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.415440320968628 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8605, 0.5482, 0.2234,  ..., 0.4827, 0.0878, 0.0084])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.415440320968628 seconds
+
+[19.37, 18.59, 18.91, 18.57, 18.46, 19.18, 18.55, 18.37, 18.39, 18.4]
+[51.53]
+14.28479814529419
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17816, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.415440320968628, 'TIME_S_1KI': 0.5846116031078036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.0956484270096, 'W': 51.53}
+[19.37, 18.59, 18.91, 18.57, 18.46, 19.18, 18.55, 18.37, 18.39, 18.4, 19.0, 18.46, 18.84, 18.52, 18.63, 19.09, 19.6, 18.71, 18.79, 18.53]
+337.31000000000006
+16.865500000000004
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17816, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.415440320968628, 'TIME_S_1KI': 0.5846116031078036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.0956484270096, 'W': 51.53, 'J_1KI': 41.31654964228837, 'W_1KI': 2.8923439604849577, 'W_D': 34.6645, 'J_D': 495.17538530755036, 'W_D_1KI': 1.9456948810058372, 'J_D_1KI': 0.109210534407602}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..2d72482
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17863, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.454811573028564, "TIME_S_1KI": 0.5852774770771183, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.3536998057365, "W": 51.37, "J_1KI": 41.05434136515347, "W_1KI": 2.875776745227565, "W_D": 34.405, "J_D": 491.1628195798397, "W_D_1KI": 1.9260482561719756, "J_D_1KI": 0.10782333629132708}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..b0a6cd6
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.07201600074768066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.8335, 0.7948, 0.4180,  ..., 0.5903, 0.3154, 0.7999])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.07201600074768066 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14580', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 8.569825887680054}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.7932, 0.8472, 0.4524,  ..., 0.8166, 0.8223, 0.8063])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 8.569825887680054 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17863', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.454811573028564}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5762, 0.2685, 0.2631,  ..., 0.8563, 0.3856, 0.9821])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.454811573028564 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5762, 0.2685, 0.2631,  ..., 0.8563, 0.3856, 0.9821])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.454811573028564 seconds
+
+[19.05, 18.49, 18.9, 18.43, 19.02, 18.5, 18.54, 19.85, 18.89, 18.44]
+[51.37]
+14.275913953781128
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17863, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.454811573028564, 'TIME_S_1KI': 0.5852774770771183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.3536998057365, 'W': 51.37}
+[19.05, 18.49, 18.9, 18.43, 19.02, 18.5, 18.54, 19.85, 18.89, 18.44, 19.31, 18.67, 18.75, 18.78, 18.85, 18.75, 19.13, 18.59, 19.0, 19.52]
+339.3
+16.965
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17863, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.454811573028564, 'TIME_S_1KI': 0.5852774770771183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.3536998057365, 'W': 51.37, 'J_1KI': 41.05434136515347, 'W_1KI': 2.875776745227565, 'W_D': 34.405, 'J_D': 491.1628195798397, 'W_D_1KI': 1.9260482561719756, 'J_D_1KI': 0.10782333629132708}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..0480bc3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17784, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.485624551773071, "TIME_S_1KI": 0.5896100175310993, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 734.9326595020294, "W": 51.42, "J_1KI": 41.325498172628734, "W_1KI": 2.8913630229419707, "W_D": 34.413, "J_D": 491.8560406737327, "W_D_1KI": 1.9350539811066125, "J_D_1KI": 0.10880870339106008}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..588bc33
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.073089599609375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.4304, 0.3415, 0.8565,  ..., 0.5779, 0.8119, 0.1412])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.073089599609375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14365', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 8.481326341629028}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.0724, 0.0070, 0.1571,  ..., 0.0880, 0.9717, 0.4875])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 8.481326341629028 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17784', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.485624551773071}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.0693, 0.9266, 0.3660,  ..., 0.8825, 0.1759, 0.2255])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.485624551773071 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.0693, 0.9266, 0.3660,  ..., 0.8825, 0.1759, 0.2255])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.485624551773071 seconds
+
+[19.17, 18.45, 18.63, 18.57, 22.5, 18.51, 18.76, 18.65, 18.52, 18.6]
+[51.42]
+14.292739391326904
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17784, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.485624551773071, 'TIME_S_1KI': 0.5896100175310993, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.9326595020294, 'W': 51.42}
+[19.17, 18.45, 18.63, 18.57, 22.5, 18.51, 18.76, 18.65, 18.52, 18.6, 19.86, 18.33, 18.58, 18.45, 18.8, 18.42, 18.91, 18.37, 19.02, 19.71]
+340.14000000000004
+17.007
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17784, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.485624551773071, 'TIME_S_1KI': 0.5896100175310993, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.9326595020294, 'W': 51.42, 'J_1KI': 41.325498172628734, 'W_1KI': 2.8913630229419707, 'W_D': 34.413, 'J_D': 491.8560406737327, 'W_D_1KI': 1.9350539811066125, 'J_D_1KI': 0.10880870339106008}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..306383a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17677, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.463706016540527, "TIME_S_1KI": 0.5919390177372025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.3628653907776, "W": 51.34, "J_1KI": 41.430269015714074, "W_1KI": 2.9043389715449455, "W_D": 34.3515, "J_D": 490.0226523270607, "W_D_1KI": 1.9432878882163265, "J_D_1KI": 0.1099331271265671}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..4279122
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.07247591018676758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.6348, 0.3858, 0.6944,  ..., 0.5866, 0.5269, 0.2191])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.07247591018676758 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14487', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.604924440383911}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.6535, 0.1468, 0.3907,  ..., 0.6473, 0.9749, 0.1558])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.604924440383911 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17677', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.463706016540527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1947, 0.8107, 0.5182,  ..., 0.1414, 0.5757, 0.2348])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.463706016540527 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1947, 0.8107, 0.5182,  ..., 0.1414, 0.5757, 0.2348])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.463706016540527 seconds
+
+[18.9, 18.56, 18.81, 22.27, 18.6, 18.59, 18.82, 18.66, 18.72, 18.69]
+[51.34]
+14.2649564743042
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17677, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.463706016540527, 'TIME_S_1KI': 0.5919390177372025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.3628653907776, 'W': 51.34}
+[18.9, 18.56, 18.81, 22.27, 18.6, 18.59, 18.82, 18.66, 18.72, 18.69, 18.98, 18.36, 18.57, 18.47, 18.36, 18.84, 18.47, 18.54, 19.67, 18.35]
+339.77
+16.9885
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17677, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.463706016540527, 'TIME_S_1KI': 0.5919390177372025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.3628653907776, 'W': 51.34, 'J_1KI': 41.430269015714074, 'W_1KI': 2.9043389715449455, 'W_D': 34.3515, 'J_D': 490.0226523270607, 'W_D_1KI': 1.9432878882163265, 'J_D_1KI': 0.1099331271265671}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_005.json
similarity index 100%
rename from pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_005.json
rename to pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_005.json
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_005.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_010.json
similarity index 100%
rename from pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_010.json
rename to pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_010.json
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_010.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_015.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_015.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_020.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_020.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_025.json
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_025.output
similarity index 100%
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_030.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_030.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_035.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_035.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_040.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_040.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_045.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_050.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_050.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_055.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_055.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_060.json
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_060.output
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diff --git a/pytorch/output_as-caida_1core/altra_1_csr_20_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core_old/altra_1_csr_20_10_10_as-caida_G_065.json
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_20_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core_old/xeon_4216_1_csr_20_10_10_as-caida_G_120.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..83b3559
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5968, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.525087594985962, "TIME_S_1KI": 1.7635870635030098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 272.15640623092645, "W": 19.161924669821744, "J_1KI": 45.60261498507481, "W_1KI": 3.2107782623695953, "W_D": 4.226924669821745, "J_D": 60.03492067575449, "W_D_1KI": 0.708264857543858, "J_D_1KI": 0.11867708739005664}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output
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index 0000000..7a87255
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.23140215873718262}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.4043, 0.1456, 0.8788,  ..., 0.2899, 0.4254, 0.2185])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.23140215873718262 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4537 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.981162071228027}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.6074, 0.8880, 0.3045,  ..., 0.0454, 0.8927, 0.3776])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 7.981162071228027 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5968 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.525087594985962}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.9329, 0.1644, 0.2943,  ..., 0.4186, 0.6680, 0.2503])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.525087594985962 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.9329, 0.1644, 0.2943,  ..., 0.4186, 0.6680, 0.2503])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.525087594985962 seconds
+
+[16.68, 16.44, 16.68, 16.88, 16.84, 16.72, 16.76, 16.76, 16.64, 16.56]
+[16.84, 16.52, 17.32, 18.8, 20.28, 23.6, 24.52, 23.72, 23.16, 20.48, 20.48, 20.32, 20.28, 20.28]
+14.202978610992432
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5968, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.525087594985962, 'TIME_S_1KI': 1.7635870635030098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.15640623092645, 'W': 19.161924669821744}
+[16.68, 16.44, 16.68, 16.88, 16.84, 16.72, 16.76, 16.76, 16.64, 16.56, 16.48, 16.48, 16.48, 16.6, 16.28, 16.6, 16.48, 16.48, 16.48, 16.48]
+298.7
+14.934999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5968, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.525087594985962, 'TIME_S_1KI': 1.7635870635030098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.15640623092645, 'W': 19.161924669821744, 'J_1KI': 45.60261498507481, 'W_1KI': 3.2107782623695953, 'W_D': 4.226924669821745, 'J_D': 60.03492067575449, 'W_D_1KI': 0.708264857543858, 'J_D_1KI': 0.11867708739005664}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..2e3083a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5340, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.30561351776123, "TIME_S_1KI": 1.929890171865399, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 289.79942796707155, "W": 20.36432449146357, "J_1KI": 54.269555799077075, "W_1KI": 3.8135439122590955, "W_D": 5.188324491463572, "J_D": 73.83370218658452, "W_D_1KI": 0.9715963467160246, "J_D_1KI": 0.18194688140749524}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..e06b49a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2796437740325928}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.3490, 0.2302, 0.3052,  ..., 0.2703, 0.6616, 0.3663])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2796437740325928 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3754 -m matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 7.3809239864349365}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5903, 0.2370, 0.1773,  ..., 0.4937, 0.1117, 0.7578])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 7.3809239864349365 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5340 -m matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.30561351776123}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.2203, 0.4930, 0.6391,  ..., 0.9730, 0.1800, 0.6516])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.30561351776123 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    28,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.2203, 0.4930, 0.6391,  ..., 0.9730, 0.1800, 0.6516])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.30561351776123 seconds
+
+[16.72, 16.92, 16.84, 16.72, 16.8, 17.08, 17.08, 17.12, 16.76, 16.52]
+[16.2, 16.4, 16.84, 19.44, 20.56, 26.36, 27.2, 26.88, 25.68, 25.68, 20.68, 20.6, 20.56, 20.28]
+14.230741024017334
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5340, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.30561351776123, 'TIME_S_1KI': 1.929890171865399, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.79942796707155, 'W': 20.36432449146357}
+[16.72, 16.92, 16.84, 16.72, 16.8, 17.08, 17.08, 17.12, 16.76, 16.52, 16.64, 16.84, 16.84, 17.16, 16.96, 16.92, 16.8, 16.48, 16.84, 16.84]
+303.52
+15.175999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5340, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.30561351776123, 'TIME_S_1KI': 1.929890171865399, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.79942796707155, 'W': 20.36432449146357, 'J_1KI': 54.269555799077075, 'W_1KI': 3.8135439122590955, 'W_D': 5.188324491463572, 'J_D': 73.83370218658452, 'W_D_1KI': 0.9715963467160246, 'J_D_1KI': 0.18194688140749524}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..8be9e23
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5272, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.199397802352905, "TIME_S_1KI": 1.9346353949834798, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.08102276802066, "W": 20.433569505252233, "J_1KI": 51.039647717758086, "W_1KI": 3.8758667498581625, "W_D": 5.440569505252233, "J_D": 71.64455561900142, "W_D_1KI": 1.0319744888566451, "J_D_1KI": 0.19574629910027413}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..c2ade6c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.25819969177246094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1310, 0.9221, 0.7402,  ..., 0.2439, 0.9445, 0.3797])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.25819969177246094 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4066 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.096591711044312}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1967, 0.8576, 0.8759,  ..., 0.1214, 0.7615, 0.8638])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 8.096591711044312 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5272 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.199397802352905}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1490, 0.8093, 0.3487,  ..., 0.4943, 0.2296, 0.2275])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.199397802352905 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.1490, 0.8093, 0.3487,  ..., 0.4943, 0.2296, 0.2275])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.199397802352905 seconds
+
+[16.92, 16.92, 16.92, 16.36, 16.52, 16.48, 16.64, 16.6, 16.56, 16.6]
+[16.64, 16.92, 17.6, 18.72, 24.2, 24.2, 25.08, 25.84, 25.64, 25.96, 20.96, 21.28, 21.48]
+13.16857647895813
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5272, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.199397802352905, 'TIME_S_1KI': 1.9346353949834798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.08102276802066, 'W': 20.433569505252233}
+[16.92, 16.92, 16.92, 16.36, 16.52, 16.48, 16.64, 16.6, 16.56, 16.6, 16.8, 16.72, 16.72, 16.72, 16.72, 16.72, 16.72, 16.68, 16.44, 16.52]
+299.86
+14.993
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5272, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.199397802352905, 'TIME_S_1KI': 1.9346353949834798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.08102276802066, 'W': 20.433569505252233, 'J_1KI': 51.039647717758086, 'W_1KI': 3.8758667498581625, 'W_D': 5.440569505252233, 'J_D': 71.64455561900142, 'W_D_1KI': 1.0319744888566451, 'J_D_1KI': 0.19574629910027413}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..4f71256
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5347, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.743478059768677, "TIME_S_1KI": 2.0092534243068405, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.04572343826294, "W": 19.60362448974542, "J_1KI": 52.00032231873255, "W_1KI": 3.6662847371882217, "W_D": 4.503624489745421, "J_D": 63.87663311958314, "W_D_1KI": 0.8422712716935518, "J_D_1KI": 0.1575222127723119}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..591de07
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.256115198135376}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.2739, 0.2547, 0.5432,  ..., 0.8080, 0.7208, 0.2831])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.256115198135376 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4099 -m matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 8.048463344573975}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.6508, 0.9989, 0.7203,  ..., 0.2533, 0.6992, 0.2593])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 8.048463344573975 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5347 -m matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.743478059768677}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.7828, 0.5181, 0.1831,  ..., 0.8473, 0.1314, 0.3587])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.743478059768677 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.7828, 0.5181, 0.1831,  ..., 0.8473, 0.1314, 0.3587])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.743478059768677 seconds
+
+[16.88, 16.88, 16.68, 16.52, 16.32, 16.32, 16.4, 16.76, 16.88, 16.76]
+[16.96, 16.92, 18.04, 18.04, 19.04, 22.88, 23.76, 24.36, 23.96, 23.92, 21.48, 21.44, 21.32, 21.08]
+14.183383464813232
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5347, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.743478059768677, 'TIME_S_1KI': 2.0092534243068405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.04572343826294, 'W': 19.60362448974542}
+[16.88, 16.88, 16.68, 16.52, 16.32, 16.32, 16.4, 16.76, 16.88, 16.76, 16.68, 16.6, 16.6, 17.04, 17.0, 17.12, 17.12, 17.28, 16.84, 16.96]
+302.0
+15.1
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5347, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.743478059768677, 'TIME_S_1KI': 2.0092534243068405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.04572343826294, 'W': 19.60362448974542, 'J_1KI': 52.00032231873255, 'W_1KI': 3.6662847371882217, 'W_D': 4.503624489745421, 'J_D': 63.87663311958314, 'W_D_1KI': 0.8422712716935518, 'J_D_1KI': 0.1575222127723119}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..89e7aab
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4821, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.69611644744873, "TIME_S_1KI": 2.2186509951148583, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 249.86814396858213, "W": 18.967472090057612, "J_1KI": 51.82911096631034, "W_1KI": 3.934343930731718, "W_D": 4.019472090057613, "J_D": 52.95054744815828, "W_D_1KI": 0.8337423957804633, "J_D_1KI": 0.17293972117412637}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..2fd70ad
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.26701927185058594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7832, 0.3673, 0.6801,  ..., 0.8769, 0.0952, 0.5341])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.26701927185058594 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3932 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 8.563699960708618}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7522, 0.7746, 0.7995,  ..., 0.5259, 0.5024, 0.1696])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 8.563699960708618 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4821 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.69611644744873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0926, 0.0666, 0.7412,  ..., 0.4121, 0.6596, 0.6440])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.69611644744873 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.0926, 0.0666, 0.7412,  ..., 0.4121, 0.6596, 0.6440])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.69611644744873 seconds
+
+[17.0, 16.88, 16.88, 16.76, 16.68, 16.68, 16.72, 16.72, 16.24, 16.32]
+[16.36, 16.28, 16.64, 18.24, 19.08, 22.76, 23.84, 23.92, 23.92, 21.0, 21.0, 21.08, 20.68]
+13.173507928848267
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.69611644744873, 'TIME_S_1KI': 2.2186509951148583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 249.86814396858213, 'W': 18.967472090057612}
+[17.0, 16.88, 16.88, 16.76, 16.68, 16.68, 16.72, 16.72, 16.24, 16.32, 16.44, 16.68, 16.8, 16.64, 16.6, 16.24, 16.32, 16.28, 16.52, 16.88]
+298.96
+14.947999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.69611644744873, 'TIME_S_1KI': 2.2186509951148583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 249.86814396858213, 'W': 18.967472090057612, 'J_1KI': 51.82911096631034, 'W_1KI': 3.934343930731718, 'W_D': 4.019472090057613, 'J_D': 52.95054744815828, 'W_D_1KI': 0.8337423957804633, 'J_D_1KI': 0.17293972117412637}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..3eec6ea
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5003, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.648566484451294, "TIME_S_1KI": 2.1284362351491692, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 306.9760194396972, "W": 21.597597385725194, "J_1KI": 61.35838885462667, "W_1KI": 4.316929319553307, "W_D": 6.5345973857251956, "J_D": 92.87906697607035, "W_D_1KI": 1.3061357956676385, "J_D_1KI": 0.26107051682343363}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..a481b65
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.2607393264770508}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.6863, 0.6130, 0.3987,  ..., 0.0518, 0.4389, 0.3796])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.2607393264770508 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4027 -m matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 8.451263666152954}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.4975, 0.9866, 0.8540,  ..., 0.5940, 0.0080, 0.4703])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 8.451263666152954 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5003 -m matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.648566484451294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8721, 0.6297, 0.9471,  ..., 0.0226, 0.3946, 0.9912])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.648566484451294 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8721, 0.6297, 0.9471,  ..., 0.0226, 0.3946, 0.9912])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.648566484451294 seconds
+
+[16.28, 16.36, 16.56, 16.56, 16.72, 16.92, 16.88, 16.8, 16.84, 16.64]
+[16.56, 16.84, 19.68, 19.68, 21.44, 28.44, 29.68, 30.4, 27.2, 26.56, 21.16, 21.08, 21.24, 21.44]
+14.213433742523193
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5003, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.648566484451294, 'TIME_S_1KI': 2.1284362351491692, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 306.9760194396972, 'W': 21.597597385725194}
+[16.28, 16.36, 16.56, 16.56, 16.72, 16.92, 16.88, 16.8, 16.84, 16.64, 16.8, 16.76, 16.56, 16.56, 16.64, 16.8, 16.8, 17.04, 17.08, 17.04]
+301.26
+15.062999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5003, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.648566484451294, 'TIME_S_1KI': 2.1284362351491692, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 306.9760194396972, 'W': 21.597597385725194, 'J_1KI': 61.35838885462667, 'W_1KI': 4.316929319553307, 'W_D': 6.5345973857251956, 'J_D': 92.87906697607035, 'W_D_1KI': 1.3061357956676385, 'J_D_1KI': 0.26107051682343363}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..fb8fd0b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4874, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.324414014816284, "TIME_S_1KI": 2.118263031353362, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.37779233932497, "W": 22.209521493082242, "J_1KI": 64.91132382833914, "W_1KI": 4.556733995297957, "W_D": 7.24852149308224, "J_D": 103.25621956419945, "W_D_1KI": 1.4871812665330817, "J_D_1KI": 0.3051254137326799}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..13dde5b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.2807776927947998}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.6842, 0.1495, 0.0414,  ..., 0.6601, 0.3430, 0.9557])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.2807776927947998 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3739 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 8.053443431854248}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.8981, 0.1545, 0.9805,  ..., 0.9874, 0.0100, 0.6766])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 8.053443431854248 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4874 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.324414014816284}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.0642, 0.5090, 0.9459,  ..., 0.2103, 0.6817, 0.2365])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.324414014816284 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.0642, 0.5090, 0.9459,  ..., 0.2103, 0.6817, 0.2365])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.324414014816284 seconds
+
+[16.24, 16.12, 16.2, 16.04, 16.04, 16.52, 17.12, 17.28, 17.36, 17.08]
+[16.84, 16.76, 19.6, 21.72, 28.36, 29.16, 30.08, 30.08, 27.72, 26.6, 20.76, 20.68, 20.76, 21.04]
+14.245142221450806
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4874, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.324414014816284, 'TIME_S_1KI': 2.118263031353362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.37779233932497, 'W': 22.209521493082242}
+[16.24, 16.12, 16.2, 16.04, 16.04, 16.52, 17.12, 17.28, 17.36, 17.08, 16.88, 16.88, 16.88, 16.92, 16.68, 16.76, 16.44, 16.44, 16.28, 16.32]
+299.22
+14.961000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4874, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.324414014816284, 'TIME_S_1KI': 2.118263031353362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.37779233932497, 'W': 22.209521493082242, 'J_1KI': 64.91132382833914, 'W_1KI': 4.556733995297957, 'W_D': 7.24852149308224, 'J_D': 103.25621956419945, 'W_D_1KI': 1.4871812665330817, 'J_D_1KI': 0.3051254137326799}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..9c682f3
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4825, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.480221509933472, "TIME_S_1KI": 2.172066634183103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 287.7667935562134, "W": 20.264004477423402, "J_1KI": 59.64078622926702, "W_1KI": 4.199793674077389, "W_D": 5.253004477423401, "J_D": 74.5973115377426, "W_D_1KI": 1.0887055911758343, "J_D_1KI": 0.2256384644924009}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..1eee62f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_040.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.28685975074768066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.4387, 0.5875, 0.4555,  ..., 0.9035, 0.0544, 0.4947])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.28685975074768066 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3660 -m matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 7.963951110839844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.5603, 0.2041, 0.4216,  ..., 0.1672, 0.3974, 0.6520])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 7.963951110839844 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4825 -m matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.480221509933472}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7403, 0.9183, 0.7710,  ..., 0.1687, 0.3194, 0.6404])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.480221509933472 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7403, 0.9183, 0.7710,  ..., 0.1687, 0.3194, 0.6404])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.480221509933472 seconds
+
+[16.8, 16.68, 16.76, 16.84, 16.6, 16.52, 16.6, 16.44, 16.76, 16.72]
+[16.8, 16.8, 17.0, 18.2, 19.8, 25.84, 26.84, 26.96, 26.08, 23.52, 21.12, 21.08, 21.24, 21.24]
+14.200884819030762
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4825, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.480221509933472, 'TIME_S_1KI': 2.172066634183103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 287.7667935562134, 'W': 20.264004477423402}
+[16.8, 16.68, 16.76, 16.84, 16.6, 16.52, 16.6, 16.44, 16.76, 16.72, 16.84, 16.84, 16.92, 17.0, 17.0, 16.64, 16.56, 16.4, 16.28, 16.4]
+300.22
+15.011000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4825, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.480221509933472, 'TIME_S_1KI': 2.172066634183103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 287.7667935562134, 'W': 20.264004477423402, 'J_1KI': 59.64078622926702, 'W_1KI': 4.199793674077389, 'W_D': 5.253004477423401, 'J_D': 74.5973115377426, 'W_D_1KI': 1.0887055911758343, 'J_D_1KI': 0.2256384644924009}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..7e89948
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4657, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.087504863739014, "TIME_S_1KI": 2.380825609563885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 301.57965709686283, "W": 22.814763029917664, "J_1KI": 64.75835454087671, "W_1KI": 4.89902577408582, "W_D": 7.638763029917666, "J_D": 100.97389712905891, "W_D_1KI": 1.640275505672679, "J_D_1KI": 0.35221720113220506}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..a098f6a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.30149173736572266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.3987, 0.5045, 0.3045,  ..., 0.2984, 0.8050, 0.7869])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.30149173736572266 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3482 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 7.849581480026245}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.1977, 0.0810, 0.4349,  ..., 0.8550, 0.4211, 0.9804])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 7.849581480026245 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4657 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.087504863739014}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6761, 0.3112, 0.9967,  ..., 0.7840, 0.7388, 0.4121])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.087504863739014 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6761, 0.3112, 0.9967,  ..., 0.7840, 0.7388, 0.4121])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 11.087504863739014 seconds
+
+[16.56, 16.8, 16.88, 16.88, 17.04, 16.96, 17.2, 17.12, 17.32, 17.0]
+[16.88, 16.68, 19.68, 21.68, 28.56, 30.0, 30.0, 30.92, 28.64, 27.84, 21.96, 21.6, 21.68]
+13.218618869781494
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4657, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.087504863739014, 'TIME_S_1KI': 2.380825609563885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 301.57965709686283, 'W': 22.814763029917664}
+[16.56, 16.8, 16.88, 16.88, 17.04, 16.96, 17.2, 17.12, 17.32, 17.0, 16.6, 16.52, 16.68, 16.76, 16.84, 16.88, 16.6, 16.6, 16.84, 17.04]
+303.52
+15.175999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4657, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.087504863739014, 'TIME_S_1KI': 2.380825609563885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 301.57965709686283, 'W': 22.814763029917664, 'J_1KI': 64.75835454087671, 'W_1KI': 4.89902577408582, 'W_D': 7.638763029917666, 'J_D': 100.97389712905891, 'W_D_1KI': 1.640275505672679, 'J_D_1KI': 0.35221720113220506}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..7159915
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4758, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.375899076461792, "TIME_S_1KI": 2.1807270021987795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 286.0907993316651, "W": 20.15934018247557, "J_1KI": 60.12837312561267, "W_1KI": 4.236935725614875, "W_D": 5.162340182475575, "J_D": 73.26122858476649, "W_D_1KI": 1.0849811228405999, "J_D_1KI": 0.2280330228752837}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..89198e4
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.23725461959838867}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.4785, 0.1103, 0.8138,  ..., 0.5036, 0.5348, 0.4789])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.23725461959838867 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4425 -m matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 9.764552354812622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.2591, 0.3069, 0.8779,  ..., 0.3942, 0.1835, 0.1540])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 9.764552354812622 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4758 -m matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.375899076461792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8174, 0.3012, 0.7437,  ..., 0.6015, 0.6611, 0.9554])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.375899076461792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8174, 0.3012, 0.7437,  ..., 0.6015, 0.6611, 0.9554])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.375899076461792 seconds
+
+[16.36, 16.56, 16.52, 16.64, 16.68, 16.68, 16.4, 16.6, 16.6, 16.68]
+[16.8, 16.76, 16.76, 17.8, 19.96, 25.76, 26.6, 27.28, 25.72, 25.52, 20.72, 20.52, 20.24, 20.32]
+14.191476345062256
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4758, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.375899076461792, 'TIME_S_1KI': 2.1807270021987795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 286.0907993316651, 'W': 20.15934018247557}
+[16.36, 16.56, 16.52, 16.64, 16.68, 16.68, 16.4, 16.6, 16.6, 16.68, 16.8, 16.8, 16.76, 16.84, 17.2, 17.04, 16.84, 16.76, 16.12, 15.96]
+299.93999999999994
+14.996999999999996
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4758, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.375899076461792, 'TIME_S_1KI': 2.1807270021987795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 286.0907993316651, 'W': 20.15934018247557, 'J_1KI': 60.12837312561267, 'W_1KI': 4.236935725614875, 'W_D': 5.162340182475575, 'J_D': 73.26122858476649, 'W_D_1KI': 1.0849811228405999, 'J_D_1KI': 0.2280330228752837}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..efbb301
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4739, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.536023616790771, "TIME_S_1KI": 2.2232588345201036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.09617205619816, "W": 20.864532191184587, "J_1KI": 62.48072843557674, "W_1KI": 4.40272888609086, "W_D": 5.663532191184588, "J_D": 80.37324713349346, "W_D_1KI": 1.1950901437401538, "J_D_1KI": 0.2521819252458649}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..213e50c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.2958800792694092}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.0279, 0.3720, 0.5419,  ..., 0.9391, 0.6222, 0.1335])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.2958800792694092 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3548 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.860142707824707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.0798, 0.6202, 0.2546,  ..., 0.5902, 0.1772, 0.5374])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 7.860142707824707 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4739 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.536023616790771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.8748, 0.1554, 0.2380,  ..., 0.7350, 0.2527, 0.7569])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.536023616790771 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.8748, 0.1554, 0.2380,  ..., 0.7350, 0.2527, 0.7569])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.536023616790771 seconds
+
+[16.32, 16.6, 16.8, 16.8, 17.0, 16.76, 16.84, 17.08, 17.24, 17.52]
+[17.4, 17.4, 17.0, 20.2, 22.04, 27.64, 28.64, 29.48, 25.76, 23.6, 20.68, 20.52, 20.36, 20.36]
+14.191364049911499
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4739, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.536023616790771, 'TIME_S_1KI': 2.2232588345201036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.09617205619816, 'W': 20.864532191184587}
+[16.32, 16.6, 16.8, 16.8, 17.0, 16.76, 16.84, 17.08, 17.24, 17.52, 17.2, 17.2, 17.16, 17.04, 16.92, 16.8, 16.6, 16.6, 16.64, 16.84]
+304.02
+15.200999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4739, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.536023616790771, 'TIME_S_1KI': 2.2232588345201036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.09617205619816, 'W': 20.864532191184587, 'J_1KI': 62.48072843557674, 'W_1KI': 4.40272888609086, 'W_D': 5.663532191184588, 'J_D': 80.37324713349346, 'W_D_1KI': 1.1950901437401538, 'J_D_1KI': 0.2521819252458649}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..25ca5d6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4639, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.635314226150513, "TIME_S_1KI": 2.292587675393514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.1446775054932, "W": 21.83934978141949, "J_1KI": 66.85593393091037, "W_1KI": 4.707771024233561, "W_D": 6.84234978141949, "J_D": 97.16948478674892, "W_D_1KI": 1.474962229234639, "J_D_1KI": 0.3179483141268892}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..0e1b978
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.285520076751709}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.7507, 0.4108, 0.5044,  ..., 0.8559, 0.6374, 0.4359])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.285520076751709 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3677 -m matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 9.238277673721313}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.4013, 0.4880, 0.8571,  ..., 0.1644, 0.4300, 0.2721])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 9.238277673721313 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4179 -m matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 9.456888675689697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.2911, 0.4206, 0.5645,  ..., 0.5151, 0.8121, 0.8956])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 9.456888675689697 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4639 -m matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.635314226150513}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6928, 0.2707, 0.1949,  ..., 0.6926, 0.5420, 0.7708])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.635314226150513 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.6928, 0.2707, 0.1949,  ..., 0.6926, 0.5420, 0.7708])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.635314226150513 seconds
+
+[16.76, 16.76, 16.88, 17.12, 16.96, 16.96, 16.92, 16.56, 16.4, 16.6]
+[16.72, 16.68, 19.88, 21.52, 21.52, 29.04, 30.04, 30.76, 27.88, 26.76, 21.04, 21.12, 20.96, 20.8]
+14.201186418533325
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4639, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.635314226150513, 'TIME_S_1KI': 2.292587675393514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.1446775054932, 'W': 21.83934978141949}
+[16.76, 16.76, 16.88, 17.12, 16.96, 16.96, 16.92, 16.56, 16.4, 16.6, 16.64, 16.72, 16.48, 16.52, 16.52, 16.48, 16.6, 16.52, 16.32, 16.44]
+299.94
+14.997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4639, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.635314226150513, 'TIME_S_1KI': 2.292587675393514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.1446775054932, 'W': 21.83934978141949, 'J_1KI': 66.85593393091037, 'W_1KI': 4.707771024233561, 'W_D': 6.84234978141949, 'J_D': 97.16948478674892, 'W_D_1KI': 1.474962229234639, 'J_D_1KI': 0.3179483141268892}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..a60fb61
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4568, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.563419342041016, "TIME_S_1KI": 2.3124823428285937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.5167537593842, "W": 20.961702986599526, "J_1KI": 69.72783576168655, "W_1KI": 4.588814138922839, "W_D": 5.795702986599526, "J_D": 88.0667235016823, "W_D_1KI": 1.268761599518285, "J_D_1KI": 0.27774991232887153}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..e314abf
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.2923262119293213}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.4849, 0.2731, 0.9040,  ..., 0.3334, 0.6361, 0.9845])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.2923262119293213 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3591 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 8.252921104431152}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.4814, 0.9926, 0.6267,  ..., 0.1528, 0.7014, 0.4043])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 8.252921104431152 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4568 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.563419342041016}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.5323, 0.6987, 0.7869,  ..., 0.7650, 0.9130, 0.3621])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.563419342041016 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.5323, 0.6987, 0.7869,  ..., 0.7650, 0.9130, 0.3621])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.563419342041016 seconds
+
+[17.04, 17.04, 17.04, 17.04, 17.0, 17.0, 16.92, 16.92, 16.92, 16.84]
+[16.56, 16.68, 19.6, 21.2, 21.2, 26.52, 27.64, 28.44, 24.84, 24.72, 20.76, 20.96, 21.36, 21.44, 21.4]
+15.195175409317017
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4568, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.563419342041016, 'TIME_S_1KI': 2.3124823428285937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5167537593842, 'W': 20.961702986599526}
+[17.04, 17.04, 17.04, 17.04, 17.0, 17.0, 16.92, 16.92, 16.92, 16.84, 16.8, 16.64, 16.72, 16.72, 16.48, 16.44, 16.68, 16.96, 16.92, 17.08]
+303.32
+15.166
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4568, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.563419342041016, 'TIME_S_1KI': 2.3124823428285937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5167537593842, 'W': 20.961702986599526, 'J_1KI': 69.72783576168655, 'W_1KI': 4.588814138922839, 'W_D': 5.795702986599526, 'J_D': 88.0667235016823, 'W_D_1KI': 1.268761599518285, 'J_D_1KI': 0.27774991232887153}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..a57fbd2
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5144, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.04363226890564, "TIME_S_1KI": 1.9524946090407542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 272.48534147262575, "W": 20.629652481783676, "J_1KI": 52.97148939981061, "W_1KI": 4.010430109211446, "W_D": 5.569652481783674, "J_D": 73.56637053012844, "W_D_1KI": 1.0827473720419272, "J_D_1KI": 0.2104874362445426}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..fc5138c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.230118989944458}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.3154, 0.4290, 0.0746,  ..., 0.3351, 0.5718, 0.1705])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.230118989944458 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4562 -m matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 9.311099290847778}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.7437, 0.5028, 0.9190,  ..., 0.7591, 0.0904, 0.3951])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 9.311099290847778 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5144 -m matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.04363226890564}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5245, 0.2035, 0.6052,  ..., 0.1284, 0.9267, 0.0163])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.04363226890564 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5245, 0.2035, 0.6052,  ..., 0.1284, 0.9267, 0.0163])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.04363226890564 seconds
+
+[16.48, 16.68, 17.08, 16.88, 17.04, 17.12, 17.12, 16.72, 16.48, 16.4]
+[16.4, 16.44, 17.0, 21.36, 23.92, 27.04, 28.16, 25.72, 24.44, 24.44, 20.6, 20.6, 20.56]
+13.208431005477905
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5144, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.04363226890564, 'TIME_S_1KI': 1.9524946090407542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.48534147262575, 'W': 20.629652481783676}
+[16.48, 16.68, 17.08, 16.88, 17.04, 17.12, 17.12, 16.72, 16.48, 16.4, 16.72, 16.8, 16.64, 16.56, 16.52, 16.24, 16.52, 16.8, 16.8, 16.8]
+301.20000000000005
+15.060000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5144, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.04363226890564, 'TIME_S_1KI': 1.9524946090407542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.48534147262575, 'W': 20.629652481783676, 'J_1KI': 52.97148939981061, 'W_1KI': 4.010430109211446, 'W_D': 5.569652481783674, 'J_D': 73.56637053012844, 'W_D_1KI': 1.0827473720419272, 'J_D_1KI': 0.2104874362445426}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..72a8002
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4489, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.591835021972656, "TIME_S_1KI": 2.359508804181924, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 293.3003367614746, "W": 20.65804885532527, "J_1KI": 65.33756666551005, "W_1KI": 4.601926677506186, "W_D": 5.662048855325271, "J_D": 80.38904582214357, "W_D_1KI": 1.2613162965750213, "J_D_1KI": 0.28097934875808}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..05a6be0
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.2903709411621094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.5496, 0.8721, 0.4707,  ..., 0.6399, 0.3015, 0.0205])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.2903709411621094 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3616 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 8.456918239593506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.8149, 0.0064, 0.2491,  ..., 0.0040, 0.4481, 0.7236])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 8.456918239593506 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4489 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.591835021972656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.6259, 0.8430, 0.9333,  ..., 0.4721, 0.8188, 0.1886])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.591835021972656 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.6259, 0.8430, 0.9333,  ..., 0.4721, 0.8188, 0.1886])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.591835021972656 seconds
+
+[16.64, 16.72, 16.88, 17.2, 17.0, 16.92, 16.72, 16.44, 16.24, 16.36]
+[16.36, 16.44, 16.52, 20.16, 21.24, 27.36, 28.6, 29.16, 26.2, 23.56, 20.64, 20.4, 20.52, 20.52]
+14.197872161865234
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4489, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.591835021972656, 'TIME_S_1KI': 2.359508804181924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.3003367614746, 'W': 20.65804885532527}
+[16.64, 16.72, 16.88, 17.2, 17.0, 16.92, 16.72, 16.44, 16.24, 16.36, 16.16, 16.08, 16.2, 16.48, 16.4, 16.56, 16.8, 17.04, 17.12, 17.08]
+299.91999999999996
+14.995999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4489, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.591835021972656, 'TIME_S_1KI': 2.359508804181924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.3003367614746, 'W': 20.65804885532527, 'J_1KI': 65.33756666551005, 'W_1KI': 4.601926677506186, 'W_D': 5.662048855325271, 'J_D': 80.38904582214357, 'W_D_1KI': 1.2613162965750213, 'J_D_1KI': 0.28097934875808}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..d955983
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4323, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.41606879234314, "TIME_S_1KI": 2.4094538034566595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.13494171142577, "W": 20.851358366110833, "J_1KI": 68.50218406463699, "W_1KI": 4.823353774256496, "W_D": 6.062358366110836, "J_D": 86.09876200199129, "W_D_1KI": 1.4023498418021827, "J_D_1KI": 0.3243927461952771}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..96ed437
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_080.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.2644522190093994}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1711, 0.4039, 0.2589,  ..., 0.2732, 0.9985, 0.8325])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.2644522190093994 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3970 -m matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 9.641419172286987}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5369, 0.0526, 0.4887,  ..., 0.5249, 0.2326, 0.2024])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 9.641419172286987 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4323 -m matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.41606879234314}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.0491, 0.2697, 0.4939,  ..., 0.9427, 0.5549, 0.3944])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.41606879234314 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.0491, 0.2697, 0.4939,  ..., 0.9427, 0.5549, 0.3944])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.41606879234314 seconds
+
+[16.04, 16.28, 16.36, 16.48, 16.68, 16.56, 16.56, 16.36, 16.12, 16.24]
+[16.16, 16.44, 17.44, 19.04, 20.4, 26.72, 27.76, 27.0, 26.96, 26.96, 21.48, 21.4, 21.4, 21.44]
+14.202189445495605
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4323, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.41606879234314, 'TIME_S_1KI': 2.4094538034566595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.13494171142577, 'W': 20.851358366110833}
+[16.04, 16.28, 16.36, 16.48, 16.68, 16.56, 16.56, 16.36, 16.12, 16.24, 16.4, 16.48, 16.32, 16.04, 16.32, 16.28, 16.52, 16.64, 16.96, 16.96]
+295.78
+14.788999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4323, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.41606879234314, 'TIME_S_1KI': 2.4094538034566595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.13494171142577, 'W': 20.851358366110833, 'J_1KI': 68.50218406463699, 'W_1KI': 4.823353774256496, 'W_D': 6.062358366110836, 'J_D': 86.09876200199129, 'W_D_1KI': 1.4023498418021827, 'J_D_1KI': 0.3243927461952771}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..41045b6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4271, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.287059783935547, "TIME_S_1KI": 2.408583419324642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.75852571487434, "W": 19.954165262711065, "J_1KI": 66.43842793605111, "W_1KI": 4.672012470782268, "W_D": 4.871165262711063, "J_D": 69.27048339343077, "W_D_1KI": 1.1405210167902278, "J_D_1KI": 0.26703840243273885}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..918dbe8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.30860424041748047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.6175, 0.3198, 0.3809,  ..., 0.8583, 0.2210, 0.7236])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.30860424041748047 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3402 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.36231279373169}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.2189, 0.7362, 0.7287,  ..., 0.2468, 0.1466, 0.5130])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 8.36231279373169 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4271 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.287059783935547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3002, 0.9937, 0.1201,  ..., 0.2870, 0.2280, 0.7451])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.287059783935547 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.3002, 0.9937, 0.1201,  ..., 0.2870, 0.2280, 0.7451])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.287059783935547 seconds
+
+[16.52, 16.64, 16.6, 16.32, 16.16, 16.4, 16.72, 17.0, 17.0, 17.44]
+[17.48, 17.52, 17.16, 19.72, 20.28, 25.76, 26.2, 26.24, 25.12, 20.56, 20.6, 20.6, 20.56, 20.4]
+14.220515966415405
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4271, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.287059783935547, 'TIME_S_1KI': 2.408583419324642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.75852571487434, 'W': 19.954165262711065}
+[16.52, 16.64, 16.6, 16.32, 16.16, 16.4, 16.72, 17.0, 17.0, 17.44, 17.12, 17.2, 17.36, 17.12, 16.88, 16.76, 16.76, 16.32, 16.56, 16.64]
+301.66
+15.083000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4271, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.287059783935547, 'TIME_S_1KI': 2.408583419324642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.75852571487434, 'W': 19.954165262711065, 'J_1KI': 66.43842793605111, 'W_1KI': 4.672012470782268, 'W_D': 4.871165262711063, 'J_D': 69.27048339343077, 'W_D_1KI': 1.1405210167902278, 'J_D_1KI': 0.26703840243273885}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..e8c3488
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4210, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.150678873062134, "TIME_S_1KI": 2.4110876183045447, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.20230672836306, "W": 21.05593392035132, "J_1KI": 71.06943152692709, "W_1KI": 5.001409482268722, "W_D": 5.993933920351317, "J_D": 85.17308527517318, "W_D_1KI": 1.423737273242593, "J_D_1KI": 0.33817987487947576}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..c6f472d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.24939775466918945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.7930, 0.7793, 0.2045,  ..., 0.7672, 0.2068, 0.4384])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.24939775466918945 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4210 -m matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.150678873062134}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8307, 0.2544, 0.1766,  ..., 0.4351, 0.9385, 0.2219])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.150678873062134 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.8307, 0.2544, 0.1766,  ..., 0.4351, 0.9385, 0.2219])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.150678873062134 seconds
+
+[16.48, 16.84, 16.6, 16.88, 16.76, 16.6, 16.44, 16.68, 16.6, 16.72]
+[16.52, 16.52, 16.64, 20.08, 21.84, 28.8, 29.48, 30.08, 26.6, 23.64, 20.72, 20.84, 20.88, 20.88]
+14.209880590438843
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4210, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.150678873062134, 'TIME_S_1KI': 2.4110876183045447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.20230672836306, 'W': 21.05593392035132}
+[16.48, 16.84, 16.6, 16.88, 16.76, 16.6, 16.44, 16.68, 16.6, 16.72, 16.76, 16.76, 16.6, 17.12, 17.04, 17.08, 17.0, 16.6, 16.48, 16.36]
+301.24
+15.062000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4210, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.150678873062134, 'TIME_S_1KI': 2.4110876183045447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.20230672836306, 'W': 21.05593392035132, 'J_1KI': 71.06943152692709, 'W_1KI': 5.001409482268722, 'W_D': 5.993933920351317, 'J_D': 85.17308527517318, 'W_D_1KI': 1.423737273242593, 'J_D_1KI': 0.33817987487947576}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..dc9a8a8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4250, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.384530544281006, "TIME_S_1KI": 2.4434189515955307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 290.7397892761231, "W": 19.124429821644952, "J_1KI": 68.4093621826172, "W_1KI": 4.499865840387048, "W_D": 4.16442982164495, "J_D": 63.309884796142576, "W_D_1KI": 0.9798658403870469, "J_D_1KI": 0.23055666832636398}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..031e728
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.26108789443969727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.7556, 0.9846, 0.5564,  ..., 0.2660, 0.6333, 0.6730])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.26108789443969727 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4021 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.932278633117676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.9238, 0.9591, 0.3426,  ..., 0.4580, 0.1545, 0.0101])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 9.932278633117676 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4250 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.384530544281006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2116, 0.2482, 0.7132,  ..., 0.4592, 0.9483, 0.3636])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.384530544281006 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2116, 0.2482, 0.7132,  ..., 0.4592, 0.9483, 0.3636])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.384530544281006 seconds
+
+[16.56, 16.56, 16.6, 16.64, 16.52, 16.64, 16.36, 16.4, 16.68, 16.92]
+[16.92, 16.84, 17.48, 18.12, 18.12, 21.68, 22.84, 23.64, 23.52, 23.32, 20.64, 20.72, 20.56, 20.44, 20.44]
+15.202533721923828
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4250, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.384530544281006, 'TIME_S_1KI': 2.4434189515955307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.7397892761231, 'W': 19.124429821644952}
+[16.56, 16.56, 16.6, 16.64, 16.52, 16.64, 16.36, 16.4, 16.68, 16.92, 16.52, 16.64, 16.6, 16.6, 16.64, 16.8, 16.8, 16.6, 16.76, 16.72]
+299.20000000000005
+14.960000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4250, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.384530544281006, 'TIME_S_1KI': 2.4434189515955307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.7397892761231, 'W': 19.124429821644952, 'J_1KI': 68.4093621826172, 'W_1KI': 4.499865840387048, 'W_D': 4.16442982164495, 'J_D': 63.309884796142576, 'W_D_1KI': 0.9798658403870469, 'J_D_1KI': 0.23055666832636398}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..fe2123c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4266, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.460581064224243, "TIME_S_1KI": 2.4520818247126686, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 317.53529266357424, "W": 22.35741232740164, "J_1KI": 74.43396452498224, "W_1KI": 5.24083739507774, "W_D": 7.580412327401643, "J_D": 107.66221115589148, "W_D_1KI": 1.7769367856075111, "J_D_1KI": 0.41653464266467677}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..ecf138f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.2723679542541504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.6448, 0.6814, 0.2847,  ..., 0.9186, 0.6241, 0.5869])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.2723679542541504 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3855 -m matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 9.486452579498291}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2403, 0.2547, 0.5130,  ..., 0.2918, 0.3102, 0.5702])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 9.486452579498291 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4266 -m matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.460581064224243}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.0167, 0.0168, 0.8671,  ..., 0.1295, 0.1043, 0.8461])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.460581064224243 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.0167, 0.0168, 0.8671,  ..., 0.1295, 0.1043, 0.8461])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.460581064224243 seconds
+
+[16.16, 16.48, 16.48, 16.6, 16.8, 16.72, 16.52, 16.32, 16.56, 16.44]
+[16.76, 16.72, 20.0, 21.84, 28.72, 28.72, 29.4, 30.12, 26.84, 26.72, 21.0, 21.56, 22.0, 22.08]
+14.202685356140137
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4266, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.460581064224243, 'TIME_S_1KI': 2.4520818247126686, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.53529266357424, 'W': 22.35741232740164}
+[16.16, 16.48, 16.48, 16.6, 16.8, 16.72, 16.52, 16.32, 16.56, 16.44, 16.12, 16.08, 16.12, 16.56, 16.56, 16.68, 16.24, 16.16, 16.24, 16.12]
+295.53999999999996
+14.776999999999997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4266, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.460581064224243, 'TIME_S_1KI': 2.4520818247126686, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.53529266357424, 'W': 22.35741232740164, 'J_1KI': 74.43396452498224, 'W_1KI': 5.24083739507774, 'W_D': 7.580412327401643, 'J_D': 107.66221115589148, 'W_D_1KI': 1.7769367856075111, 'J_D_1KI': 0.41653464266467677}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..137d00d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4088, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.275744199752808, "TIME_S_1KI": 2.513636056691, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 289.4017578125, "W": 20.387012119755294, "J_1KI": 70.79299359405577, "W_1KI": 4.987038189764015, "W_D": 5.504012119755293, "J_D": 78.13164445686343, "W_D_1KI": 1.346382612464602, "J_D_1KI": 0.32934995412539186}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..7d25df3
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.31772923469543457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8246, 0.9143, 0.6234,  ..., 0.5880, 0.0932, 0.8821])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.31772923469543457 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3304 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.484694242477417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.5893, 0.7585, 0.8994,  ..., 0.5279, 0.1317, 0.1500])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 8.484694242477417 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4088 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.275744199752808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.6257, 0.8973, 0.7039,  ..., 0.3870, 0.8920, 0.1294])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.275744199752808 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.6257, 0.8973, 0.7039,  ..., 0.3870, 0.8920, 0.1294])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.275744199752808 seconds
+
+[16.64, 16.6, 16.52, 16.8, 16.76, 16.6, 16.56, 16.48, 16.28, 16.48]
+[16.72, 16.6, 17.92, 17.92, 19.24, 25.56, 26.8, 27.68, 26.8, 26.44, 20.84, 20.4, 20.6, 20.48]
+14.195398330688477
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4088, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.275744199752808, 'TIME_S_1KI': 2.513636056691, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.4017578125, 'W': 20.387012119755294}
+[16.64, 16.6, 16.52, 16.8, 16.76, 16.6, 16.56, 16.48, 16.28, 16.48, 16.36, 16.44, 16.44, 16.52, 16.68, 16.8, 16.68, 16.48, 16.2, 16.16]
+297.66
+14.883000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4088, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.275744199752808, 'TIME_S_1KI': 2.513636056691, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.4017578125, 'W': 20.387012119755294, 'J_1KI': 70.79299359405577, 'W_1KI': 4.987038189764015, 'W_D': 5.504012119755293, 'J_D': 78.13164445686343, 'W_D_1KI': 1.346382612464602, 'J_D_1KI': 0.32934995412539186}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..83e4c93
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3965, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.04805588722229, "TIME_S_1KI": 2.534188117836643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 255.93813702583313, "W": 19.440286072270194, "J_1KI": 64.54934099012185, "W_1KI": 4.90297252768479, "W_D": 4.485286072270192, "J_D": 59.050353327989555, "W_D_1KI": 1.131219690358182, "J_D_1KI": 0.2853013090436777}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..df0eb87
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.26479268074035645}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5281, 0.6895, 0.4409,  ..., 0.8118, 0.1791, 0.1370])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.26479268074035645 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3965 -m matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.04805588722229}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6717, 0.7293, 0.9391,  ..., 0.1389, 0.8945, 0.0011])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.04805588722229 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6717, 0.7293, 0.9391,  ..., 0.1389, 0.8945, 0.0011])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.04805588722229 seconds
+
+[16.6, 16.52, 16.64, 16.6, 16.56, 16.56, 16.6, 16.68, 16.64, 16.6]
+[16.36, 16.4, 17.16, 17.8, 21.8, 22.8, 23.64, 23.64, 23.72, 23.84, 21.12, 21.44, 21.64]
+13.165348291397095
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3965, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.04805588722229, 'TIME_S_1KI': 2.534188117836643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 255.93813702583313, 'W': 19.440286072270194}
+[16.6, 16.52, 16.64, 16.6, 16.56, 16.56, 16.6, 16.68, 16.64, 16.6, 16.72, 16.8, 16.68, 16.76, 16.56, 16.52, 16.56, 16.56, 16.56, 16.68]
+299.1
+14.955000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3965, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.04805588722229, 'TIME_S_1KI': 2.534188117836643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 255.93813702583313, 'W': 19.440286072270194, 'J_1KI': 64.54934099012185, 'W_1KI': 4.90297252768479, 'W_D': 4.485286072270192, 'J_D': 59.050353327989555, 'W_D_1KI': 1.131219690358182, 'J_D_1KI': 0.2853013090436777}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..db98120
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3741, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.685789585113525, "TIME_S_1KI": 2.8563992475577455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 251.80008142471314, "W": 19.08240471242988, "J_1KI": 67.30822812742933, "W_1KI": 5.100883376752173, "W_D": 4.222404712429881, "J_D": 55.71634531497957, "W_D_1KI": 1.1286834302138147, "J_D_1KI": 0.3017063432808914}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..8a8882c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.28065943717956543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.5799, 0.8054, 0.1060,  ..., 0.8848, 0.9407, 0.5317])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.28065943717956543 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3741 -m matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.685789585113525}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.6935, 0.6685, 0.1445,  ..., 0.3962, 0.8482, 0.0100])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.685789585113525 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.6935, 0.6685, 0.1445,  ..., 0.3962, 0.8482, 0.0100])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.685789585113525 seconds
+
+[16.24, 16.12, 15.88, 16.04, 16.12, 16.2, 16.36, 16.44, 16.52, 16.44]
+[16.64, 16.56, 17.16, 17.16, 18.44, 22.28, 23.4, 24.16, 24.24, 23.92, 20.76, 20.88, 20.88]
+13.195406198501587
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3741, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.685789585113525, 'TIME_S_1KI': 2.8563992475577455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 251.80008142471314, 'W': 19.08240471242988}
+[16.24, 16.12, 15.88, 16.04, 16.12, 16.2, 16.36, 16.44, 16.52, 16.44, 16.8, 16.8, 17.04, 16.8, 16.8, 16.64, 16.76, 16.76, 16.84, 16.68]
+297.2
+14.86
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3741, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.685789585113525, 'TIME_S_1KI': 2.8563992475577455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 251.80008142471314, 'W': 19.08240471242988, 'J_1KI': 67.30822812742933, 'W_1KI': 5.100883376752173, 'W_D': 4.222404712429881, 'J_D': 55.71634531497957, 'W_D_1KI': 1.1286834302138147, 'J_D_1KI': 0.3017063432808914}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..83238cf
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3937, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.046002388000488, "TIME_S_1KI": 2.551689709931544, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 290.3304630088806, "W": 22.037675427010264, "J_1KI": 73.74408509242586, "W_1KI": 5.597580753622114, "W_D": 7.046675427010268, "J_D": 92.83486119818691, "W_D_1KI": 1.7898591381788844, "J_D_1KI": 0.4546251303476973}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..b792aa4
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.26668810844421387}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.9452, 0.4929, 0.4146,  ..., 0.4034, 0.0303, 0.2118])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.26668810844421387 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3937 -m matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.046002388000488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7126, 0.3308, 0.8384,  ..., 0.5018, 0.7437, 0.4357])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.046002388000488 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/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,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.7126, 0.3308, 0.8384,  ..., 0.5018, 0.7437, 0.4357])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.046002388000488 seconds
+
+[16.0, 16.16, 16.28, 16.56, 16.64, 16.64, 17.0, 17.28, 17.16, 17.0]
+[16.68, 16.28, 16.8, 21.6, 25.96, 29.52, 30.76, 28.32, 28.32, 27.12, 21.56, 21.24, 21.52]
+13.174278020858765
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3937, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.046002388000488, 'TIME_S_1KI': 2.551689709931544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.3304630088806, 'W': 22.037675427010264}
+[16.0, 16.16, 16.28, 16.56, 16.64, 16.64, 17.0, 17.28, 17.16, 17.0, 16.68, 16.6, 17.0, 16.6, 16.44, 16.72, 16.76, 16.36, 16.52, 16.52]
+299.81999999999994
+14.990999999999996
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3937, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.046002388000488, 'TIME_S_1KI': 2.551689709931544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.3304630088806, 'W': 22.037675427010264, 'J_1KI': 73.74408509242586, 'W_1KI': 5.597580753622114, 'W_D': 7.046675427010268, 'J_D': 92.83486119818691, 'W_D_1KI': 1.7898591381788844, 'J_D_1KI': 0.4546251303476973}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..4a37c59
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130157, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.980890989303589, "TIME_S_1KI": 0.08436650344817097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1360.8988891363142, "W": 103.84999999999998, "J_1KI": 10.455825573240887, "W_1KI": 0.797882557219358, "W_D": 68.19524999999999, "J_D": 893.6623973940609, "W_D_1KI": 0.5239460805027774, "J_D_1KI": 0.004025492908585611}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..8a7f427
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.027045488357543945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.0726, 0.8672, 0.2478,  ..., 0.8634, 0.5840, 0.1396])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.027045488357543945 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38823', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 3.131915807723999}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.8327, 0.6571, 0.9655,  ..., 0.9197, 0.6003, 0.8237])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 3.131915807723999 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130157', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.980890989303589}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5182, 0.8575, 0.3280,  ..., 0.2900, 0.6283, 0.6352])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.980890989303589 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5182, 0.8575, 0.3280,  ..., 0.2900, 0.6283, 0.6352])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.980890989303589 seconds
+
+[40.02, 39.58, 40.96, 39.71, 39.45, 39.25, 39.49, 39.49, 39.68, 39.5]
+[103.85]
+13.104466915130615
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.980890989303589, 'TIME_S_1KI': 0.08436650344817097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8988891363142, 'W': 103.84999999999998}
+[40.02, 39.58, 40.96, 39.71, 39.45, 39.25, 39.49, 39.49, 39.68, 39.5, 39.99, 40.15, 39.53, 39.12, 39.3, 39.41, 39.19, 39.32, 39.94, 39.54]
+713.095
+35.65475
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.980890989303589, 'TIME_S_1KI': 0.08436650344817097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8988891363142, 'W': 103.84999999999998, 'J_1KI': 10.455825573240887, 'W_1KI': 0.797882557219358, 'W_D': 68.19524999999999, 'J_D': 893.6623973940609, 'W_D_1KI': 0.5239460805027774, 'J_D_1KI': 0.004025492908585611}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..5a6a48e
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 124107, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.472471475601196, "TIME_S_1KI": 0.08438260110711883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1401.2223204374313, "W": 103.5, "J_1KI": 11.290437448632481, "W_1KI": 0.8339577944837921, "W_D": 67.606, "J_D": 915.2757120337485, "W_D_1KI": 0.5447396198441667, "J_D_1KI": 0.004389273931721552}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..0042459
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.03391551971435547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.0415, 0.1780, 0.6161,  ..., 0.4639, 0.4621, 0.7782])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.03391551971435547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30959', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 2.619264841079712}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.0435, 0.7144, 0.3205,  ..., 0.3230, 0.6095, 0.5117])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 2.619264841079712 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '124107', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.472471475601196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4145, 0.3509, 0.6190,  ..., 0.6049, 0.0654, 0.9027])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.472471475601196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.4145, 0.3509, 0.6190,  ..., 0.6049, 0.0654, 0.9027])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.472471475601196 seconds
+
+[40.43, 39.36, 39.47, 40.33, 39.32, 39.53, 39.81, 39.74, 39.4, 39.3]
+[103.5]
+13.538379907608032
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124107, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.472471475601196, 'TIME_S_1KI': 0.08438260110711883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1401.2223204374313, 'W': 103.5}
+[40.43, 39.36, 39.47, 40.33, 39.32, 39.53, 39.81, 39.74, 39.4, 39.3, 40.0, 39.42, 39.66, 39.28, 39.4, 39.2, 44.9, 39.68, 39.58, 39.87]
+717.8800000000001
+35.894000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124107, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.472471475601196, 'TIME_S_1KI': 0.08438260110711883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1401.2223204374313, 'W': 103.5, 'J_1KI': 11.290437448632481, 'W_1KI': 0.8339577944837921, 'W_D': 67.606, 'J_D': 915.2757120337485, 'W_D_1KI': 0.5447396198441667, 'J_D_1KI': 0.004389273931721552}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..86d232e
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 127569, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.044095754623413, "TIME_S_1KI": 0.08657350731465649, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1376.8041901683807, "W": 104.67999999999999, "J_1KI": 10.792623522708343, "W_1KI": 0.8205755316730553, "W_D": 68.81824999999999, "J_D": 905.1323553692697, "W_D_1KI": 0.539459037854024, "J_D_1KI": 0.004228762770375437}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..eb5a0a8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024904251098632812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.5342, 0.2839, 0.0573,  ..., 0.3701, 0.8163, 0.3579])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.024904251098632812 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '42161', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 3.470177412033081}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.2040, 0.7311, 0.0868,  ..., 0.2631, 0.7558, 0.0829])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 3.470177412033081 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '127569', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.044095754623413}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8849, 0.5798, 0.8579,  ..., 0.4043, 0.5210, 0.4997])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 11.044095754623413 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8849, 0.5798, 0.8579,  ..., 0.4043, 0.5210, 0.4997])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 11.044095754623413 seconds
+
+[40.38, 41.29, 40.48, 39.34, 39.37, 39.3, 39.47, 39.55, 40.0, 39.59]
+[104.68]
+13.152504682540894
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 127569, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.044095754623413, 'TIME_S_1KI': 0.08657350731465649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.8041901683807, 'W': 104.67999999999999}
+[40.38, 41.29, 40.48, 39.34, 39.37, 39.3, 39.47, 39.55, 40.0, 39.59, 40.5, 39.43, 39.41, 41.1, 40.42, 39.23, 40.12, 39.53, 39.35, 39.22]
+717.235
+35.86175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 127569, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.044095754623413, 'TIME_S_1KI': 0.08657350731465649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.8041901683807, 'W': 104.67999999999999, 'J_1KI': 10.792623522708343, 'W_1KI': 0.8205755316730553, 'W_D': 68.81824999999999, 'J_D': 905.1323553692697, 'W_D_1KI': 0.539459037854024, 'J_D_1KI': 0.004228762770375437}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..0f83da2
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 124648, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.882070779800415, "TIME_S_1KI": 0.08730240982446902, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1349.5646020889283, "W": 104.56, "J_1KI": 10.827005664663117, "W_1KI": 0.8388421795776908, "W_D": 68.90950000000001, "J_D": 889.4206383669377, "W_D_1KI": 0.5528327771003145, "J_D_1KI": 0.004435151603718587}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..a873f46
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.02689981460571289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.1363, 0.1545, 0.8748,  ..., 0.3034, 0.9828, 0.6600])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.02689981460571289 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '39033', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 3.6170003414154053}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3721, 0.2392, 0.8629,  ..., 0.3806, 0.2264, 0.3147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 3.6170003414154053 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '113311', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 9.544935703277588}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.9963, 0.5687, 0.3573,  ..., 0.5516, 0.0785, 0.7433])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 9.544935703277588 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '124648', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.882070779800415}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4840, 0.8851, 0.1929,  ..., 0.3027, 0.3253, 0.5642])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.882070779800415 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.4840, 0.8851, 0.1929,  ..., 0.3027, 0.3253, 0.5642])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.882070779800415 seconds
+
+[40.11, 39.24, 39.61, 39.27, 39.85, 40.14, 39.37, 40.1, 39.25, 39.11]
+[104.56]
+12.90708303451538
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124648, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.882070779800415, 'TIME_S_1KI': 0.08730240982446902, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.5646020889283, 'W': 104.56}
+[40.11, 39.24, 39.61, 39.27, 39.85, 40.14, 39.37, 40.1, 39.25, 39.11, 39.85, 39.23, 39.63, 39.14, 39.68, 40.45, 40.01, 39.37, 39.58, 39.11]
+713.01
+35.6505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124648, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.882070779800415, 'TIME_S_1KI': 0.08730240982446902, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.5646020889283, 'W': 104.56, 'J_1KI': 10.827005664663117, 'W_1KI': 0.8388421795776908, 'W_D': 68.90950000000001, 'J_D': 889.4206383669377, 'W_D_1KI': 0.5528327771003145, 'J_D_1KI': 0.004435151603718587}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..eb322b1
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122117, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.6493821144104, "TIME_S_1KI": 0.0872063849784256, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1405.5062645673752, "W": 105.55, "J_1KI": 11.509505347882564, "W_1KI": 0.8643350229697749, "W_D": 69.588, "J_D": 926.6354328632353, "W_D_1KI": 0.5698469500560938, "J_D_1KI": 0.0046664014842822355}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..abe0184
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.027940988540649414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.7800, 0.8216, 0.9425,  ..., 0.9095, 0.6124, 0.7757])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.027940988540649414 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37579', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 3.4802803993225098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.1792, 0.6829, 0.8674,  ..., 0.8857, 0.9544, 0.5010])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 3.4802803993225098 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '113375', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 9.748293399810791}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.9918, 0.4166, 0.8271,  ..., 0.2758, 0.1683, 0.2261])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 9.748293399810791 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122117', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.6493821144104}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2449, 0.0211, 0.8597,  ..., 0.1539, 0.4018, 0.0751])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.6493821144104 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2449, 0.0211, 0.8597,  ..., 0.1539, 0.4018, 0.0751])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.6493821144104 seconds
+
+[41.05, 40.38, 40.06, 39.47, 40.24, 39.63, 39.65, 39.45, 39.9, 40.39]
+[105.55]
+13.316023349761963
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122117, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.6493821144104, 'TIME_S_1KI': 0.0872063849784256, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.5062645673752, 'W': 105.55}
+[41.05, 40.38, 40.06, 39.47, 40.24, 39.63, 39.65, 39.45, 39.9, 40.39, 40.87, 39.32, 39.97, 39.77, 39.91, 40.1, 39.85, 39.52, 41.22, 39.29]
+719.24
+35.962
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122117, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.6493821144104, 'TIME_S_1KI': 0.0872063849784256, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.5062645673752, 'W': 105.55, 'J_1KI': 11.509505347882564, 'W_1KI': 0.8643350229697749, 'W_D': 69.588, 'J_D': 926.6354328632353, 'W_D_1KI': 0.5698469500560938, 'J_D_1KI': 0.0046664014842822355}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..7429512
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129175, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 11.496757745742798, "TIME_S_1KI": 0.0890014147144788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1509.4191314578056, "W": 104.25, "J_1KI": 11.685071658276026, "W_1KI": 0.80704470679311, "W_D": 68.373, "J_D": 989.9617676274777, "W_D_1KI": 0.5293052061157345, "J_D_1KI": 0.004097582396870404}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..3079a7d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.028039216995239258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.3154, 0.4372, 0.6280,  ..., 0.2119, 0.0302, 0.4417])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.028039216995239258 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37447', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 3.043867349624634}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.9312, 0.0191, 0.4668,  ..., 0.5502, 0.1701, 0.7904])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 3.043867349624634 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129175', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 11.496757745742798}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.3616, 0.7976, 0.8715,  ..., 0.1924, 0.7424, 0.0768])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 11.496757745742798 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.3616, 0.7976, 0.8715,  ..., 0.1924, 0.7424, 0.0768])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 11.496757745742798 seconds
+
+[40.03, 39.45, 40.1, 40.64, 40.0, 39.56, 39.33, 39.22, 39.24, 39.3]
+[104.25]
+14.478840589523315
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129175, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 11.496757745742798, 'TIME_S_1KI': 0.0890014147144788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.4191314578056, 'W': 104.25}
+[40.03, 39.45, 40.1, 40.64, 40.0, 39.56, 39.33, 39.22, 39.24, 39.3, 40.19, 39.37, 39.54, 39.28, 39.32, 44.77, 39.55, 39.28, 39.51, 39.24]
+717.54
+35.876999999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129175, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 11.496757745742798, 'TIME_S_1KI': 0.0890014147144788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.4191314578056, 'W': 104.25, 'J_1KI': 11.685071658276026, 'W_1KI': 0.80704470679311, 'W_D': 68.373, 'J_D': 989.9617676274777, 'W_D_1KI': 0.5293052061157345, 'J_D_1KI': 0.004097582396870404}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..ee00d05
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 119218, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.408023834228516, "TIME_S_1KI": 0.08730245293687627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1334.6079625511168, "W": 104.82, "J_1KI": 11.194685052182697, "W_1KI": 0.8792296465298864, "W_D": 69.02749999999999, "J_D": 878.884288637638, "W_D_1KI": 0.579002331862638, "J_D_1KI": 0.004856668723369274}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..c85480e
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.02867746353149414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.6240, 0.5141, 0.7011,  ..., 0.5180, 0.0910, 0.3078])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.02867746353149414 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36614', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 3.22471284866333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.8117, 0.1193, 0.9490,  ..., 0.3605, 0.3665, 0.8073])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 3.22471284866333 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '119218', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.408023834228516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4188, 0.0252, 0.5651,  ..., 0.9342, 0.6089, 0.7497])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.408023834228516 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4188, 0.0252, 0.5651,  ..., 0.9342, 0.6089, 0.7497])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.408023834228516 seconds
+
+[40.18, 39.58, 40.1, 39.73, 39.46, 39.77, 39.41, 39.32, 39.48, 39.48]
+[104.82]
+12.732378959655762
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.408023834228516, 'TIME_S_1KI': 0.08730245293687627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.6079625511168, 'W': 104.82}
+[40.18, 39.58, 40.1, 39.73, 39.46, 39.77, 39.41, 39.32, 39.48, 39.48, 42.99, 39.89, 39.53, 39.6, 39.67, 39.64, 39.89, 39.62, 39.62, 40.43]
+715.85
+35.792500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.408023834228516, 'TIME_S_1KI': 0.08730245293687627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.6079625511168, 'W': 104.82, 'J_1KI': 11.194685052182697, 'W_1KI': 0.8792296465298864, 'W_D': 69.02749999999999, 'J_D': 878.884288637638, 'W_D_1KI': 0.579002331862638, 'J_D_1KI': 0.004856668723369274}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..989c877
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122135, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.492765665054321, "TIME_S_1KI": 0.08591121025958423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1348.5001153278351, "W": 104.98, "J_1KI": 11.041062065156058, "W_1KI": 0.859540672206984, "W_D": 68.86574999999999, "J_D": 884.6015604604481, "W_D_1KI": 0.5638494289106316, "J_D_1KI": 0.004616608088677542}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..2c251fe
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_040.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.028779983520507812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.7202, 0.2273, 0.0912,  ..., 0.5117, 0.0438, 0.0652])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.028779983520507812 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36483', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 3.136458396911621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.6196, 0.3813, 0.9165,  ..., 0.6485, 0.6202, 0.1775])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 3.136458396911621 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122135', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.492765665054321}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.1673, 0.0575, 0.9445,  ..., 0.6838, 0.0058, 0.2294])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.492765665054321 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.1673, 0.0575, 0.9445,  ..., 0.6838, 0.0058, 0.2294])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.492765665054321 seconds
+
+[40.45, 39.57, 39.56, 39.67, 39.55, 39.35, 39.42, 39.13, 45.25, 39.21]
+[104.98]
+12.8453049659729
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122135, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.492765665054321, 'TIME_S_1KI': 0.08591121025958423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.5001153278351, 'W': 104.98}
+[40.45, 39.57, 39.56, 39.67, 39.55, 39.35, 39.42, 39.13, 45.25, 39.21, 40.11, 39.42, 39.29, 39.11, 39.53, 44.61, 39.57, 39.72, 39.86, 39.58]
+722.2850000000001
+36.114250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122135, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.492765665054321, 'TIME_S_1KI': 0.08591121025958423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.5001153278351, 'W': 104.98, 'J_1KI': 11.041062065156058, 'W_1KI': 0.859540672206984, 'W_D': 68.86574999999999, 'J_D': 884.6015604604481, 'W_D_1KI': 0.5638494289106316, 'J_D_1KI': 0.004616608088677542}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..df8b678
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123262, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.604638814926147, "TIME_S_1KI": 0.08603331776967879, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1498.7498827266693, "W": 104.66, "J_1KI": 12.159058612765241, "W_1KI": 0.8490856873975758, "W_D": 69.0005, "J_D": 988.099477193594, "W_D_1KI": 0.5597872823741299, "J_D_1KI": 0.004541442475167772}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..793de8a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.022446870803833008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7240, 0.4288, 0.8026,  ..., 0.9422, 0.3141, 0.8470])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.022446870803833008 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46777', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.238167762756348}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6263, 0.4951, 0.8826,  ..., 0.4290, 0.9805, 0.9078])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 4.238167762756348 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '115889', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.87186861038208}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.6856, 0.8550, 0.0184,  ..., 0.3333, 0.3446, 0.1554])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 9.87186861038208 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123262', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.604638814926147}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7577, 0.3700, 0.4727,  ..., 0.6682, 0.7731, 0.8829])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.604638814926147 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.7577, 0.3700, 0.4727,  ..., 0.6682, 0.7731, 0.8829])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.604638814926147 seconds
+
+[40.75, 39.54, 39.62, 39.96, 39.38, 39.41, 39.91, 39.55, 39.67, 39.71]
+[104.66]
+14.320178508758545
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123262, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.604638814926147, 'TIME_S_1KI': 0.08603331776967879, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1498.7498827266693, 'W': 104.66}
+[40.75, 39.54, 39.62, 39.96, 39.38, 39.41, 39.91, 39.55, 39.67, 39.71, 39.94, 39.22, 39.33, 39.47, 39.36, 39.85, 40.29, 39.23, 39.64, 39.12]
+713.1899999999999
+35.659499999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123262, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.604638814926147, 'TIME_S_1KI': 0.08603331776967879, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1498.7498827266693, 'W': 104.66, 'J_1KI': 12.159058612765241, 'W_1KI': 0.8490856873975758, 'W_D': 69.0005, 'J_D': 988.099477193594, 'W_D_1KI': 0.5597872823741299, 'J_D_1KI': 0.004541442475167772}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..38a8935
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117787, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.421326398849487, "TIME_S_1KI": 0.08847603214997825, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1389.3025615692138, "W": 103.6, "J_1KI": 11.79504157138915, "W_1KI": 0.8795537707896457, "W_D": 68.03399999999999, "J_D": 912.3533829517363, "W_D_1KI": 0.5776019424894088, "J_D_1KI": 0.004903783460733432}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..1e27e71
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.028438329696655273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.5989, 0.7672, 0.6938,  ..., 0.5834, 0.2482, 0.6886])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.028438329696655273 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36921', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 3.2912728786468506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.8959, 0.9802, 0.0892,  ..., 0.7733, 0.8889, 0.9217])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 3.2912728786468506 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117787', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.421326398849487}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.2291, 0.3797, 0.8941,  ..., 0.7234, 0.2991, 0.8486])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.421326398849487 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.2291, 0.3797, 0.8941,  ..., 0.7234, 0.2991, 0.8486])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.421326398849487 seconds
+
+[39.84, 39.19, 39.62, 39.26, 39.54, 40.45, 39.65, 39.62, 39.64, 39.53]
+[103.6]
+13.410256385803223
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117787, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.421326398849487, 'TIME_S_1KI': 0.08847603214997825, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1389.3025615692138, 'W': 103.6}
+[39.84, 39.19, 39.62, 39.26, 39.54, 40.45, 39.65, 39.62, 39.64, 39.53, 39.93, 39.8, 39.84, 39.1, 39.42, 39.24, 39.2, 39.22, 39.2, 39.36]
+711.3199999999999
+35.565999999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117787, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.421326398849487, 'TIME_S_1KI': 0.08847603214997825, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1389.3025615692138, 'W': 103.6, 'J_1KI': 11.79504157138915, 'W_1KI': 0.8795537707896457, 'W_D': 68.03399999999999, 'J_D': 912.3533829517363, 'W_D_1KI': 0.5776019424894088, 'J_D_1KI': 0.004903783460733432}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..d19ae4f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 115993, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.341620922088623, "TIME_S_1KI": 0.0977784945823336, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1311.9969903469087, "W": 105.11000000000001, "J_1KI": 11.311001442732826, "W_1KI": 0.906175372651798, "W_D": 68.70625000000001, "J_D": 857.6005443632604, "W_D_1KI": 0.5923310027329237, "J_D_1KI": 0.005106609905191897}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..9ccce60
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.028304576873779297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.7892, 0.3566, 0.7127,  ..., 0.2432, 0.3071, 0.5377])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.028304576873779297 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37096', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 3.358018398284912}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.1850, 0.6800, 0.5942,  ..., 0.9886, 0.1400, 0.4140])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 3.358018398284912 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '115993', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.341620922088623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.2569, 0.5049, 0.9878,  ..., 0.1193, 0.2877, 0.6010])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 11.341620922088623 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.2569, 0.5049, 0.9878,  ..., 0.1193, 0.2877, 0.6010])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 11.341620922088623 seconds
+
+[40.22, 39.34, 39.44, 39.29, 39.78, 39.4, 39.25, 39.27, 39.2, 39.57]
+[105.11]
+12.482132911682129
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115993, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.341620922088623, 'TIME_S_1KI': 0.0977784945823336, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1311.9969903469087, 'W': 105.11000000000001}
+[40.22, 39.34, 39.44, 39.29, 39.78, 39.4, 39.25, 39.27, 39.2, 39.57, 40.43, 40.81, 39.88, 39.99, 39.29, 39.57, 39.3, 49.45, 45.11, 39.19]
+728.075
+36.40375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115993, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.341620922088623, 'TIME_S_1KI': 0.0977784945823336, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1311.9969903469087, 'W': 105.11000000000001, 'J_1KI': 11.311001442732826, 'W_1KI': 0.906175372651798, 'W_D': 68.70625000000001, 'J_D': 857.6005443632604, 'W_D_1KI': 0.5923310027329237, 'J_D_1KI': 0.005106609905191897}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..11cd5b5
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123317, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.896259069442749, "TIME_S_1KI": 0.08835974820537922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1425.307745819092, "W": 105.12, "J_1KI": 11.558079955067766, "W_1KI": 0.8524372146581575, "W_D": 69.44200000000001, "J_D": 941.5546088771821, "W_D_1KI": 0.5631178183056677, "J_D_1KI": 0.004566424891180192}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..08b262c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.028558731079101562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.9395, 0.8605, 0.5079,  ..., 0.7432, 0.1105, 0.2893])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.028558731079101562 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36766', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 3.1304874420166016}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.0101, 0.3306, 0.5019,  ..., 0.2731, 0.4830, 0.5172])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 3.1304874420166016 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123317', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.896259069442749}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.2571, 0.5592, 0.2032,  ..., 0.0835, 0.1211, 0.1576])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.896259069442749 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.2571, 0.5592, 0.2032,  ..., 0.0835, 0.1211, 0.1576])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.896259069442749 seconds
+
+[40.57, 39.93, 40.0, 40.02, 39.47, 39.92, 39.42, 39.38, 39.4, 39.2]
+[105.12]
+13.558863639831543
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.896259069442749, 'TIME_S_1KI': 0.08835974820537922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1425.307745819092, 'W': 105.12}
+[40.57, 39.93, 40.0, 40.02, 39.47, 39.92, 39.42, 39.38, 39.4, 39.2, 41.02, 39.3, 39.33, 39.74, 39.48, 39.25, 39.48, 39.96, 39.4, 39.37]
+713.5600000000001
+35.678000000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.896259069442749, 'TIME_S_1KI': 0.08835974820537922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1425.307745819092, 'W': 105.12, 'J_1KI': 11.558079955067766, 'W_1KI': 0.8524372146581575, 'W_D': 69.44200000000001, 'J_D': 941.5546088771821, 'W_D_1KI': 0.5631178183056677, 'J_D_1KI': 0.004566424891180192}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..02301d3
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 119305, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.269909381866455, "TIME_S_1KI": 0.08608113140158799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1369.542292098999, "W": 104.99, "J_1KI": 11.479336927194996, "W_1KI": 0.8800134110054063, "W_D": 69.32875, "J_D": 904.3590359401703, "W_D_1KI": 0.5811051506642639, "J_D_1KI": 0.004870752698246209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..a82eb47
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.05011296272277832}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1460, 0.2426, 0.2871,  ..., 0.8209, 0.8175, 0.1827])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.05011296272277832 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20952', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 1.843977928161621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.4486, 0.9899, 0.6798,  ..., 0.6606, 0.4190, 0.7337])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 1.843977928161621 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '119305', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.269909381866455}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1419, 0.9123, 0.3177,  ..., 0.8018, 0.7036, 0.8347])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.269909381866455 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.1419, 0.9123, 0.3177,  ..., 0.8018, 0.7036, 0.8347])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.269909381866455 seconds
+
+[40.89, 39.97, 39.48, 39.83, 39.9, 39.69, 39.45, 39.42, 39.72, 39.16]
+[104.99]
+13.044502258300781
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119305, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.269909381866455, 'TIME_S_1KI': 0.08608113140158799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1369.542292098999, 'W': 104.99}
+[40.89, 39.97, 39.48, 39.83, 39.9, 39.69, 39.45, 39.42, 39.72, 39.16, 39.96, 39.46, 39.25, 39.6, 39.18, 39.57, 39.73, 39.28, 40.1, 39.18]
+713.2249999999999
+35.661249999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119305, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.269909381866455, 'TIME_S_1KI': 0.08608113140158799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1369.542292098999, 'W': 104.99, 'J_1KI': 11.479336927194996, 'W_1KI': 0.8800134110054063, 'W_D': 69.32875, 'J_D': 904.3590359401703, 'W_D_1KI': 0.5811051506642639, 'J_D_1KI': 0.004870752698246209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..6575eec
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129583, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 12.000130653381348, "TIME_S_1KI": 0.09260574807946526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1437.4487916231155, "W": 104.75, "J_1KI": 11.09288094598146, "W_1KI": 0.8083622080056797, "W_D": 68.6745, "J_D": 942.3969168527126, "W_D_1KI": 0.5299653503931843, "J_D_1KI": 0.004089775282198933}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..4eeef4f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.027138948440551758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5244, 0.3660, 0.9222,  ..., 0.4445, 0.2345, 0.4500])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.027138948440551758 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38689', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 3.3633055686950684}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.9505, 0.6704, 0.1707,  ..., 0.6835, 0.9333, 0.3906])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 3.3633055686950684 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120784', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 9.786993265151978}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.4438, 0.5060, 0.3649,  ..., 0.3540, 0.3863, 0.0532])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 9.786993265151978 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129583', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 12.000130653381348}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5405, 0.8603, 0.5073,  ..., 0.3733, 0.9795, 0.3129])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 12.000130653381348 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.5405, 0.8603, 0.5073,  ..., 0.3733, 0.9795, 0.3129])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 12.000130653381348 seconds
+
+[42.83, 40.0, 40.35, 39.54, 39.49, 39.76, 39.66, 39.49, 39.56, 39.48]
+[104.75]
+13.72266149520874
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129583, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 12.000130653381348, 'TIME_S_1KI': 0.09260574807946526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.4487916231155, 'W': 104.75}
+[42.83, 40.0, 40.35, 39.54, 39.49, 39.76, 39.66, 39.49, 39.56, 39.48, 40.08, 40.02, 39.86, 39.87, 39.43, 39.62, 45.0, 39.31, 39.62, 39.47]
+721.51
+36.0755
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129583, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 12.000130653381348, 'TIME_S_1KI': 0.09260574807946526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.4487916231155, 'W': 104.75, 'J_1KI': 11.09288094598146, 'W_1KI': 0.8083622080056797, 'W_D': 68.6745, 'J_D': 942.3969168527126, 'W_D_1KI': 0.5299653503931843, 'J_D_1KI': 0.004089775282198933}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..52bd678
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117871, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.46876573562622, "TIME_S_1KI": 0.08881544854651459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.9325737524032, "W": 105.46, "J_1KI": 11.749561586415686, "W_1KI": 0.8947069253675628, "W_D": 69.29524999999998, "J_D": 910.0061533407567, "W_D_1KI": 0.5878905752899355, "J_D_1KI": 0.004987576038974264}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..edf1a35
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.029708385467529297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.7657, 0.6949, 0.8038,  ..., 0.0810, 0.5586, 0.1756])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.029708385467529297 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35343', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 3.1483521461486816}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.7560, 0.3404, 0.1077,  ..., 0.6219, 0.8552, 0.3773])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 3.1483521461486816 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117871', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.46876573562622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9284, 0.7225, 0.5215,  ..., 0.9584, 0.4959, 0.3353])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.46876573562622 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9284, 0.7225, 0.5215,  ..., 0.9584, 0.4959, 0.3353])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.46876573562622 seconds
+
+[40.06, 40.12, 39.51, 39.49, 39.39, 39.9, 39.43, 39.81, 44.85, 39.81]
+[105.46]
+13.132302045822144
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117871, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.46876573562622, 'TIME_S_1KI': 0.08881544854651459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.9325737524032, 'W': 105.46}
+[40.06, 40.12, 39.51, 39.49, 39.39, 39.9, 39.43, 39.81, 44.85, 39.81, 40.59, 39.7, 39.35, 39.63, 39.73, 44.15, 39.7, 39.24, 39.48, 39.17]
+723.2950000000001
+36.164750000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117871, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.46876573562622, 'TIME_S_1KI': 0.08881544854651459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.9325737524032, 'W': 105.46, 'J_1KI': 11.749561586415686, 'W_1KI': 0.8947069253675628, 'W_D': 69.29524999999998, 'J_D': 910.0061533407567, 'W_D_1KI': 0.5878905752899355, 'J_D_1KI': 0.004987576038974264}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..6b3c59d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 113085, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.022817373275757, "TIME_S_1KI": 0.08863082967038738, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1346.034346253872, "W": 105.09, "J_1KI": 11.90285489900404, "W_1KI": 0.9293009682981829, "W_D": 69.12225000000001, "J_D": 885.3451573922039, "W_D_1KI": 0.6112415439713491, "J_D_1KI": 0.005405151381450671}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..338a7e7
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_080.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.0285036563873291}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.3486, 0.2746, 0.8325,  ..., 0.8912, 0.4608, 0.3383])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.0285036563873291 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36837', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 3.420318365097046}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.2942, 0.6821, 0.6704,  ..., 0.1315, 0.6588, 0.9862])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 3.420318365097046 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '113085', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.022817373275757}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5127, 0.2694, 0.5905,  ..., 0.2032, 0.1394, 0.4417])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.022817373275757 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5127, 0.2694, 0.5905,  ..., 0.2032, 0.1394, 0.4417])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.022817373275757 seconds
+
+[40.32, 39.74, 39.76, 39.18, 39.81, 39.21, 39.37, 39.14, 39.25, 44.53]
+[105.09]
+12.808396100997925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 113085, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.022817373275757, 'TIME_S_1KI': 0.08863082967038738, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1346.034346253872, 'W': 105.09}
+[40.32, 39.74, 39.76, 39.18, 39.81, 39.21, 39.37, 39.14, 39.25, 44.53, 39.94, 39.5, 39.3, 39.27, 39.61, 39.73, 45.0, 39.54, 39.63, 39.84]
+719.3549999999999
+35.967749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 113085, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.022817373275757, 'TIME_S_1KI': 0.08863082967038738, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1346.034346253872, 'W': 105.09, 'J_1KI': 11.90285489900404, 'W_1KI': 0.9293009682981829, 'W_D': 69.12225000000001, 'J_D': 885.3451573922039, 'W_D_1KI': 0.6112415439713491, 'J_D_1KI': 0.005405151381450671}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..dac1835
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121444, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.505080699920654, "TIME_S_1KI": 0.08650143852245194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1336.2899490594864, "W": 106.5, "J_1KI": 11.003342685184005, "W_1KI": 0.8769473996245183, "W_D": 70.737, "J_D": 887.5600199682711, "W_D_1KI": 0.5824659925562399, "J_D_1KI": 0.004796169366590691}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..c674a30
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.027776479721069336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7671, 0.5798, 0.8911,  ..., 0.4919, 0.5874, 0.4864])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.027776479721069336 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37801', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 3.268259286880493}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7766, 0.5291, 0.5743,  ..., 0.9245, 0.7238, 0.3581])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 3.268259286880493 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121444', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.505080699920654}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.5279, 0.9609, 0.7308,  ..., 0.9106, 0.1679, 0.0523])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.505080699920654 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.5279, 0.9609, 0.7308,  ..., 0.9106, 0.1679, 0.0523])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.505080699920654 seconds
+
+[40.27, 40.61, 39.36, 39.54, 39.41, 39.69, 39.72, 39.77, 40.14, 39.67]
+[106.5]
+12.54732346534729
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.505080699920654, 'TIME_S_1KI': 0.08650143852245194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1336.2899490594864, 'W': 106.5}
+[40.27, 40.61, 39.36, 39.54, 39.41, 39.69, 39.72, 39.77, 40.14, 39.67, 41.71, 39.67, 39.95, 39.27, 39.45, 39.23, 39.37, 40.22, 39.21, 39.65]
+715.26
+35.763
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.505080699920654, 'TIME_S_1KI': 0.08650143852245194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1336.2899490594864, 'W': 106.5, 'J_1KI': 11.003342685184005, 'W_1KI': 0.8769473996245183, 'W_D': 70.737, 'J_D': 887.5600199682711, 'W_D_1KI': 0.5824659925562399, 'J_D_1KI': 0.004796169366590691}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..5fa3772
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 125348, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 11.665527820587158, "TIME_S_1KI": 0.09306512924487952, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1422.9701265430451, "W": 106.34, "J_1KI": 11.352156608346725, "W_1KI": 0.8483581708523471, "W_D": 70.38125000000001, "J_D": 941.7943973928691, "W_D_1KI": 0.5614868206911958, "J_D_1KI": 0.004479423849532468}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..3e65e7b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.028897523880004883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.9373, 0.7320, 0.7825,  ..., 0.9064, 0.8274, 0.7830])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.028897523880004883 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36335', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 3.0436627864837646}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.5085, 0.7667, 0.7009,  ..., 0.3763, 0.9758, 0.8419])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 3.0436627864837646 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '125348', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 11.665527820587158}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4521, 0.9553, 0.5191,  ..., 0.2710, 0.2446, 0.7581])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 11.665527820587158 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4521, 0.9553, 0.5191,  ..., 0.2710, 0.2446, 0.7581])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 11.665527820587158 seconds
+
+[39.91, 39.4, 39.42, 39.33, 40.79, 39.74, 39.46, 39.9, 39.32, 39.28]
+[106.34]
+13.381325244903564
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125348, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 11.665527820587158, 'TIME_S_1KI': 0.09306512924487952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1422.9701265430451, 'W': 106.34}
+[39.91, 39.4, 39.42, 39.33, 40.79, 39.74, 39.46, 39.9, 39.32, 39.28, 40.08, 39.29, 39.67, 39.26, 39.41, 39.72, 42.19, 42.94, 39.79, 39.82]
+719.175
+35.958749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125348, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 11.665527820587158, 'TIME_S_1KI': 0.09306512924487952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1422.9701265430451, 'W': 106.34, 'J_1KI': 11.352156608346725, 'W_1KI': 0.8483581708523471, 'W_D': 70.38125000000001, 'J_D': 941.7943973928691, 'W_D_1KI': 0.5614868206911958, 'J_D_1KI': 0.004479423849532468}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..336d888
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123705, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.90046238899231, "TIME_S_1KI": 0.08811658695276917, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1428.9193895983694, "W": 106.12999999999998, "J_1KI": 11.5510237225526, "W_1KI": 0.85792813548361, "W_D": 69.78774999999999, "J_D": 939.6124482374786, "W_D_1KI": 0.5641465583444485, "J_D_1KI": 0.00456041840139403}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..ea67830
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.0280454158782959}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6080, 0.8018, 0.6987,  ..., 0.3141, 0.8100, 0.8933])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.0280454158782959 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37439', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 3.1777875423431396}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.8331, 0.2134, 0.1814,  ..., 0.8302, 0.3682, 0.5485])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 3.1777875423431396 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123705', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.90046238899231}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6564, 0.4288, 0.1493,  ..., 0.5365, 0.7866, 0.0555])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.90046238899231 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.6564, 0.4288, 0.1493,  ..., 0.5365, 0.7866, 0.0555])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.90046238899231 seconds
+
+[45.18, 51.54, 39.34, 39.19, 39.19, 39.24, 39.49, 39.13, 40.19, 40.24]
+[106.13]
+13.46385931968689
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123705, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.90046238899231, 'TIME_S_1KI': 0.08811658695276917, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1428.9193895983694, 'W': 106.12999999999998}
+[45.18, 51.54, 39.34, 39.19, 39.19, 39.24, 39.49, 39.13, 40.19, 40.24, 39.83, 39.8, 39.94, 39.19, 39.68, 40.58, 39.51, 39.26, 39.27, 39.36]
+726.845
+36.34225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123705, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.90046238899231, 'TIME_S_1KI': 0.08811658695276917, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1428.9193895983694, 'W': 106.12999999999998, 'J_1KI': 11.5510237225526, 'W_1KI': 0.85792813548361, 'W_D': 69.78774999999999, 'J_D': 939.6124482374786, 'W_D_1KI': 0.5641465583444485, 'J_D_1KI': 0.00456041840139403}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..f98617b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 118070, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.865741491317749, "TIME_S_1KI": 0.09202796215226348, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1345.6706349611281, "W": 106.1, "J_1KI": 11.397227364793158, "W_1KI": 0.8986194630304056, "W_D": 69.83699999999999, "J_D": 885.7455243523119, "W_D_1KI": 0.5914881002794952, "J_D_1KI": 0.005009639199453673}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..e4a611d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.0224916934967041}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.7130, 0.6339, 0.0628,  ..., 0.3904, 0.4507, 0.5139])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.0224916934967041 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46683', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 4.151525974273682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.8270, 0.0943, 0.6527,  ..., 0.3375, 0.9215, 0.2050])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 4.151525974273682 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '118070', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.865741491317749}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2092, 0.4711, 0.3921,  ..., 0.4964, 0.3549, 0.3565])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.865741491317749 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.2092, 0.4711, 0.3921,  ..., 0.4964, 0.3549, 0.3565])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.865741491317749 seconds
+
+[40.15, 41.28, 39.53, 40.04, 39.94, 39.32, 39.34, 39.4, 45.11, 39.27]
+[106.1]
+12.683040857315063
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 118070, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.865741491317749, 'TIME_S_1KI': 0.09202796215226348, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.6706349611281, 'W': 106.1}
+[40.15, 41.28, 39.53, 40.04, 39.94, 39.32, 39.34, 39.4, 45.11, 39.27, 40.19, 39.43, 39.45, 39.32, 39.36, 45.07, 39.89, 39.39, 39.84, 39.49]
+725.26
+36.263
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 118070, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.865741491317749, 'TIME_S_1KI': 0.09202796215226348, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.6706349611281, 'W': 106.1, 'J_1KI': 11.397227364793158, 'W_1KI': 0.8986194630304056, 'W_D': 69.83699999999999, 'J_D': 885.7455243523119, 'W_D_1KI': 0.5914881002794952, 'J_D_1KI': 0.005009639199453673}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..7178369
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122486, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.397178411483765, "TIME_S_1KI": 0.08488462690824881, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1412.024871377945, "W": 106.03999999999999, "J_1KI": 11.528051135459929, "W_1KI": 0.8657315938148032, "W_D": 70.02574999999999, "J_D": 932.4603983109591, "W_D_1KI": 0.5717041131231323, "J_D_1KI": 0.004667505781257714}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..4c2c51c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.028494596481323242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.0686, 0.0328, 0.2033,  ..., 0.1197, 0.3793, 0.1423])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.028494596481323242 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36849', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 3.158832311630249}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.1470, 0.7207, 0.2182,  ..., 0.8892, 0.7915, 0.0167])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 3.158832311630249 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122486', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.397178411483765}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.7336, 0.8024, 0.7188,  ..., 0.1561, 0.7889, 0.4305])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.397178411483765 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.7336, 0.8024, 0.7188,  ..., 0.1561, 0.7889, 0.4305])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.397178411483765 seconds
+
+[50.54, 39.93, 39.56, 39.45, 39.38, 39.37, 39.74, 39.95, 39.8, 41.73]
+[106.04]
+13.315964460372925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122486, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.397178411483765, 'TIME_S_1KI': 0.08488462690824881, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.024871377945, 'W': 106.03999999999999}
+[50.54, 39.93, 39.56, 39.45, 39.38, 39.37, 39.74, 39.95, 39.8, 41.73, 39.97, 39.3, 39.55, 39.68, 39.55, 39.47, 40.65, 39.24, 39.69, 39.71]
+720.2850000000001
+36.014250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122486, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.397178411483765, 'TIME_S_1KI': 0.08488462690824881, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.024871377945, 'W': 106.03999999999999, 'J_1KI': 11.528051135459929, 'W_1KI': 0.8657315938148032, 'W_D': 70.02574999999999, 'J_D': 932.4603983109591, 'W_D_1KI': 0.5717041131231323, 'J_D_1KI': 0.004667505781257714}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..3206458
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117404, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.663360834121704, "TIME_S_1KI": 0.09082621404825819, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1379.33864120245, "W": 105.65, "J_1KI": 11.748651163524666, "W_1KI": 0.8998841606759566, "W_D": 69.403, "J_D": 906.1073328478337, "W_D_1KI": 0.5911468093080304, "J_D_1KI": 0.005035150500051365}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..e4cdd2c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.02773261070251465}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.0593, 0.3688, 0.0297,  ..., 0.9361, 0.0859, 0.1215])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.02773261070251465 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37861', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 3.386080265045166}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5515, 0.8867, 0.2110,  ..., 0.2273, 0.7773, 0.5836])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 3.386080265045166 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117404', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.663360834121704}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.4713, 0.3034, 0.9385,  ..., 0.1912, 0.4405, 0.1616])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.663360834121704 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.4713, 0.3034, 0.9385,  ..., 0.1912, 0.4405, 0.1616])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.663360834121704 seconds
+
+[43.3, 39.9, 39.77, 39.36, 40.44, 39.87, 39.42, 39.38, 39.69, 44.78]
+[105.65]
+13.055737257003784
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117404, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.663360834121704, 'TIME_S_1KI': 0.09082621404825819, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.33864120245, 'W': 105.65}
+[43.3, 39.9, 39.77, 39.36, 40.44, 39.87, 39.42, 39.38, 39.69, 44.78, 42.65, 39.88, 39.96, 39.28, 39.32, 39.4, 45.11, 39.73, 39.28, 39.57]
+724.94
+36.247
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117404, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.663360834121704, 'TIME_S_1KI': 0.09082621404825819, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.33864120245, 'W': 105.65, 'J_1KI': 11.748651163524666, 'W_1KI': 0.8998841606759566, 'W_D': 69.403, 'J_D': 906.1073328478337, 'W_D_1KI': 0.5911468093080304, 'J_D_1KI': 0.005035150500051365}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..a790206
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 124105, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.59231972694397, "TIME_S_1KI": 0.08534966139111212, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4742.8229215764995, "W": 99.33, "J_1KI": 38.21621144656943, "W_1KI": 0.8003706538817936, "W_D": 61.29225, "J_D": 2926.5910421322587, "W_D_1KI": 0.49387413883405185, "J_D_1KI": 0.003979486232094209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..c33a124
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.02862715721130371}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.8751, 0.4686, 0.5237,  ..., 0.8724, 0.2861, 0.0906])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.02862715721130371 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36678', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 3.1031546592712402}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.7984, 0.5145, 0.9517,  ..., 0.9171, 0.3029, 0.5667])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 3.1031546592712402 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '124105', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.59231972694397}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.9062, 0.9528, 0.4977,  ..., 0.5925, 0.1787, 0.9051])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.59231972694397 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.9062, 0.9528, 0.4977,  ..., 0.5925, 0.1787, 0.9051])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.59231972694397 seconds
+
+[40.47, 39.18, 39.36, 39.46, 39.2, 39.2, 39.25, 49.28, 64.87, 63.39]
+[99.33]
+47.74814176559448
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.59231972694397, 'TIME_S_1KI': 0.08534966139111212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4742.8229215764995, 'W': 99.33}
+[40.47, 39.18, 39.36, 39.46, 39.2, 39.2, 39.25, 49.28, 64.87, 63.39, 40.46, 39.73, 40.14, 40.06, 39.55, 39.41, 40.08, 39.85, 40.03, 39.89]
+760.7549999999999
+38.037749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.59231972694397, 'TIME_S_1KI': 0.08534966139111212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4742.8229215764995, 'W': 99.33, 'J_1KI': 38.21621144656943, 'W_1KI': 0.8003706538817936, 'W_D': 61.29225, 'J_D': 2926.5910421322587, 'W_D_1KI': 0.49387413883405185, 'J_D_1KI': 0.003979486232094209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..784bf08
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122857, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.686526536941528, "TIME_S_1KI": 0.08698345667679927, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1494.927384853363, "W": 106.3, "J_1KI": 12.168027746513125, "W_1KI": 0.8652335642250746, "W_D": 70.24249999999999, "J_D": 987.8404217362403, "W_D_1KI": 0.5717419438859812, "J_D_1KI": 0.0046537189080474144}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..6360cf3
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.027823925018310547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1737, 0.7519, 0.3072,  ..., 0.3798, 0.0187, 0.1408])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.027823925018310547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37737', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 3.4002528190612793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.2486, 0.2091, 0.2961,  ..., 0.6999, 0.4583, 0.5901])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 3.4002528190612793 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '116532', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 9.959382057189941}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.5321, 0.8850, 0.1620,  ..., 0.3222, 0.4176, 0.7012])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 9.959382057189941 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122857', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.686526536941528}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.8201, 0.0900, 0.5048,  ..., 0.8013, 0.0849, 0.3500])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.686526536941528 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.8201, 0.0900, 0.5048,  ..., 0.8013, 0.0849, 0.3500])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.686526536941528 seconds
+
+[40.22, 39.39, 40.2, 39.52, 39.44, 40.1, 41.57, 39.97, 39.74, 39.63]
+[106.3]
+14.063286781311035
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122857, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.686526536941528, 'TIME_S_1KI': 0.08698345667679927, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.927384853363, 'W': 106.3}
+[40.22, 39.39, 40.2, 39.52, 39.44, 40.1, 41.57, 39.97, 39.74, 39.63, 40.14, 39.47, 39.48, 39.64, 39.41, 39.48, 39.42, 39.76, 44.74, 39.65]
+721.1500000000001
+36.057500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122857, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.686526536941528, 'TIME_S_1KI': 0.08698345667679927, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.927384853363, 'W': 106.3, 'J_1KI': 12.168027746513125, 'W_1KI': 0.8652335642250746, 'W_D': 70.24249999999999, 'J_D': 987.8404217362403, 'W_D_1KI': 0.5717419438859812, 'J_D_1KI': 0.0046537189080474144}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json
new file mode 100644
index 0000000..cb8197c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 114732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.352796077728271, "TIME_S_1KI": 0.09023459956880618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1152.0286125135422, "W": 84.58, "J_1KI": 10.04104009791115, "W_1KI": 0.7371962486490254, "W_D": 67.46775, "J_D": 918.9498512876629, "W_D_1KI": 0.5880464909528291, "J_D_1KI": 0.005125392139532381}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output
new file mode 100644
index 0000000..5c696d9
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.022900104522705078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.6590, 0.2806, 0.2495,  ..., 0.2832, 0.5182, 0.5123])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 0.022900104522705078 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45851', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 4.196166276931763}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.0852, 0.1228, 0.9411,  ..., 0.5747, 0.5628, 0.7446])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 4.196166276931763 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '114732', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.352796077728271}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5031, 0.9770, 0.1362,  ..., 0.3615, 0.8578, 0.7650])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.352796077728271 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
+       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
+       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=70026, layout=torch.sparse_csr)
+tensor([0.5031, 0.9770, 0.1362,  ..., 0.3615, 0.8578, 0.7650])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_005
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 70026
+Density: 7.111825976492498e-05
+Time: 10.352796077728271 seconds
+
+[19.42, 18.64, 18.8, 18.86, 18.73, 18.71, 18.65, 18.39, 18.57, 18.62]
+[84.58]
+13.620579481124878
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.352796077728271, 'TIME_S_1KI': 0.09023459956880618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.0286125135422, 'W': 84.58}
+[19.42, 18.64, 18.8, 18.86, 18.73, 18.71, 18.65, 18.39, 18.57, 18.62, 18.93, 18.74, 18.93, 19.06, 18.58, 18.76, 19.16, 18.8, 22.96, 18.84]
+342.245
+17.11225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.352796077728271, 'TIME_S_1KI': 0.09023459956880618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.0286125135422, 'W': 84.58, 'J_1KI': 10.04104009791115, 'W_1KI': 0.7371962486490254, 'W_D': 67.46775, 'J_D': 918.9498512876629, 'W_D_1KI': 0.5880464909528291, 'J_D_1KI': 0.005125392139532381}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..57d763a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111702, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.38921594619751, "TIME_S_1KI": 0.0930083252421399, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1163.0663761782646, "W": 84.59, "J_1KI": 10.412225172138946, "W_1KI": 0.757282770227928, "W_D": 67.78525, "J_D": 932.0102266915442, "W_D_1KI": 0.6068400744838948, "J_D_1KI": 0.005432669732716467}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..53d1471
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_010.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.02340102195739746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.1781, 0.4201, 0.5614,  ..., 0.5216, 0.3543, 0.1043])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.02340102195739746 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44869', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 4.21767783164978}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.5193, 0.5407, 0.4792,  ..., 0.6482, 0.1762, 0.8992])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 4.21767783164978 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '111702', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.38921594619751}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.7487, 0.6697, 0.3733,  ..., 0.1553, 0.3372, 0.4885])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.38921594619751 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    28,  ..., 74993, 74993, 74994]),
+       col_indices=tensor([ 1040,  2020,  2054,  ...,   160,   160, 12170]),
+       values=tensor([1., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=74994, layout=torch.sparse_csr)
+tensor([0.7487, 0.6697, 0.3733,  ..., 0.1553, 0.3372, 0.4885])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.38921594619751 seconds
+
+[18.75, 18.59, 18.5, 18.71, 18.64, 18.9, 18.39, 18.48, 18.43, 18.71]
+[84.59]
+13.749454736709595
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111702, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.38921594619751, 'TIME_S_1KI': 0.0930083252421399, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.0663761782646, 'W': 84.59}
+[18.75, 18.59, 18.5, 18.71, 18.64, 18.9, 18.39, 18.48, 18.43, 18.71, 19.07, 18.82, 18.83, 18.93, 18.66, 18.87, 18.51, 18.7, 18.6, 18.54]
+336.095
+16.804750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111702, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.38921594619751, 'TIME_S_1KI': 0.0930083252421399, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.0663761782646, 'W': 84.59, 'J_1KI': 10.412225172138946, 'W_1KI': 0.757282770227928, 'W_D': 67.78525, 'J_D': 932.0102266915442, 'W_D_1KI': 0.6068400744838948, 'J_D_1KI': 0.005432669732716467}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json
new file mode 100644
index 0000000..9a89d63
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 107283, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.024356126785278, "TIME_S_1KI": 0.09343843970419617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1167.401763653755, "W": 84.69, "J_1KI": 10.881516770166336, "W_1KI": 0.7894074550487962, "W_D": 67.7505, "J_D": 933.9007343065739, "W_D_1KI": 0.6315119823271163, "J_D_1KI": 0.0058864124076239135}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output
new file mode 100644
index 0000000..8223898
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024006128311157227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.9343, 0.0575, 0.7588,  ..., 0.4632, 0.2744, 0.8237])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 0.024006128311157227 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43738', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.2806923389434814}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.8682, 0.4247, 0.3572,  ..., 0.4764, 0.2418, 0.9826])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 4.2806923389434814 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '107283', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.024356126785278}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.4742, 0.5931, 0.8061,  ..., 0.3283, 0.2105, 0.2866])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.024356126785278 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
+       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=77124, layout=torch.sparse_csr)
+tensor([0.4742, 0.5931, 0.8061,  ..., 0.3283, 0.2105, 0.2866])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_015
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 77124
+Density: 7.832697378273889e-05
+Time: 10.024356126785278 seconds
+
+[19.22, 18.5, 18.53, 20.77, 19.04, 18.5, 18.59, 19.04, 18.95, 18.38]
+[84.69]
+13.784410953521729
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107283, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.024356126785278, 'TIME_S_1KI': 0.09343843970419617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1167.401763653755, 'W': 84.69}
+[19.22, 18.5, 18.53, 20.77, 19.04, 18.5, 18.59, 19.04, 18.95, 18.38, 19.57, 18.55, 19.15, 18.4, 18.43, 18.65, 18.7, 18.52, 18.6, 18.57]
+338.78999999999996
+16.9395
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107283, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.024356126785278, 'TIME_S_1KI': 0.09343843970419617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1167.401763653755, 'W': 84.69, 'J_1KI': 10.881516770166336, 'W_1KI': 0.7894074550487962, 'W_D': 67.7505, 'J_D': 933.9007343065739, 'W_D_1KI': 0.6315119823271163, 'J_D_1KI': 0.0058864124076239135}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..dd0a1d8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 110408, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.283727407455444, "TIME_S_1KI": 0.0931429552881625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.020034651756, "W": 85.81999999999998, "J_1KI": 11.113506581513622, "W_1KI": 0.7772987464676471, "W_D": 68.78699999999998, "J_D": 983.4890133254524, "W_D_1KI": 0.6230255053981594, "J_D_1KI": 0.005642938060631109}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..5ad7b22
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_020.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.023978710174560547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.6900, 0.0371, 0.2641,  ..., 0.3456, 0.3606, 0.9968])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.023978710174560547 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43788', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 4.1642844676971436}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.8359, 0.0286, 0.9454,  ..., 0.8317, 0.0859, 0.7447])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 4.1642844676971436 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '110408', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.283727407455444}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3467, 0.5082, 0.8796,  ..., 0.4563, 0.0673, 0.1727])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.283727407455444 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 80944, 80946, 80948]),
+       col_indices=tensor([ 1040,  5699,   106,  ..., 31378, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=80948, layout=torch.sparse_csr)
+tensor([0.3467, 0.5082, 0.8796,  ..., 0.4563, 0.0673, 0.1727])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.283727407455444 seconds
+
+[19.23, 18.84, 18.71, 18.5, 18.52, 18.83, 18.59, 18.33, 18.56, 18.53]
+[85.82]
+14.297600030899048
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110408, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.283727407455444, 'TIME_S_1KI': 0.0931429552881625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.020034651756, 'W': 85.81999999999998}
+[19.23, 18.84, 18.71, 18.5, 18.52, 18.83, 18.59, 18.33, 18.56, 18.53, 19.71, 18.77, 18.74, 19.27, 18.84, 18.8, 20.98, 18.97, 19.29, 18.77]
+340.65999999999997
+17.032999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110408, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.283727407455444, 'TIME_S_1KI': 0.0931429552881625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.020034651756, 'W': 85.81999999999998, 'J_1KI': 11.113506581513622, 'W_1KI': 0.7772987464676471, 'W_D': 68.78699999999998, 'J_D': 983.4890133254524, 'W_D_1KI': 0.6230255053981594, 'J_D_1KI': 0.005642938060631109}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json
new file mode 100644
index 0000000..ac04a12
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109001, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.287229776382446, "TIME_S_1KI": 0.09437738898159143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.7143850159644, "W": 85.57, "J_1KI": 11.052324153135883, "W_1KI": 0.7850386693700058, "W_D": 68.3185, "J_D": 961.8356867209673, "W_D_1KI": 0.6267694791790901, "J_D_1KI": 0.005750125954615922}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output
new file mode 100644
index 0000000..c5dbd7f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.023378610610961914}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2787, 0.0070, 0.8156,  ..., 0.2081, 0.1934, 0.2181])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 0.023378610610961914 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44912', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 4.326327562332153}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.1269, 0.3329, 0.3331,  ..., 0.2645, 0.1870, 0.6544])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 4.326327562332153 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '109001', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.287229776382446}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2367, 0.6225, 0.7020,  ..., 0.6877, 0.6724, 0.8482])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.287229776382446 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 85845, 85847, 85850]),
+       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
+       nnz=85850, layout=torch.sparse_csr)
+tensor([0.2367, 0.6225, 0.7020,  ..., 0.6877, 0.6724, 0.8482])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_025
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 85850
+Density: 8.718908121010495e-05
+Time: 10.287229776382446 seconds
+
+[19.11, 18.88, 18.79, 18.69, 18.99, 18.57, 22.71, 18.57, 18.78, 18.52]
+[85.57]
+14.078700304031372
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.287229776382446, 'TIME_S_1KI': 0.09437738898159143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7143850159644, 'W': 85.57}
+[19.11, 18.88, 18.79, 18.69, 18.99, 18.57, 22.71, 18.57, 18.78, 18.52, 19.61, 18.91, 22.51, 18.3, 18.93, 18.54, 18.82, 18.5, 18.7, 18.44]
+345.03
+17.2515
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.287229776382446, 'TIME_S_1KI': 0.09437738898159143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7143850159644, 'W': 85.57, 'J_1KI': 11.052324153135883, 'W_1KI': 0.7850386693700058, 'W_D': 68.3185, 'J_D': 961.8356867209673, 'W_D_1KI': 0.6267694791790901, 'J_D_1KI': 0.005750125954615922}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..dd4127f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 108566, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.996469974517822, "TIME_S_1KI": 0.1012883404981101, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1198.3566909980773, "W": 85.72, "J_1KI": 11.038047740527212, "W_1KI": 0.7895657940791776, "W_D": 68.6185, "J_D": 959.2794983813762, "W_D_1KI": 0.6320441022051103, "J_D_1KI": 0.00582174992359588}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..69bedbe
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_030.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.023941993713378906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.9702, 0.3092, 0.3149,  ..., 0.8172, 0.7407, 0.6788])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 0.023941993713378906 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43855', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 4.241435527801514}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.3948, 0.7468, 0.9374,  ..., 0.0232, 0.8850, 0.9257])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 4.241435527801514 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '108566', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.996469974517822}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8081, 0.1953, 0.5127,  ..., 0.2634, 0.0979, 0.9842])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.996469974517822 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 86850, 86850, 86850]),
+       col_indices=tensor([ 1809, 21783,   106,  ...,  7018,   160,   882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=86850, layout=torch.sparse_csr)
+tensor([0.8081, 0.1953, 0.5127,  ..., 0.2634, 0.0979, 0.9842])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
+Time: 10.996469974517822 seconds
+
+[19.33, 18.79, 18.48, 19.6, 18.78, 18.81, 18.79, 19.04, 18.61, 18.48]
+[85.72]
+13.979896068572998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108566, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.996469974517822, 'TIME_S_1KI': 0.1012883404981101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.3566909980773, 'W': 85.72}
+[19.33, 18.79, 18.48, 19.6, 18.78, 18.81, 18.79, 19.04, 18.61, 18.48, 19.2, 19.19, 18.71, 18.74, 18.94, 19.21, 20.72, 18.83, 18.68, 19.21]
+342.03
+17.101499999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108566, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 10.996469974517822, 'TIME_S_1KI': 0.1012883404981101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.3566909980773, 'W': 85.72, 'J_1KI': 11.038047740527212, 'W_1KI': 0.7895657940791776, 'W_D': 68.6185, 'J_D': 959.2794983813762, 'W_D_1KI': 0.6320441022051103, 'J_D_1KI': 0.00582174992359588}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json
new file mode 100644
index 0000000..5edced6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103439, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.351110458374023, "TIME_S_1KI": 0.10006970734804109, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1118.8645426464082, "W": 85.18, "J_1KI": 10.81666047280434, "W_1KI": 0.8234805054186526, "W_D": 67.80525, "J_D": 890.64205248034, "W_D_1KI": 0.6555095273542861, "J_D_1KI": 0.006337160329801005}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output
new file mode 100644
index 0000000..6408866
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.02468276023864746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.4003, 0.8236, 0.0269,  ..., 0.2308, 0.8303, 0.1459])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 0.02468276023864746 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42539', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 4.318082571029663}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.2117, 0.5564, 0.7291,  ..., 0.0848, 0.9734, 0.5692])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 4.318082571029663 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103439', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.351110458374023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7311, 0.5094, 0.4269,  ..., 0.7263, 0.3700, 0.5742])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.351110458374023 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 87559, 87559, 87560]),
+       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=87560, layout=torch.sparse_csr)
+tensor([0.7311, 0.5094, 0.4269,  ..., 0.7263, 0.3700, 0.5742])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_035
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 87560
+Density: 8.892575364888514e-05
+Time: 10.351110458374023 seconds
+
+[19.14, 18.98, 18.66, 18.56, 18.83, 18.87, 22.69, 18.77, 18.81, 18.9]
+[85.18]
+13.13529634475708
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103439, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.351110458374023, 'TIME_S_1KI': 0.10006970734804109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1118.8645426464082, 'W': 85.18}
+[19.14, 18.98, 18.66, 18.56, 18.83, 18.87, 22.69, 18.77, 18.81, 18.9, 19.57, 18.61, 18.66, 22.69, 19.25, 19.17, 18.61, 18.71, 19.31, 19.02]
+347.49499999999995
+17.37475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103439, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.351110458374023, 'TIME_S_1KI': 0.10006970734804109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1118.8645426464082, 'W': 85.18, 'J_1KI': 10.81666047280434, 'W_1KI': 0.8234805054186526, 'W_D': 67.80525, 'J_D': 890.64205248034, 'W_D_1KI': 0.6555095273542861, 'J_D_1KI': 0.006337160329801005}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..8fc8b8b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 106181, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.6138916015625, "TIME_S_1KI": 0.09996036580520526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1185.0728600692748, "W": 85.56, "J_1KI": 11.16087492177767, "W_1KI": 0.8057938802610637, "W_D": 68.70025000000001, "J_D": 951.5521476738454, "W_D_1KI": 0.6470107646377413, "J_D_1KI": 0.00609347025021182}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..88620eb
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_040.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.02361464500427246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.4925, 0.3547, 0.3206,  ..., 0.0377, 0.5635, 0.3779])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 0.02361464500427246 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44463', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 4.396841764450073}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.8706, 0.2631, 0.7886,  ..., 0.5949, 0.1926, 0.3156])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 4.396841764450073 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '106181', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.6138916015625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.2870, 0.2188, 0.1771,  ..., 0.3412, 0.5388, 0.2765])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.6138916015625 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89657, 89657, 89658]),
+       col_indices=tensor([  106,   329,  1040,  ..., 10144,   882, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89658, layout=torch.sparse_csr)
+tensor([0.2870, 0.2188, 0.1771,  ..., 0.3412, 0.5388, 0.2765])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
+Time: 10.6138916015625 seconds
+
+[18.82, 18.67, 18.79, 18.51, 18.63, 18.93, 18.95, 19.25, 18.61, 18.58]
+[85.56]
+13.850781440734863
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106181, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.6138916015625, 'TIME_S_1KI': 0.09996036580520526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.0728600692748, 'W': 85.56}
+[18.82, 18.67, 18.79, 18.51, 18.63, 18.93, 18.95, 19.25, 18.61, 18.58, 19.08, 18.56, 18.58, 18.7, 18.58, 18.47, 18.7, 18.9, 18.9, 18.45]
+337.19499999999994
+16.85975
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106181, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 10.6138916015625, 'TIME_S_1KI': 0.09996036580520526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.0728600692748, 'W': 85.56, 'J_1KI': 11.16087492177767, 'W_1KI': 0.8057938802610637, 'W_D': 68.70025000000001, 'J_D': 951.5521476738454, 'W_D_1KI': 0.6470107646377413, 'J_D_1KI': 0.00609347025021182}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json
new file mode 100644
index 0000000..cee2dd2
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104342, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.794602870941162, "TIME_S_1KI": 0.10345405369785093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1213.257156124115, "W": 85.72, "J_1KI": 11.627696959269661, "W_1KI": 0.8215292020471142, "W_D": 68.65825, "J_D": 971.7698686357736, "W_D_1KI": 0.6580116348162772, "J_D_1KI": 0.006306296935234874}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output
new file mode 100644
index 0000000..8f96d81
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.024647951126098633}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.2181, 0.0875, 0.6140,  ..., 0.7443, 0.8424, 0.1920])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 0.024647951126098633 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42599', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.286748647689819}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.9399, 0.2522, 0.4169,  ..., 0.3599, 0.7670, 0.0171])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 4.286748647689819 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104342', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.794602870941162}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.4314, 0.9415, 0.5407,  ..., 0.5093, 0.0658, 0.7203])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.794602870941162 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=89152, layout=torch.sparse_csr)
+tensor([0.4314, 0.9415, 0.5407,  ..., 0.5093, 0.0658, 0.7203])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_045
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89152
+Density: 9.054258553341032e-05
+Time: 10.794602870941162 seconds
+
+[19.11, 18.76, 19.16, 18.8, 18.66, 18.43, 18.88, 18.35, 18.59, 18.42]
+[85.72]
+14.153723239898682
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104342, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.794602870941162, 'TIME_S_1KI': 0.10345405369785093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1213.257156124115, 'W': 85.72}
+[19.11, 18.76, 19.16, 18.8, 18.66, 18.43, 18.88, 18.35, 18.59, 18.42, 19.33, 19.07, 18.75, 18.46, 18.8, 18.47, 18.71, 22.15, 19.49, 18.55]
+341.235
+17.06175
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104342, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.794602870941162, 'TIME_S_1KI': 0.10345405369785093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1213.257156124115, 'W': 85.72, 'J_1KI': 11.627696959269661, 'W_1KI': 0.8215292020471142, 'W_D': 68.65825, 'J_D': 971.7698686357736, 'W_D_1KI': 0.6580116348162772, 'J_D_1KI': 0.006306296935234874}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..2f8a196
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105128, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.364393472671509, "TIME_S_1KI": 0.09858832540019319, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.563982129097, "W": 86.5, "J_1KI": 11.829046325708632, "W_1KI": 0.8228064835248459, "W_D": 69.52275, "J_D": 999.4911888851524, "W_D_1KI": 0.661315253785861, "J_D_1KI": 0.006290572005420639}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..93a64ba
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_050.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.02408146858215332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.7358, 0.3600, 0.8429,  ..., 0.7814, 0.7794, 0.3764])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 0.02408146858215332 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43601', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 4.354790449142456}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.3230, 0.9559, 0.0845,  ..., 0.8343, 0.7927, 0.7634])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 4.354790449142456 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105128', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.364393472671509}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.6033, 0.9053, 0.6513,  ..., 0.7717, 0.0558, 0.8843])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.364393472671509 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 90390, 90390, 90392]),
+       col_indices=tensor([ 5326,   106,   329,  ...,   882,  2232, 16085]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=90392, layout=torch.sparse_csr)
+tensor([0.6033, 0.9053, 0.6513,  ..., 0.7717, 0.0558, 0.8843])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_050
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 90392
+Density: 9.180192695100532e-05
+Time: 10.364393472671509 seconds
+
+[21.94, 18.59, 18.68, 18.83, 19.36, 18.66, 18.81, 18.43, 18.92, 19.55]
+[86.5]
+14.37646222114563
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105128, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.364393472671509, 'TIME_S_1KI': 0.09858832540019319, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.563982129097, 'W': 86.5}
+[21.94, 18.59, 18.68, 18.83, 19.36, 18.66, 18.81, 18.43, 18.92, 19.55, 19.61, 18.49, 19.15, 18.4, 18.57, 18.53, 18.89, 18.51, 18.93, 18.49]
+339.54499999999996
+16.977249999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105128, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 10.364393472671509, 'TIME_S_1KI': 0.09858832540019319, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.563982129097, 'W': 86.5, 'J_1KI': 11.829046325708632, 'W_1KI': 0.8228064835248459, 'W_D': 69.52275, 'J_D': 999.4911888851524, 'W_D_1KI': 0.661315253785861, 'J_D_1KI': 0.006290572005420639}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json
new file mode 100644
index 0000000..16ff754
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 108819, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.897866487503052, "TIME_S_1KI": 0.10014672518129235, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1284.6524841690064, "W": 86.48, "J_1KI": 11.805406079535802, "W_1KI": 0.7947141583730782, "W_D": 69.0845, "J_D": 1026.2439239428045, "W_D_1KI": 0.6348569643168932, "J_D_1KI": 0.005834063576368954}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output
new file mode 100644
index 0000000..1351c73
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.023081541061401367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.2528, 0.0970, 0.7933,  ..., 0.5516, 0.4644, 0.6686])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 0.023081541061401367 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45490', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 4.389326810836792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.3779, 0.3653, 0.4471,  ..., 0.3867, 0.0629, 0.2299])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 4.389326810836792 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '108819', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.897866487503052}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.8117, 0.8203, 0.5161,  ..., 0.5522, 0.8518, 0.2789])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.897866487503052 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 91475, 91475, 91476]),
+       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=91476, layout=torch.sparse_csr)
+tensor([0.8117, 0.8203, 0.5161,  ..., 0.5522, 0.8518, 0.2789])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_055
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 91476
+Density: 9.290283509348351e-05
+Time: 10.897866487503052 seconds
+
+[19.82, 19.58, 22.76, 18.73, 18.98, 18.92, 19.19, 18.75, 18.85, 18.86]
+[86.48]
+14.854908466339111
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108819, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.897866487503052, 'TIME_S_1KI': 0.10014672518129235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1284.6524841690064, 'W': 86.48}
+[19.82, 19.58, 22.76, 18.73, 18.98, 18.92, 19.19, 18.75, 18.85, 18.86, 19.16, 18.57, 19.11, 18.51, 18.92, 18.66, 20.05, 18.75, 19.25, 22.82]
+347.91
+17.395500000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108819, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.897866487503052, 'TIME_S_1KI': 0.10014672518129235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1284.6524841690064, 'W': 86.48, 'J_1KI': 11.805406079535802, 'W_1KI': 0.7947141583730782, 'W_D': 69.0845, 'J_D': 1026.2439239428045, 'W_D_1KI': 0.6348569643168932, 'J_D_1KI': 0.005834063576368954}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..5ee65e7
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103633, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.614157915115356, "TIME_S_1KI": 0.10242063739460747, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1236.6533923459053, "W": 86.29, "J_1KI": 11.933007751834891, "W_1KI": 0.8326498316173421, "W_D": 69.065, "J_D": 989.7956488859653, "W_D_1KI": 0.666438296681559, "J_D_1KI": 0.0064307536854241315}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..f18bb52
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_060.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.026314496994018555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.3444, 0.9973, 0.3290,  ..., 0.5556, 0.2242, 0.3336])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 0.026314496994018555 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '39901', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 4.042727708816528}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.8538, 0.9136, 0.0865,  ..., 0.4966, 0.6553, 0.2087])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 4.042727708816528 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103633', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.614157915115356}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.5159, 0.9513, 0.3785,  ..., 0.6684, 0.6365, 0.8631])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.614157915115356 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 94180, 94180, 94180]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=94180, layout=torch.sparse_csr)
+tensor([0.5159, 0.9513, 0.3785,  ..., 0.6684, 0.6365, 0.8631])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 94180
+Density: 9.564901186217454e-05
+Time: 10.614157915115356 seconds
+
+[19.56, 18.69, 18.86, 18.88, 20.82, 18.73, 18.76, 19.13, 19.46, 18.77]
+[86.29]
+14.331363916397095
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103633, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.614157915115356, 'TIME_S_1KI': 0.10242063739460747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1236.6533923459053, 'W': 86.29}
+[19.56, 18.69, 18.86, 18.88, 20.82, 18.73, 18.76, 19.13, 19.46, 18.77, 22.24, 18.95, 18.95, 18.81, 18.81, 19.24, 18.73, 18.65, 18.87, 19.75]
+344.5
+17.225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103633, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 10.614157915115356, 'TIME_S_1KI': 0.10242063739460747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1236.6533923459053, 'W': 86.29, 'J_1KI': 11.933007751834891, 'W_1KI': 0.8326498316173421, 'W_D': 69.065, 'J_D': 989.7956488859653, 'W_D_1KI': 0.666438296681559, 'J_D_1KI': 0.0064307536854241315}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json
new file mode 100644
index 0000000..322da9f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 101100, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.192413091659546, "TIME_S_1KI": 0.10081516411137038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1177.807462463379, "W": 86.32, "J_1KI": 11.649925444741632, "W_1KI": 0.8538081107814045, "W_D": 56.87149999999999, "J_D": 775.9925521488188, "W_D_1KI": 0.5625272007912957, "J_D_1KI": 0.005564067267965339}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output
new file mode 100644
index 0000000..1d544ec
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.023540258407592773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.6962, 0.9656, 0.9232,  ..., 0.4983, 0.2135, 0.8824])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 0.023540258407592773 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44604', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 4.632462501525879}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2472, 0.8203, 0.9803,  ..., 0.4259, 0.4111, 0.8965])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 4.632462501525879 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '101100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.192413091659546}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2591, 0.4101, 0.9221,  ..., 0.4342, 0.1148, 0.3704])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.192413091659546 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
+       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=95068, layout=torch.sparse_csr)
+tensor([0.2591, 0.4101, 0.9221,  ..., 0.4342, 0.1148, 0.3704])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_065
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 95068
+Density: 9.655086281283934e-05
+Time: 10.192413091659546 seconds
+
+[43.11, 45.01, 42.05, 51.61, 51.92, 51.75, 47.55, 42.25, 42.99, 42.64]
+[86.32]
+13.644664764404297
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 101100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.192413091659546, 'TIME_S_1KI': 0.10081516411137038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.807462463379, 'W': 86.32}
+[43.11, 45.01, 42.05, 51.61, 51.92, 51.75, 47.55, 42.25, 42.99, 42.64, 19.89, 18.77, 19.55, 18.49, 19.73, 18.5, 18.75, 18.62, 19.33, 18.56]
+588.97
+29.448500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 101100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.192413091659546, 'TIME_S_1KI': 0.10081516411137038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.807462463379, 'W': 86.32, 'J_1KI': 11.649925444741632, 'W_1KI': 0.8538081107814045, 'W_D': 56.87149999999999, 'J_D': 775.9925521488188, 'W_D_1KI': 0.5625272007912957, 'J_D_1KI': 0.005564067267965339}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..2f39571
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104291, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.099636316299438, "TIME_S_1KI": 0.09684091931518002, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1121.6735239505767, "W": 85.22999999999999, "J_1KI": 10.755228389320044, "W_1KI": 0.8172325512268555, "W_D": 68.27224999999999, "J_D": 898.5002375400064, "W_D_1KI": 0.654632230969115, "J_D_1KI": 0.006276977217296939}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..b64bae4
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_070.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.02328205108642578}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.3779, 0.2211, 0.7260,  ..., 0.4901, 0.3247, 0.5495])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 0.02328205108642578 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45099', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 4.540517091751099}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.2104, 0.8367, 0.9369,  ..., 0.2344, 0.5676, 0.4367])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 4.540517091751099 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104291', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.099636316299438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.1732, 0.6606, 0.8063,  ..., 0.0030, 0.8646, 0.6542])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.099636316299438 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 78684, 78684, 78684]),
+       col_indices=tensor([  106,   329,  1040,  ..., 16263,  2242,  2242]),
+       values=tensor([1., 1., 1.,  ..., 3., 1., 1.]), size=(31379, 31379),
+       nnz=78684, layout=torch.sparse_csr)
+tensor([0.1732, 0.6606, 0.8063,  ..., 0.0030, 0.8646, 0.6542])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_070
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 78684
+Density: 7.991130653390679e-05
+Time: 10.099636316299438 seconds
+
+[18.99, 18.96, 19.18, 18.58, 18.73, 18.61, 18.91, 18.52, 18.79, 19.0]
+[85.23]
+13.160548210144043
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104291, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.099636316299438, 'TIME_S_1KI': 0.09684091931518002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6735239505767, 'W': 85.22999999999999}
+[18.99, 18.96, 19.18, 18.58, 18.73, 18.61, 18.91, 18.52, 18.79, 19.0, 19.24, 18.62, 19.77, 18.65, 18.86, 18.53, 19.05, 18.5, 18.92, 18.72]
+339.155
+16.957749999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104291, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 10.099636316299438, 'TIME_S_1KI': 0.09684091931518002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6735239505767, 'W': 85.22999999999999, 'J_1KI': 10.755228389320044, 'W_1KI': 0.8172325512268555, 'W_D': 68.27224999999999, 'J_D': 898.5002375400064, 'W_D_1KI': 0.654632230969115, 'J_D_1KI': 0.006276977217296939}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json
new file mode 100644
index 0000000..139ccda
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105268, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.531576871871948, "TIME_S_1KI": 0.10004537819538652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.4668582105637, "W": 86.09, "J_1KI": 11.584402270495913, "W_1KI": 0.8178173804005017, "W_D": 69.07325, "J_D": 978.4241975129843, "W_D_1KI": 0.6561656913781967, "J_D_1KI": 0.006233287336875372}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output
new file mode 100644
index 0000000..ad13e41
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.024872303009033203}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9027, 0.7284, 0.4730,  ..., 0.3675, 0.6758, 0.7746])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 0.024872303009033203 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42215', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 4.210744380950928}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.9390, 0.3678, 0.4837,  ..., 0.7294, 0.6703, 0.0534])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 4.210744380950928 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105268', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.531576871871948}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.7677, 0.2796, 0.1915,  ..., 0.1541, 0.8730, 0.0067])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.531576871871948 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 97491, 97491, 97492]),
+       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=97492, layout=torch.sparse_csr)
+tensor([0.7677, 0.2796, 0.1915,  ..., 0.1541, 0.8730, 0.0067])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_075
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 97492
+Density: 9.901267216465406e-05
+Time: 10.531576871871948 seconds
+
+[19.05, 18.57, 18.7, 18.82, 18.8, 18.65, 18.86, 19.11, 18.93, 18.94]
+[86.09]
+14.16502332687378
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105268, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.531576871871948, 'TIME_S_1KI': 0.10004537819538652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.4668582105637, 'W': 86.09}
+[19.05, 18.57, 18.7, 18.82, 18.8, 18.65, 18.86, 19.11, 18.93, 18.94, 18.9, 18.76, 19.26, 19.08, 19.08, 19.0, 18.81, 19.15, 18.76, 19.1]
+340.33500000000004
+17.016750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105268, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.531576871871948, 'TIME_S_1KI': 0.10004537819538652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.4668582105637, 'W': 86.09, 'J_1KI': 11.584402270495913, 'W_1KI': 0.8178173804005017, 'W_D': 69.07325, 'J_D': 978.4241975129843, 'W_D_1KI': 0.6561656913781967, 'J_D_1KI': 0.006233287336875372}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..fb10afd
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 102170, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.624010801315308, "TIME_S_1KI": 0.10398366253611929, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1188.1788166952133, "W": 85.98, "J_1KI": 11.629429545808097, "W_1KI": 0.8415386121170598, "W_D": 69.04050000000001, "J_D": 954.0876900912524, "W_D_1KI": 0.6757414113732017, "J_D_1KI": 0.006613892643370868}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..d90bab0
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_080.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.02446913719177246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.1514, 0.7192, 0.8705,  ..., 0.9893, 0.1056, 0.5568])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 0.02446913719177246 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42911', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 4.409920930862427}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.7645, 0.9001, 0.8217,  ..., 0.2869, 0.0309, 0.2560])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 4.409920930862427 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '102170', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.624010801315308}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5569, 0.4230, 0.1061,  ..., 0.5662, 0.1665, 0.8823])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.624010801315308 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 98111, 98111, 98112]),
+       col_indices=tensor([22754, 22754,   106,  ...,  4133, 31329, 12170]),
+       values=tensor([1., 1., 1.,  ..., 3., 3., 1.]), size=(31379, 31379),
+       nnz=98112, layout=torch.sparse_csr)
+tensor([0.5569, 0.4230, 0.1061,  ..., 0.5662, 0.1665, 0.8823])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_080
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 98112
+Density: 9.964234287345156e-05
+Time: 10.624010801315308 seconds
+
+[18.83, 18.35, 18.54, 18.56, 18.63, 18.49, 22.37, 18.7, 19.55, 18.48]
+[85.98]
+13.819246530532837
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 102170, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.624010801315308, 'TIME_S_1KI': 0.10398366253611929, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1188.1788166952133, 'W': 85.98}
+[18.83, 18.35, 18.54, 18.56, 18.63, 18.49, 22.37, 18.7, 19.55, 18.48, 19.14, 18.71, 18.39, 18.45, 18.58, 18.54, 18.51, 18.29, 18.52, 18.77]
+338.78999999999996
+16.9395
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 102170, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 10.624010801315308, 'TIME_S_1KI': 0.10398366253611929, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1188.1788166952133, 'W': 85.98, 'J_1KI': 11.629429545808097, 'W_1KI': 0.8415386121170598, 'W_D': 69.04050000000001, 'J_D': 954.0876900912524, 'W_D_1KI': 0.6757414113732017, 'J_D_1KI': 0.006613892643370868}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json
new file mode 100644
index 0000000..d8ea646
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 110476, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.772056579589844, "TIME_S_1KI": 0.0975058526701713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.966497516632, "W": 86.6, "J_1KI": 10.952301834938194, "W_1KI": 0.7838806618632101, "W_D": 69.526, "J_D": 971.4102852926254, "W_D_1KI": 0.6293312574676853, "J_D_1KI": 0.005696542755600178}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output
new file mode 100644
index 0000000..1ebf518
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.02397441864013672}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.7886, 0.1701, 0.5772,  ..., 0.5950, 0.8361, 0.6037])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 0.02397441864013672 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43796', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 4.1625049114227295}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.6182, 0.4960, 0.6196,  ..., 0.3101, 0.0930, 0.9002])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 4.1625049114227295 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '110476', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.772056579589844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.8448, 0.2084, 0.2325,  ..., 0.3995, 0.9349, 0.2762])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.772056579589844 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=99166, layout=torch.sparse_csr)
+tensor([0.8448, 0.2084, 0.2325,  ..., 0.3995, 0.9349, 0.2762])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_085
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 99166
+Density: 0.0001007127830784073
+Time: 10.772056579589844 seconds
+
+[22.78, 18.74, 18.96, 19.11, 18.72, 18.74, 18.69, 19.0, 18.58, 18.61]
+[86.6]
+13.971899509429932
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110476, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.772056579589844, 'TIME_S_1KI': 0.0975058526701713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.966497516632, 'W': 86.6}
+[22.78, 18.74, 18.96, 19.11, 18.72, 18.74, 18.69, 19.0, 18.58, 18.61, 19.3, 19.21, 18.84, 18.59, 18.51, 19.52, 19.13, 18.42, 18.76, 19.23]
+341.48
+17.074
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110476, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.772056579589844, 'TIME_S_1KI': 0.0975058526701713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.966497516632, 'W': 86.6, 'J_1KI': 10.952301834938194, 'W_1KI': 0.7838806618632101, 'W_D': 69.526, 'J_D': 971.4102852926254, 'W_D_1KI': 0.6293312574676853, 'J_D_1KI': 0.005696542755600178}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..d6ec094
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105661, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.476396322250366, "TIME_S_1KI": 0.09915102376705091, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1201.1792421340942, "W": 86.25, "J_1KI": 11.368236550232293, "W_1KI": 0.8162898325777723, "W_D": 69.17725, "J_D": 963.411904091835, "W_D_1KI": 0.6547094008196023, "J_D_1KI": 0.006196320315155093}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..28a91b4
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_090.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.02511739730834961}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.3835, 0.8667, 0.5561,  ..., 0.5519, 0.8662, 0.6529])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 0.02511739730834961 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41803', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 4.154126405715942}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.7360, 0.6989, 0.7408,  ..., 0.3100, 0.0914, 0.1008])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 4.154126405715942 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105661', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.476396322250366}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4895, 0.5578, 0.3404,  ..., 0.8904, 0.8379, 0.8794])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.476396322250366 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100923, 100923,
+                            100924]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=100924, layout=torch.sparse_csr)
+tensor([0.4895, 0.5578, 0.3404,  ..., 0.8904, 0.8379, 0.8794])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_090
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 100924
+Density: 0.00010249820421722343
+Time: 10.476396322250366 seconds
+
+[19.34, 18.61, 19.86, 18.92, 18.56, 18.56, 19.44, 18.69, 18.75, 18.88]
+[86.25]
+13.926715850830078
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105661, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.476396322250366, 'TIME_S_1KI': 0.09915102376705091, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1201.1792421340942, 'W': 86.25}
+[19.34, 18.61, 19.86, 18.92, 18.56, 18.56, 19.44, 18.69, 18.75, 18.88, 19.4, 18.9, 18.83, 19.13, 19.15, 18.59, 19.62, 18.59, 19.04, 18.81]
+341.455
+17.07275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105661, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 10.476396322250366, 'TIME_S_1KI': 0.09915102376705091, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1201.1792421340942, 'W': 86.25, 'J_1KI': 11.368236550232293, 'W_1KI': 0.8162898325777723, 'W_D': 69.17725, 'J_D': 963.411904091835, 'W_D_1KI': 0.6547094008196023, 'J_D_1KI': 0.006196320315155093}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json
new file mode 100644
index 0000000..fbaff5d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 107497, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.770461082458496, "TIME_S_1KI": 0.10019313173817405, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1238.6108507156373, "W": 86.48, "J_1KI": 11.522282954088368, "W_1KI": 0.8044875670948957, "W_D": 69.43700000000001, "J_D": 994.5122761464121, "W_D_1KI": 0.6459436077285879, "J_D_1KI": 0.006008945437813036}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output
new file mode 100644
index 0000000..7ff6446
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.0250704288482666}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.4833, 0.8951, 0.5308,  ..., 0.3731, 0.7013, 0.1331])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 0.0250704288482666 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41882', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 4.090898036956787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.0579, 0.7468, 0.4378,  ..., 0.4871, 0.7748, 0.6891])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 4.090898036956787 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '107497', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.770461082458496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2379, 0.0163, 0.9504,  ..., 0.9694, 0.0397, 0.3577])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.770461082458496 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
+                            102290]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102290, layout=torch.sparse_csr)
+tensor([0.2379, 0.0163, 0.9504,  ..., 0.9694, 0.0397, 0.3577])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_095
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102290
+Density: 0.00010388551097241275
+Time: 10.770461082458496 seconds
+
+[18.93, 21.33, 19.05, 18.6, 18.75, 19.05, 18.88, 18.52, 18.7, 18.69]
+[86.48]
+14.322512149810791
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107497, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.770461082458496, 'TIME_S_1KI': 0.10019313173817405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1238.6108507156373, 'W': 86.48}
+[18.93, 21.33, 19.05, 18.6, 18.75, 19.05, 18.88, 18.52, 18.7, 18.69, 19.38, 18.53, 19.04, 18.57, 19.18, 18.64, 18.84, 18.71, 18.71, 18.52]
+340.86
+17.043
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107497, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.770461082458496, 'TIME_S_1KI': 0.10019313173817405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1238.6108507156373, 'W': 86.48, 'J_1KI': 11.522282954088368, 'W_1KI': 0.8044875670948957, 'W_D': 69.43700000000001, 'J_D': 994.5122761464121, 'W_D_1KI': 0.6459436077285879, 'J_D_1KI': 0.006008945437813036}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..84f9780
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105160, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.657647609710693, "TIME_S_1KI": 0.10134697232513022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1162.8676796245575, "W": 85.99, "J_1KI": 11.058079874710513, "W_1KI": 0.8177063522251806, "W_D": 68.56675, "J_D": 927.2480226991177, "W_D_1KI": 0.6520231076454926, "J_D_1KI": 0.006200295812528458}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..ed16927
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_100.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.023373842239379883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.6881, 0.8400, 0.8026,  ..., 0.8342, 0.7562, 0.6440])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 0.023373842239379883 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44922', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 4.4853386878967285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.5935, 0.7555, 0.7433,  ..., 0.7134, 0.5418, 0.7238])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 4.4853386878967285 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105160', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.657647609710693}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.0932, 0.8041, 0.1997,  ..., 0.2370, 0.2314, 0.0038])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.657647609710693 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 102886, 102887,
+                            102888]),
+       col_indices=tensor([  106,   329,  1040,  ..., 25970,  5128, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=102888, layout=torch.sparse_csr)
+tensor([0.0932, 0.8041, 0.1997,  ..., 0.2370, 0.2314, 0.0038])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_100
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 102888
+Density: 0.00010449283852702711
+Time: 10.657647609710693 seconds
+
+[19.3, 18.61, 18.94, 18.56, 18.98, 18.59, 19.27, 22.85, 19.85, 18.73]
+[85.99]
+13.523289680480957
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.657647609710693, 'TIME_S_1KI': 0.10134697232513022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1162.8676796245575, 'W': 85.99}
+[19.3, 18.61, 18.94, 18.56, 18.98, 18.59, 19.27, 22.85, 19.85, 18.73, 18.97, 18.77, 18.99, 18.82, 23.47, 18.54, 18.94, 18.85, 18.68, 18.51]
+348.465
+17.42325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 10.657647609710693, 'TIME_S_1KI': 0.10134697232513022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1162.8676796245575, 'W': 85.99, 'J_1KI': 11.058079874710513, 'W_1KI': 0.8177063522251806, 'W_D': 68.56675, 'J_D': 927.2480226991177, 'W_D_1KI': 0.6520231076454926, 'J_D_1KI': 0.006200295812528458}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json
new file mode 100644
index 0000000..6f2de89
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103831, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.351726531982422, "TIME_S_1KI": 0.09969784102996622, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.3511731219292, "W": 86.07, "J_1KI": 11.647303532874856, "W_1KI": 0.8289431865242557, "W_D": 69.082, "J_D": 970.6564161915778, "W_D_1KI": 0.6653311631401025, "J_D_1KI": 0.0064078277502875106}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output
new file mode 100644
index 0000000..bcbcdde
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.023554086685180664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.2517, 0.3763, 0.9196,  ..., 0.6094, 0.1457, 0.6521])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 0.023554086685180664 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44578', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 4.50795316696167}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.4299, 0.9586, 0.9105,  ..., 0.3781, 0.8739, 0.8459])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 4.50795316696167 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103831', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.351726531982422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8719, 0.2400, 0.7607,  ..., 0.7182, 0.0947, 0.1009])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.351726531982422 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
+                            104726]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104726, layout=torch.sparse_csr)
+tensor([0.8719, 0.2400, 0.7607,  ..., 0.7182, 0.0947, 0.1009])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_105
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104726
+Density: 0.00010635950749923647
+Time: 10.351726531982422 seconds
+
+[19.38, 18.67, 18.72, 18.67, 19.24, 18.61, 18.54, 18.91, 19.05, 18.66]
+[86.07]
+14.050786256790161
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103831, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.351726531982422, 'TIME_S_1KI': 0.09969784102996622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.3511731219292, 'W': 86.07}
+[19.38, 18.67, 18.72, 18.67, 19.24, 18.61, 18.54, 18.91, 19.05, 18.66, 19.08, 18.59, 19.94, 18.67, 18.85, 18.81, 19.2, 18.59, 18.8, 18.68]
+339.76
+16.988
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103831, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.351726531982422, 'TIME_S_1KI': 0.09969784102996622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.3511731219292, 'W': 86.07, 'J_1KI': 11.647303532874856, 'W_1KI': 0.8289431865242557, 'W_D': 69.082, 'J_D': 970.6564161915778, 'W_D_1KI': 0.6653311631401025, 'J_D_1KI': 0.0064078277502875106}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..592b850
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104681, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.71884036064148, "TIME_S_1KI": 0.10239528052503778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1218.6463611221313, "W": 86.24, "J_1KI": 11.641523878470128, "W_1KI": 0.8238362262492716, "W_D": 69.23124999999999, "J_D": 978.2978999122976, "W_D_1KI": 0.661354496040351, "J_D_1KI": 0.006317808351471146}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..93e0335
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_110.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.02509593963623047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.6393, 0.2401, 0.3292,  ..., 0.4982, 0.6250, 0.7472])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 0.02509593963623047 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41839', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 4.196627616882324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.0653, 0.1010, 0.1116,  ..., 0.0944, 0.3341, 0.9303])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 4.196627616882324 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104681', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.71884036064148}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5992, 0.3509, 0.4410,  ..., 0.2674, 0.6418, 0.5284])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.71884036064148 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 104844, 104844,
+                            104846]),
+       col_indices=tensor([  106,   329,  1040,  ...,   882,  2616, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=104846, layout=torch.sparse_csr)
+tensor([0.5992, 0.3509, 0.4410,  ..., 0.2674, 0.6418, 0.5284])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 104846
+Density: 0.0001064813792493263
+Time: 10.71884036064148 seconds
+
+[19.16, 18.75, 19.08, 18.79, 18.92, 19.02, 18.6, 18.74, 18.93, 18.88]
+[86.24]
+14.130871534347534
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104681, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.71884036064148, 'TIME_S_1KI': 0.10239528052503778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1218.6463611221313, 'W': 86.24}
+[19.16, 18.75, 19.08, 18.79, 18.92, 19.02, 18.6, 18.74, 18.93, 18.88, 19.51, 19.24, 18.89, 18.62, 18.64, 19.59, 18.7, 18.63, 18.92, 18.68]
+340.17500000000007
+17.008750000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104681, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.71884036064148, 'TIME_S_1KI': 0.10239528052503778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1218.6463611221313, 'W': 86.24, 'J_1KI': 11.641523878470128, 'W_1KI': 0.8238362262492716, 'W_D': 69.23124999999999, 'J_D': 978.2978999122976, 'W_D_1KI': 0.661354496040351, 'J_D_1KI': 0.006317808351471146}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json
new file mode 100644
index 0000000..b47cb22
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104289, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.945078134536743, "TIME_S_1KI": 0.10494949740180406, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.009027416706, "W": 86.63, "J_1KI": 11.727114340119341, "W_1KI": 0.8306724582650136, "W_D": 69.74199999999999, "J_D": 984.5907375054358, "W_D_1KI": 0.6687378342874127, "J_D_1KI": 0.006412352542333446}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output
new file mode 100644
index 0000000..312abf7
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.024857282638549805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.9051, 0.6149, 0.4220,  ..., 0.0889, 0.4273, 0.7147])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 0.024857282638549805 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42241', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 4.252889633178711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.2480, 0.5973, 0.7725,  ..., 0.3227, 0.0475, 0.0987])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 4.252889633178711 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104289', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.945078134536743}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.4120, 0.5717, 0.6301,  ..., 0.0346, 0.1378, 0.3165])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.945078134536743 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
+                            106312]),
+       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106312, layout=torch.sparse_csr)
+tensor([0.4120, 0.5717, 0.6301,  ..., 0.0346, 0.1378, 0.3165])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_115
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106312
+Density: 0.00010797024579625715
+Time: 10.945078134536743 seconds
+
+[19.1, 18.45, 19.14, 18.48, 19.1, 19.01, 18.81, 18.56, 18.71, 18.72]
+[86.63]
+14.117615461349487
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104289, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.945078134536743, 'TIME_S_1KI': 0.10494949740180406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.009027416706, 'W': 86.63}
+[19.1, 18.45, 19.14, 18.48, 19.1, 19.01, 18.81, 18.56, 18.71, 18.72, 20.23, 18.52, 18.94, 18.59, 18.76, 18.47, 18.57, 18.57, 18.84, 18.43]
+337.76
+16.887999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104289, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.945078134536743, 'TIME_S_1KI': 0.10494949740180406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.009027416706, 'W': 86.63, 'J_1KI': 11.727114340119341, 'W_1KI': 0.8306724582650136, 'W_D': 69.74199999999999, 'J_D': 984.5907375054358, 'W_D_1KI': 0.6687378342874127, 'J_D_1KI': 0.006412352542333446}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..45b9c3b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103124, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.32769775390625, "TIME_S_1KI": 0.10014834329454103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1215.7269132113456, "W": 86.31, "J_1KI": 11.788981354595881, "W_1KI": 0.8369535704588651, "W_D": 69.2765, "J_D": 975.8000869318247, "W_D_1KI": 0.671778635429192, "J_D_1KI": 0.00651428023960661}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..d58596f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_120.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.024603843688964844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.9476, 0.9505, 0.2794,  ..., 0.2296, 0.8612, 0.6742])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.024603843688964844 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42676', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 4.345205783843994}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.5835, 0.8336, 0.1087,  ..., 0.7330, 0.1672, 0.4448])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 4.345205783843994 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103124', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.32769775390625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1018, 0.3446, 0.4846,  ..., 0.8394, 0.0093, 0.0283])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.32769775390625 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 106509, 106509,
+                            106510]),
+       col_indices=tensor([  106,   329,  1040,  ...,   155,   160, 12170]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
+       nnz=106510, layout=torch.sparse_csr)
+tensor([0.1018, 0.3446, 0.4846,  ..., 0.8394, 0.0093, 0.0283])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: csr
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.32769775390625 seconds
+
+[19.31, 18.65, 18.84, 18.54, 19.71, 19.22, 18.64, 18.52, 19.02, 18.8]
+[86.31]
+14.085585832595825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.32769775390625, 'TIME_S_1KI': 0.10014834329454103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1215.7269132113456, 'W': 86.31}
+[19.31, 18.65, 18.84, 18.54, 19.71, 19.22, 18.64, 18.52, 19.02, 18.8, 19.06, 18.66, 19.42, 18.62, 18.74, 18.89, 18.97, 18.63, 19.77, 18.49]
+340.66999999999996
+17.033499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.32769775390625, 'TIME_S_1KI': 0.10014834329454103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1215.7269132113456, 'W': 86.31, 'J_1KI': 11.788981354595881, 'W_1KI': 0.8369535704588651, 'W_D': 69.2765, 'J_D': 975.8000869318247, 'W_D_1KI': 0.671778635429192, 'J_D_1KI': 0.00651428023960661}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore_old/altra_max_csr_20_10_10_as-caida_G_005.json
similarity index 100%
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