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cephi 2024-12-17 20:55:35 -05:00
parent 346d7162df
commit e22addba44
270 changed files with 6443 additions and 1216 deletions
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2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1345, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325, "TIME_S_1KI": 7.663848231716227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 549.730076084137, "W": 38.131256134653135, "J_1KI": 408.7212461592096, "W_1KI": 28.350376308292294, "W_D": 16.28825613465314, "J_D": 234.82426732969293, "W_D_1KI": 12.110227609407538, "J_D_1KI": 9.003886698444267}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1352, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.404298067092896, "TIME_S_1KI": 7.695486736015455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 484.69812128067014, "W": 33.67487731653053, "J_1KI": 358.50452757446016, "W_1KI": 24.907453636487073, "W_D": 18.345877316530526, "J_D": 264.060717578411, "W_D_1KI": 13.569435885007785, "J_D_1KI": 10.036565003703982}

36
pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output
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@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 7.806152820587158}
['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/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.7762420177459717}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.5486, 0.8485, 0.8195, ..., 0.3778, 0.3275, 0.7623])
tensor([0.5915, 0.9110, 0.5650, ..., 0.0235, 0.2388, 0.5939])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 7.806152820587158 seconds
Time: 0.7762420177459717 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1345 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325}
['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 1352 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.404298067092896}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6343, 0.9450, 0.3421, ..., 0.5967, 0.9759, 0.2168])
tensor([0.5062, 0.3218, 0.3012, ..., 0.8526, 0.1987, 0.4083])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -40,7 +40,7 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.307875871658325 seconds
Time: 10.404298067092896 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +50,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6343, 0.9450, 0.3421, ..., 0.5967, 0.9759, 0.2168])
tensor([0.5062, 0.3218, 0.3012, ..., 0.8526, 0.1987, 0.4083])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -59,13 +59,13 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.307875871658325 seconds
Time: 10.404298067092896 seconds
[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28]
[24.24, 24.0, 24.0, 24.8, 25.52, 28.6, 36.84, 43.04, 50.0, 55.48, 57.44, 57.44, 57.68, 57.56]
14.416783809661865
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135}
[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28, 24.12, 24.24, 24.0, 23.96, 23.96, 24.08, 24.08, 24.08, 24.08, 24.08]
436.85999999999996
21.842999999999996
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135, 'J_1KI': 408.7212461592096, 'W_1KI': 28.350376308292294, 'W_D': 16.28825613465314, 'J_D': 234.82426732969293, 'W_D_1KI': 12.110227609407538, 'J_D_1KI': 9.003886698444267}
[16.44, 16.76, 17.04, 17.32, 17.32, 17.44, 17.36, 17.56, 17.64, 17.64]
[17.64, 17.28, 17.36, 21.52, 22.6, 27.56, 34.52, 39.6, 44.56, 50.48, 51.8, 51.4, 51.56, 51.08]
14.393463611602783
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1352, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.404298067092896, 'TIME_S_1KI': 7.695486736015455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 484.69812128067014, 'W': 33.67487731653053}
[16.44, 16.76, 17.04, 17.32, 17.32, 17.44, 17.36, 17.56, 17.64, 17.64, 16.04, 16.04, 16.32, 16.56, 16.8, 17.08, 17.2, 17.36, 17.24, 16.96]
306.58000000000004
15.329000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1352, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.404298067092896, 'TIME_S_1KI': 7.695486736015455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 484.69812128067014, 'W': 33.67487731653053, 'J_1KI': 358.50452757446016, 'W_1KI': 24.907453636487073, 'W_D': 18.345877316530526, 'J_D': 264.060717578411, 'W_D_1KI': 13.569435885007785, 'J_D_1KI': 10.036565003703982}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1801, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053, "TIME_S_1KI": 5.764393856762384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8333181858063, "W": 39.805332297033694, "J_1KI": 323.6165009360391, "W_1KI": 22.10179472350566, "W_D": 18.366332297033694, "J_D": 268.92151824545857, "W_D_1KI": 10.197852469202495, "J_D_1KI": 5.66232785630344}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1795, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.901362419128418, "TIME_S_1KI": 6.073182406199676, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 485.2392428779602, "W": 33.151518867757645, "J_1KI": 270.32826901279117, "W_1KI": 18.468812739697853, "W_D": 17.880518867757644, "J_D": 261.7174034247398, "W_D_1KI": 9.96129184833295, "J_D_1KI": 5.549466210770445}

36
pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output
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@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 5.829137325286865}
['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/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.5847852230072021}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8248, 0.9604, 0.9464, ..., 0.7437, 0.8759, 0.5369])
tensor([0.4345, 0.9896, 0.1121, ..., 0.0615, 0.0342, 0.3663])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 5.829137325286865 seconds
Time: 0.5847852230072021 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1801 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053}
['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 1795 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.901362419128418}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8790, 0.0885, 0.6163, ..., 0.1605, 0.4532, 0.8862])
tensor([0.7385, 0.6645, 0.9399, ..., 0.0094, 0.2086, 0.2750])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -42,7 +42,7 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.381673336029053 seconds
Time: 10.901362419128418 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8790, 0.0885, 0.6163, ..., 0.1605, 0.4532, 0.8862])
tensor([0.7385, 0.6645, 0.9399, ..., 0.0094, 0.2086, 0.2750])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -62,13 +62,13 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.381673336029053 seconds
Time: 10.901362419128418 seconds
[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72]
[23.88, 24.0, 24.28, 26.28, 27.2, 34.12, 40.64, 47.44, 53.0, 57.04, 57.04, 56.96, 57.52, 57.24]
14.642091512680054
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694}
[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72, 23.68, 24.04, 24.0, 24.12, 24.0, 23.92, 24.08, 24.04, 23.76, 23.88]
428.78000000000003
21.439
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694, 'J_1KI': 323.6165009360391, 'W_1KI': 22.10179472350566, 'W_D': 18.366332297033694, 'J_D': 268.92151824545857, 'W_D_1KI': 10.197852469202495, 'J_D_1KI': 5.66232785630344}
[17.36, 17.0, 17.04, 16.92, 16.92, 17.04, 17.0, 17.24, 17.56, 17.36]
[17.36, 17.32, 17.16, 18.2, 18.8, 24.32, 31.48, 39.12, 44.96, 50.68, 51.72, 52.04, 51.68, 51.96]
14.637013912200928
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1795, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.901362419128418, 'TIME_S_1KI': 6.073182406199676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 485.2392428779602, 'W': 33.151518867757645}
[17.36, 17.0, 17.04, 16.92, 16.92, 17.04, 17.0, 17.24, 17.56, 17.36, 16.76, 16.8, 16.6, 16.92, 16.96, 16.96, 17.0, 16.68, 16.68, 16.72]
305.42
15.271
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1795, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.901362419128418, 'TIME_S_1KI': 6.073182406199676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 485.2392428779602, 'W': 33.151518867757645, 'J_1KI': 270.32826901279117, 'W_1KI': 18.468812739697853, 'W_D': 17.880518867757644, 'J_D': 261.7174034247398, 'W_D_1KI': 9.96129184833295, 'J_D_1KI': 5.549466210770445}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 2142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005, "TIME_S_1KI": 4.896873296921104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 527.8806602096558, "W": 35.901030267154354, "J_1KI": 246.44288525194017, "W_1KI": 16.760518332004835, "W_D": 14.631030267154355, "J_D": 215.1313725399972, "W_D_1KI": 6.830546343209316, "J_D_1KI": 3.1888638390332944}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 2094, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.280184030532837, "TIME_S_1KI": 4.909352450111193, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 442.49099234580996, "W": 30.26491057424758, "J_1KI": 211.31374992636577, "W_1KI": 14.453156912248128, "W_D": 15.088910574247581, "J_D": 220.60884657287602, "W_D_1KI": 7.205783464301615, "J_D_1KI": 3.4411573372978106}

36
pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 4.9012720584869385}
['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/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.5013799667358398}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0783, 0.8612, 0.3161, ..., 0.8531, 0.6998, 0.6080])
tensor([0.4719, 0.1819, 0.8948, ..., 0.0352, 0.6260, 0.9931])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 4.9012720584869385 seconds
Time: 0.5013799667358398 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 2142 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005}
['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 2094 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.280184030532837}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5677, 0.4069, 0.3735, ..., 0.4488, 0.2885, 0.1400])
tensor([0.1949, 0.8836, 0.2349, ..., 0.0984, 0.2287, 0.0139])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -40,7 +40,7 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.489102602005005 seconds
Time: 10.280184030532837 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +50,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5677, 0.4069, 0.3735, ..., 0.4488, 0.2885, 0.1400])
tensor([0.1949, 0.8836, 0.2349, ..., 0.0984, 0.2287, 0.0139])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -59,13 +59,13 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.489102602005005 seconds
Time: 10.280184030532837 seconds
[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24]
[23.28, 23.4, 23.56, 24.68, 26.6, 30.12, 36.56, 41.44, 45.96, 49.92, 50.84, 50.96, 50.76, 51.04]
14.703774690628052
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354}
[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24, 23.6, 23.64, 23.64, 23.68, 23.68, 23.68, 23.76, 23.64, 23.64, 23.52]
425.4
21.27
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354, 'J_1KI': 246.44288525194017, 'W_1KI': 16.760518332004835, 'W_D': 14.631030267154355, 'J_D': 215.1313725399972, 'W_D_1KI': 6.830546343209316, 'J_D_1KI': 3.1888638390332944}
[16.92, 16.84, 16.84, 16.84, 16.88, 16.88, 16.88, 16.92, 17.24, 17.16]
[17.24, 17.2, 17.52, 19.36, 20.36, 25.52, 32.16, 36.16, 40.08, 43.72, 44.2, 44.12, 44.12, 44.04]
14.62059473991394
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2094, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.280184030532837, 'TIME_S_1KI': 4.909352450111193, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 442.49099234580996, 'W': 30.26491057424758}
[16.92, 16.84, 16.84, 16.84, 16.88, 16.88, 16.88, 16.92, 17.24, 17.16, 16.88, 16.88, 16.92, 16.84, 16.88, 16.88, 16.8, 16.52, 16.64, 16.72]
303.52
15.175999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2094, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.280184030532837, 'TIME_S_1KI': 4.909352450111193, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 442.49099234580996, 'W': 30.26491057424758, 'J_1KI': 211.31374992636577, 'W_1KI': 14.453156912248128, 'W_D': 15.088910574247581, 'J_D': 220.60884657287602, 'W_D_1KI': 7.205783464301615, 'J_D_1KI': 3.4411573372978106}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262, "TIME_S_1KI": 12.712969779968262, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 642.9576612091064, "W": 41.13704135154824, "J_1KI": 642.9576612091064, "W_1KI": 41.13704135154824, "W_D": 19.36904135154824, "J_D": 302.73138558578484, "W_D_1KI": 19.36904135154824, "J_D_1KI": 19.36904135154824}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 917, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.353132724761963, "TIME_S_1KI": 11.29022107389527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 482.1942669677734, "W": 33.1435262250795, "J_1KI": 525.8388952756525, "W_1KI": 36.14343099790567, "W_D": 17.752526225079496, "J_D": 258.27566782712927, "W_D_1KI": 19.359352481002723, "J_D_1KI": 21.11161666412511}

51
pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262}
['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/389000+_cols/marine1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.1447782516479492}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7918, 0.6380, 0.4821, ..., 0.8085, 0.1927, 0.4528])
tensor([0.6209, 0.5562, 0.7338, ..., 0.2745, 0.3369, 0.4331])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -19,7 +19,10 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 12.712969779968262 seconds
Time: 1.1447782516479492 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 917 -m matrices/389000+_cols/marine1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.353132724761963}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -30,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7918, 0.6380, 0.4821, ..., 0.8085, 0.1927, 0.4528])
tensor([0.9018, 0.2899, 0.8286, ..., 0.6758, 0.9034, 0.3604])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -39,13 +42,33 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 12.712969779968262 seconds
Time: 10.353132724761963 seconds
[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24]
[23.96, 23.92, 24.76, 26.04, 30.04, 34.84, 41.8, 41.8, 48.8, 54.68, 57.92, 59.36, 59.6, 60.0, 59.68]
15.629652500152588
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824}
[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24, 24.24, 24.16, 24.08, 24.12, 24.12, 24.0, 23.92, 24.0, 24.0, 24.08]
435.36
21.768
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824, 'J_1KI': 642.9576612091064, 'W_1KI': 41.13704135154824, 'W_D': 19.36904135154824, 'J_D': 302.73138558578484, 'W_D_1KI': 19.36904135154824, 'J_D_1KI': 19.36904135154824}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.9018, 0.2899, 0.8286, ..., 0.6758, 0.9034, 0.3604])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.353132724761963 seconds
[17.0, 17.12, 17.12, 17.36, 17.16, 17.32, 17.2, 17.32, 17.24, 17.0]
[17.04, 16.96, 17.52, 19.68, 23.4, 26.64, 35.04, 35.04, 41.12, 47.52, 51.16, 52.28, 52.28, 52.48]
14.548671245574951
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 917, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.353132724761963, 'TIME_S_1KI': 11.29022107389527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 482.1942669677734, 'W': 33.1435262250795}
[17.0, 17.12, 17.12, 17.36, 17.16, 17.32, 17.2, 17.32, 17.24, 17.0, 17.08, 17.04, 17.0, 17.12, 17.12, 16.96, 16.92, 17.08, 16.8, 16.8]
307.82000000000005
15.391000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 917, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.353132724761963, 'TIME_S_1KI': 11.29022107389527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 482.1942669677734, 'W': 33.1435262250795, 'J_1KI': 525.8388952756525, 'W_1KI': 36.14343099790567, 'W_D': 17.752526225079496, 'J_D': 258.27566782712927, 'W_D_1KI': 19.359352481002723, 'J_D_1KI': 21.11161666412511}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1694, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093, "TIME_S_1KI": 6.164034958291081, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 563.8270891189575, "W": 38.54107447042048, "J_1KI": 332.8377149462559, "W_1KI": 22.751519758217523, "W_D": 16.675074470420483, "J_D": 243.94386582851405, "W_D_1KI": 9.843609486670887, "J_D_1KI": 5.81086746556723}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1714, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.564941644668579, "TIME_S_1KI": 6.163909944380735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 475.8493723487854, "W": 32.517380096721375, "J_1KI": 277.6250713820218, "W_1KI": 18.971633662031138, "W_D": 17.289380096721374, "J_D": 253.0074883909225, "W_D_1KI": 10.087152915240008, "J_D_1KI": 5.885153392788803}

58
pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 7.140163421630859}
['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/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.612572193145752}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.1948, 0.1869, 0.5638, ..., 0.6155, 0.6170, 0.7726])
tensor([0.8854, 0.6217, 0.0819, ..., 0.6959, 0.1904, 0.8047])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 7.140163421630859 seconds
Time: 0.612572193145752 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1470 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 9.107451438903809}
['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 1714 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.564941644668579}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.1872, 0.8693, 0.3135, ..., 0.4431, 0.3648, 0.2379])
tensor([0.7532, 0.1897, 0.3960, ..., 0.4105, 0.1994, 0.9818])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -40,10 +40,7 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 9.107451438903809 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1694 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093}
Time: 10.564941644668579 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +50,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.6416, 0.1879, 0.5321, ..., 0.0693, 0.5314, 0.2281])
tensor([0.7532, 0.1897, 0.3960, ..., 0.4105, 0.1994, 0.9818])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -62,32 +59,13 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.441875219345093 seconds
Time: 10.564941644668579 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.6416, 0.1879, 0.5321, ..., 0.0693, 0.5314, 0.2281])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.441875219345093 seconds
[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2]
[24.04, 23.88, 24.16, 26.0, 26.68, 32.8, 39.08, 45.28, 50.52, 54.72, 54.84, 55.48, 55.4, 55.76]
14.629251956939697
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048}
[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2, 24.0, 23.96, 24.08, 24.08, 24.44, 24.72, 24.6, 24.56, 24.48, 24.32]
437.32
21.866
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048, 'J_1KI': 332.8377149462559, 'W_1KI': 22.751519758217523, 'W_D': 16.675074470420483, 'J_D': 243.94386582851405, 'W_D_1KI': 9.843609486670887, 'J_D_1KI': 5.81086746556723}
[16.6, 16.6, 16.56, 16.76, 17.08, 16.92, 16.92, 17.0, 17.0, 16.8]
[16.96, 16.84, 17.24, 18.56, 20.56, 27.12, 33.44, 40.08, 45.52, 48.68, 48.28, 48.12, 48.24, 48.24]
14.63369345664978
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1714, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.564941644668579, 'TIME_S_1KI': 6.163909944380735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 475.8493723487854, 'W': 32.517380096721375}
[16.6, 16.6, 16.56, 16.76, 17.08, 16.92, 16.92, 17.0, 17.0, 16.8, 16.92, 16.92, 16.96, 17.2, 17.04, 17.04, 16.92, 17.08, 16.92, 16.96]
304.56
15.228
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1714, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.564941644668579, 'TIME_S_1KI': 6.163909944380735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 475.8493723487854, 'W': 32.517380096721375, 'J_1KI': 277.6250713820218, 'W_1KI': 18.971633662031138, 'W_D': 17.289380096721374, 'J_D': 253.0074883909225, 'W_D_1KI': 10.087152915240008, 'J_D_1KI': 5.885153392788803}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.460163831710815, "TIME_S_1KI": 37.89914431779281, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 556.1444871425629, "W": 33.16277212022306, "J_1KI": 2015.016257762909, "W_1KI": 120.15497145008355, "W_D": 17.98077212022306, "J_D": 301.54015029191976, "W_D_1KI": 65.14772507327196, "J_D_1KI": 236.04248214953608}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 275, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.42946195602417, "TIME_S_1KI": 37.92531620372425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 566.1214665985108, "W": 33.90281362428241, "J_1KI": 2058.6235149036756, "W_1KI": 123.28295863375422, "W_D": 18.28381362428241, "J_D": 305.3097450466157, "W_D_1KI": 66.48659499739058, "J_D_1KI": 241.7694363541476}

35
pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.output
View File

@ -1,5 +1,5 @@
['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/389000+_cols/msdoor.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 3.7967092990875244}
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 3.8150064945220947}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,8 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.9111, 0.9233, 0.5316, ..., 0.5920, 0.1518, 0.8793])
tensor([7.3562e-01, 1.2058e-01, 9.9206e-01, ..., 5.5295e-04, 2.9990e-01,
5.0520e-01])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -19,10 +20,10 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 3.7967092990875244 seconds
Time: 3.8150064945220947 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 276 -m matrices/389000+_cols/msdoor.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.460163831710815}
['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 275 -m matrices/389000+_cols/msdoor.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.42946195602417}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +34,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.6477, 0.4686, 0.6140, ..., 0.5698, 0.0750, 0.2417])
tensor([0.6989, 0.0414, 0.3885, ..., 0.0161, 0.6882, 0.8990])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -42,7 +43,7 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.460163831710815 seconds
Time: 10.42946195602417 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +54,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.6477, 0.4686, 0.6140, ..., 0.5698, 0.0750, 0.2417])
tensor([0.6989, 0.0414, 0.3885, ..., 0.0161, 0.6882, 0.8990])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -62,13 +63,13 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.460163831710815 seconds
Time: 10.42946195602417 seconds
[16.64, 16.68, 16.6, 16.4, 16.68, 16.76, 16.96, 17.0, 17.04, 17.04]
[16.68, 16.64, 16.8, 20.28, 21.28, 25.56, 26.76, 29.96, 35.88, 41.68, 46.2, 50.76, 50.72, 50.92, 50.4, 50.36]
16.77014470100403
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.460163831710815, 'TIME_S_1KI': 37.89914431779281, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 556.1444871425629, 'W': 33.16277212022306}
[16.64, 16.68, 16.6, 16.4, 16.68, 16.76, 16.96, 17.0, 17.04, 17.04, 16.92, 17.2, 17.16, 17.16, 17.04, 16.8, 17.0, 16.76, 16.76, 16.68]
303.64
15.181999999999999
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.460163831710815, 'TIME_S_1KI': 37.89914431779281, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 556.1444871425629, 'W': 33.16277212022306, 'J_1KI': 2015.016257762909, 'W_1KI': 120.15497145008355, 'W_D': 17.98077212022306, 'J_D': 301.54015029191976, 'W_D_1KI': 65.14772507327196, 'J_D_1KI': 236.04248214953608}
[16.56, 16.68, 16.72, 17.0, 17.28, 17.32, 17.24, 17.24, 17.44, 16.96]
[16.8, 16.8, 17.0, 17.96, 19.72, 23.2, 25.16, 32.12, 38.4, 44.28, 48.48, 52.76, 52.8, 52.88, 52.84, 52.84]
16.698362350463867
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 275, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.42946195602417, 'TIME_S_1KI': 37.92531620372425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 566.1214665985108, 'W': 33.90281362428241}
[16.56, 16.68, 16.72, 17.0, 17.28, 17.32, 17.24, 17.24, 17.44, 16.96, 17.16, 17.6, 17.64, 17.8, 18.0, 17.96, 17.68, 17.44, 17.44, 17.12]
312.38
15.619
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 275, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.42946195602417, 'TIME_S_1KI': 37.92531620372425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 566.1214665985108, 'W': 33.90281362428241, 'J_1KI': 2058.6235149036756, 'W_1KI': 123.28295863375422, 'W_D': 18.28381362428241, 'J_D': 305.3097450466157, 'W_D_1KI': 66.48659499739058, 'J_D_1KI': 241.7694363541476}

2
pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444, "TIME_S_1KI": 33.385778188705444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1758.8809212875371, "W": 47.990539203077404, "J_1KI": 1758.8809212875371, "W_1KI": 47.990539203077404, "W_D": 26.191539203077404, "J_D": 959.9350073668963, "W_D_1KI": 26.191539203077404, "J_D_1KI": 26.191539203077404}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 329, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.213600873947144, "TIME_S_1KI": 31.044379556070346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 510.11903370857243, "W": 32.53359816182552, "J_1KI": 1550.513780269217, "W_1KI": 98.88631660129337, "W_D": 17.313598161825524, "J_D": 271.47307593822484, "W_D_1KI": 52.624918425001596, "J_D_1KI": 159.9541593465094}

51
pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444}
['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/389000+_cols/test1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.1875340938568115}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3716, 0.7020, 0.0579, ..., 0.5562, 0.4218, 0.2724])
tensor([0.5119, 0.6550, 0.1519, ..., 0.5345, 0.5557, 0.3293])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -19,7 +19,10 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 33.385778188705444 seconds
Time: 3.1875340938568115 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 329 -m matrices/389000+_cols/test1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.213600873947144}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -30,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3716, 0.7020, 0.0579, ..., 0.5562, 0.4218, 0.2724])
tensor([0.1109, 0.9152, 0.4054, ..., 0.5953, 0.4611, 0.1490])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -39,13 +42,33 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 33.385778188705444 seconds
Time: 10.213600873947144 seconds
[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08]
[24.24, 24.16, 24.36, 28.92, 31.16, 35.28, 37.04, 40.44, 46.16, 49.16, 54.2, 56.72, 56.48, 56.72, 56.72, 56.48, 57.24, 56.52, 56.44, 56.56, 56.52, 57.2, 56.84, 56.8, 56.92, 56.92, 58.08, 57.8, 57.88, 57.44, 57.4, 56.88, 56.44, 57.16, 57.12]
36.65057635307312
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404}
[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08, 24.24, 24.24, 24.24, 24.28, 24.48, 24.36, 24.4, 24.16, 24.16, 24.32]
435.98
21.799
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404, 'J_1KI': 1758.8809212875371, 'W_1KI': 47.990539203077404, 'W_D': 26.191539203077404, 'J_D': 959.9350073668963, 'W_D_1KI': 26.191539203077404, 'J_D_1KI': 26.191539203077404}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.1109, 0.9152, 0.4054, ..., 0.5953, 0.4611, 0.1490])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.213600873947144 seconds
[17.32, 16.96, 16.96, 16.84, 16.68, 16.6, 16.48, 16.48, 16.76, 16.72]
[17.32, 17.32, 17.84, 19.64, 21.04, 24.16, 26.64, 32.2, 38.32, 43.04, 48.6, 50.84, 51.0, 50.84, 50.84]
15.679760694503784
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 329, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.213600873947144, 'TIME_S_1KI': 31.044379556070346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 510.11903370857243, 'W': 32.53359816182552}
[17.32, 16.96, 16.96, 16.84, 16.68, 16.6, 16.48, 16.48, 16.76, 16.72, 16.84, 16.8, 16.8, 17.04, 17.2, 17.28, 17.44, 17.16, 17.08, 16.8]
304.4
15.219999999999999
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 329, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.213600873947144, 'TIME_S_1KI': 31.044379556070346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 510.11903370857243, 'W': 32.53359816182552, 'J_1KI': 1550.513780269217, 'W_1KI': 98.88631660129337, 'W_D': 17.313598161825524, 'J_D': 271.47307593822484, 'W_D_1KI': 52.624918425001596, 'J_D_1KI': 159.9541593465094}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20402, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435, "TIME_S_1KI": 0.5215651123515065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.5667917442322, "W": 146.96, "J_1KI": 97.3711788914926, "W_1KI": 7.203215371042055, "W_D": 111.28150000000001, "J_D": 1504.2741728054286, "W_D_1KI": 5.454440741103813, "J_D_1KI": 0.2673483355114113}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19834, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.26876711845398, "TIME_S_1KI": 0.5177355610796601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1929.6995246815682, "W": 146.81, "J_1KI": 97.29250401742303, "W_1KI": 7.40193606937582, "W_D": 110.58925, "J_D": 1453.6068602948785, "W_D_1KI": 5.575741151557931, "J_D_1KI": 0.28112035653715495}

66
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.5630712509155273}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.08910059928894043}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.4842, 0.5105, 0.4860, ..., 0.7675, 0.4934, 0.1706])
tensor([0.8696, 0.7677, 0.4203, ..., 0.8884, 0.9521, 0.5613])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 0.5630712509155273 seconds
Time: 0.08910059928894043 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18647', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.59645700454712}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '11784', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 6.655792474746704}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6749, 0.4854, 0.2428, ..., 0.8655, 0.6324, 0.8376])
tensor([0.6044, 0.8209, 0.6264, ..., 0.2561, 0.5060, 0.4891])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -40,10 +40,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 9.59645700454712 seconds
Time: 6.655792474746704 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20402', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18590', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.841410398483276}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0724, 0.4329, 0.4595, ..., 0.8349, 0.8167, 0.6766])
tensor([0.4688, 0.0705, 0.1588, ..., 0.4616, 0.9274, 0.1459])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -62,7 +62,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.640971422195435 seconds
Time: 9.841410398483276 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19834', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.26876711845398}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -72,7 +75,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0724, 0.4329, 0.4595, ..., 0.8349, 0.8167, 0.6766])
tensor([0.3652, 0.4115, 0.5586, ..., 0.9928, 0.9446, 0.6145])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -81,13 +84,32 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.640971422195435 seconds
Time: 10.26876711845398 seconds
[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95]
[146.96]
13.517738103866577
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96}
[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95, 39.85, 39.43, 39.2, 39.39, 39.57, 39.13, 39.11, 38.95, 44.59, 39.27]
713.5699999999999
35.6785
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96, 'J_1KI': 97.3711788914926, 'W_1KI': 7.203215371042055, 'W_D': 111.28150000000001, 'J_D': 1504.2741728054286, 'W_D_1KI': 5.454440741103813, 'J_D_1KI': 0.2673483355114113}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.3652, 0.4115, 0.5586, ..., 0.9928, 0.9446, 0.6145])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.26876711845398 seconds
[46.11, 41.22, 39.92, 39.9, 39.99, 39.7, 40.48, 41.26, 40.32, 40.25]
[146.81]
13.14419674873352
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19834, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.26876711845398, 'TIME_S_1KI': 0.5177355610796601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.6995246815682, 'W': 146.81}
[46.11, 41.22, 39.92, 39.9, 39.99, 39.7, 40.48, 41.26, 40.32, 40.25, 41.32, 39.71, 39.98, 39.69, 39.76, 39.86, 39.67, 39.63, 39.71, 39.55]
724.415
36.220749999999995
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19834, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.26876711845398, 'TIME_S_1KI': 0.5177355610796601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.6995246815682, 'W': 146.81, 'J_1KI': 97.29250401742303, 'W_1KI': 7.40193606937582, 'W_D': 110.58925, 'J_D': 1453.6068602948785, 'W_D_1KI': 5.575741151557931, 'J_D_1KI': 0.28112035653715495}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30984, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501, "TIME_S_1KI": 0.33298414897894074, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.101525504589, "W": 149.37, "J_1KI": 61.84164489751449, "W_1KI": 4.820875290472502, "W_D": 113.45200000000001, "J_D": 1455.3494695825577, "W_D_1KI": 3.661631809966435, "J_D_1KI": 0.11817815033457382}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.523735046386719, "TIME_S_1KI": 0.3362323092235125, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1943.7005113601683, "W": 148.13, "J_1KI": 62.10104192977949, "W_1KI": 4.732739065145851, "W_D": 112.20949999999999, "J_D": 1472.3665869808196, "W_D_1KI": 3.5850825904980987, "J_D_1KI": 0.11454303941014406}

44
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.39348506927490234}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.06582307815551758}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.1720, 0.8662, 0.8556, ..., 0.0402, 0.8663, 0.3929])
tensor([0.4399, 0.0930, 0.2889, ..., 0.8965, 0.3350, 0.2920])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 0.39348506927490234 seconds
Time: 0.06582307815551758 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26684', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 9.042525291442871}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '15951', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 5.350985050201416}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.7375, 0.5933, 0.9050, ..., 0.8578, 0.6740, 0.2052])
tensor([0.4355, 0.6168, 0.7478, ..., 0.8719, 0.1231, 0.2000])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -42,10 +42,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 9.042525291442871 seconds
Time: 5.350985050201416 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30984', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31299', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.523735046386719}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -56,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.0633, 0.8834, 0.2857, ..., 0.1984, 0.6858, 0.2922])
tensor([0.7730, 0.2459, 0.8687, ..., 0.8555, 0.1868, 0.7916])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -65,7 +65,7 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.317180871963501 seconds
Time: 10.523735046386719 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -76,7 +76,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.0633, 0.8834, 0.2857, ..., 0.1984, 0.6858, 0.2922])
tensor([0.7730, 0.2459, 0.8687, ..., 0.8555, 0.1868, 0.7916])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -85,13 +85,13 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.317180871963501 seconds
Time: 10.523735046386719 seconds
[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22]
[149.37]
12.827887296676636
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37}
[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22, 40.56, 39.23, 39.36, 44.65, 39.73, 38.95, 39.17, 38.94, 39.11, 39.04]
718.3599999999999
35.91799999999999
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37, 'J_1KI': 61.84164489751449, 'W_1KI': 4.820875290472502, 'W_D': 113.45200000000001, 'J_D': 1455.3494695825577, 'W_D_1KI': 3.661631809966435, 'J_D_1KI': 0.11817815033457382}
[40.48, 40.26, 39.69, 39.63, 39.75, 39.55, 39.71, 39.55, 39.83, 39.84]
[148.13]
13.121585845947266
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.523735046386719, 'TIME_S_1KI': 0.3362323092235125, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.7005113601683, 'W': 148.13}
[40.48, 40.26, 39.69, 39.63, 39.75, 39.55, 39.71, 39.55, 39.83, 39.84, 40.6, 40.67, 39.68, 40.49, 39.95, 40.07, 40.02, 39.84, 39.54, 39.44]
718.4100000000001
35.920500000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.523735046386719, 'TIME_S_1KI': 0.3362323092235125, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.7005113601683, 'W': 148.13, 'J_1KI': 62.10104192977949, 'W_1KI': 4.732739065145851, 'W_D': 112.20949999999999, 'J_D': 1472.3665869808196, 'W_D_1KI': 3.5850825904980987, 'J_D_1KI': 0.11454303941014406}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30366, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355, "TIME_S_1KI": 0.33961335307625473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1775.0306821584702, "W": 137.59, "J_1KI": 58.454543968862225, "W_1KI": 4.531054468813805, "W_D": 102.3395, "J_D": 1320.2685696399212, "W_D_1KI": 3.370200223934664, "J_D_1KI": 0.11098597852646591}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.455351829528809, "TIME_S_1KI": 0.34021058927270625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1778.4344532775879, "W": 138.56, "J_1KI": 57.869141392606664, "W_1KI": 4.508655473122478, "W_D": 102.3395, "J_D": 1313.5399302194119, "W_D_1KI": 3.3300631263829237, "J_D_1KI": 0.10835816498707938}

44
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output
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@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3785414695739746}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.06256437301635742}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.2846, 0.0684, 0.3415, ..., 0.3157, 0.0663, 0.9624])
tensor([0.2581, 0.4160, 0.4313, ..., 0.6707, 0.6252, 0.3825])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 0.3785414695739746 seconds
Time: 0.06256437301635742 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '27738', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.591154098510742}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '16782', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 5.733691215515137}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.1194, 0.5235, 0.5697, ..., 0.7322, 0.5132, 0.4627])
tensor([0.6395, 0.3773, 0.7697, ..., 0.3156, 0.5090, 0.9266])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -40,10 +40,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 9.591154098510742 seconds
Time: 5.733691215515137 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30366', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30732', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.455351829528809}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.4535, 0.4285, 0.5680, ..., 0.4151, 0.8586, 0.5793])
tensor([0.7462, 0.3665, 0.1709, ..., 0.3913, 0.0839, 0.5832])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -62,7 +62,7 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.31269907951355 seconds
Time: 10.455351829528809 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -72,7 +72,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.4535, 0.4285, 0.5680, ..., 0.4151, 0.8586, 0.5793])
tensor([0.7462, 0.3665, 0.1709, ..., 0.3913, 0.0839, 0.5832])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -81,13 +81,13 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.31269907951355 seconds
Time: 10.455351829528809 seconds
[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57]
[137.59]
12.900869846343994
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59}
[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57, 39.44, 38.8, 39.85, 39.35, 38.9, 38.8, 38.74, 39.19, 39.29, 38.76]
705.01
35.2505
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59, 'J_1KI': 58.454543968862225, 'W_1KI': 4.531054468813805, 'W_D': 102.3395, 'J_D': 1320.2685696399212, 'W_D_1KI': 3.370200223934664, 'J_D_1KI': 0.11098597852646591}
[40.22, 39.55, 44.81, 39.94, 39.86, 41.29, 39.59, 39.52, 39.68, 39.43]
[138.56]
12.835121631622314
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.455351829528809, 'TIME_S_1KI': 0.34021058927270625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1778.4344532775879, 'W': 138.56}
[40.22, 39.55, 44.81, 39.94, 39.86, 41.29, 39.59, 39.52, 39.68, 39.43, 40.24, 39.65, 39.73, 39.42, 39.89, 39.63, 41.73, 40.12, 40.16, 39.79]
724.4100000000001
36.2205
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.455351829528809, 'TIME_S_1KI': 0.34021058927270625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1778.4344532775879, 'W': 138.56, 'J_1KI': 57.869141392606664, 'W_1KI': 4.508655473122478, 'W_D': 102.3395, 'J_D': 1313.5399302194119, 'W_D_1KI': 3.3300631263829237, 'J_D_1KI': 0.10835816498707938}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19495, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956, "TIME_S_1KI": 0.5495852401666869, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2110.003728497028, "W": 158.07, "J_1KI": 108.23307147971418, "W_1KI": 8.108232880225698, "W_D": 122.3625, "J_D": 1633.3607340306044, "W_D_1KI": 6.276609387022313, "J_D_1KI": 0.32195995829814383}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19017, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.458677291870117, "TIME_S_1KI": 0.549964625959411, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2018.272961883545, "W": 155.04, "J_1KI": 106.12993436838327, "W_1KI": 8.152705474049533, "W_D": 118.88175, "J_D": 1547.57366928792, "W_D_1KI": 6.2513409055056, "J_D_1KI": 0.3287238210814324}

44
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.6046795845031738}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.09623193740844727}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7423, 0.4233, 0.1707, ..., 0.4030, 0.8937, 0.1151])
tensor([0.0021, 0.1389, 0.5908, ..., 0.3513, 0.8998, 0.0779])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 0.6046795845031738 seconds
Time: 0.09623193740844727 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17364', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 9.352032661437988}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '10911', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 6.024064302444458}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.3036, 0.7996, 0.4739, ..., 0.0238, 0.6033, 0.9918])
tensor([0.2048, 0.7810, 0.9251, ..., 0.9609, 0.7921, 0.5584])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -42,10 +42,10 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 9.352032661437988 seconds
Time: 6.024064302444458 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19495', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19017', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.458677291870117}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -56,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1117, 0.6424, 0.8924, ..., 0.5333, 0.0312, 0.4242])
tensor([0.9504, 0.1558, 0.7077, ..., 0.9646, 0.1483, 0.5744])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -65,7 +65,7 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.71416425704956 seconds
Time: 10.458677291870117 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -76,7 +76,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1117, 0.6424, 0.8924, ..., 0.5333, 0.0312, 0.4242])
tensor([0.9504, 0.1558, 0.7077, ..., 0.9646, 0.1483, 0.5744])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -85,13 +85,13 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.71416425704956 seconds
Time: 10.458677291870117 seconds
[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37]
[158.07]
13.34854006767273
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07}
[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37, 41.26, 39.31, 39.83, 39.61, 39.66, 39.59, 39.58, 39.11, 39.31, 39.11]
714.15
35.707499999999996
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07, 'J_1KI': 108.23307147971418, 'W_1KI': 8.108232880225698, 'W_D': 122.3625, 'J_D': 1633.3607340306044, 'W_D_1KI': 6.276609387022313, 'J_D_1KI': 0.32195995829814383}
[40.45, 40.82, 40.24, 40.2, 40.27, 39.65, 39.74, 40.44, 39.91, 39.62]
[155.04]
13.017756462097168
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19017, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.458677291870117, 'TIME_S_1KI': 0.549964625959411, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2018.272961883545, 'W': 155.04}
[40.45, 40.82, 40.24, 40.2, 40.27, 39.65, 39.74, 40.44, 39.91, 39.62, 40.39, 39.79, 40.12, 39.57, 39.74, 39.85, 40.5, 39.97, 39.71, 44.83]
723.165
36.158249999999995
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19017, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.458677291870117, 'TIME_S_1KI': 0.549964625959411, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2018.272961883545, 'W': 155.04, 'J_1KI': 106.12993436838327, 'W_1KI': 8.152705474049533, 'W_D': 118.88175, 'J_D': 1547.57366928792, 'W_D_1KI': 6.2513409055056, 'J_D_1KI': 0.3287238210814324}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 23986, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974, "TIME_S_1KI": 0.4254478952221285, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1608.246218369007, "W": 138.89, "J_1KI": 67.04937123192725, "W_1KI": 5.79046110230968, "W_D": 103.45274999999998, "J_D": 1197.9083732981082, "W_D_1KI": 4.313047194196614, "J_D_1KI": 0.1798151919534985}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 26430, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.683592081069946, "TIME_S_1KI": 0.40422217484184436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1751.0713453888893, "W": 139.15, "J_1KI": 66.2531723567495, "W_1KI": 5.264850548618994, "W_D": 102.79675, "J_D": 1293.6000238886477, "W_D_1KI": 3.889396519107075, "J_D_1KI": 0.14715840026890184}

58
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.43773889541625977}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.07805013656616211}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9846, 0.2787, 0.2893, ..., 0.3452, 0.1271, 0.5089])
tensor([0.6892, 0.2287, 0.4946, ..., 0.5869, 0.8658, 0.8397])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 0.43773889541625977 seconds
Time: 0.07805013656616211 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23986', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '13452', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 5.3439531326293945}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.2099, 0.5726, 0.9552, ..., 0.7541, 0.8652, 0.1203])
tensor([0.2184, 0.6500, 0.9412, ..., 0.4516, 0.1296, 0.0617])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -40,7 +40,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.204793214797974 seconds
Time: 5.3439531326293945 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26430', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.683592081069946}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.2099, 0.5726, 0.9552, ..., 0.7541, 0.8652, 0.1203])
tensor([0.6226, 0.2270, 0.7151, ..., 0.0099, 0.3705, 0.6085])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -59,13 +62,32 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.204793214797974 seconds
Time: 10.683592081069946 seconds
[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21]
[138.89]
11.579280138015747
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89}
[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21, 41.54, 39.4, 39.48, 39.09, 39.42, 38.94, 39.43, 39.44, 39.0, 41.44]
708.7450000000001
35.437250000000006
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89, 'J_1KI': 67.04937123192725, 'W_1KI': 5.79046110230968, 'W_D': 103.45274999999998, 'J_D': 1197.9083732981082, 'W_D_1KI': 4.313047194196614, 'J_D_1KI': 0.1798151919534985}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.6226, 0.2270, 0.7151, ..., 0.0099, 0.3705, 0.6085])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.683592081069946 seconds
[40.83, 45.04, 40.3, 39.72, 40.5, 40.2, 39.92, 39.73, 39.77, 39.65]
[139.15]
12.584055662155151
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 26430, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.683592081069946, 'TIME_S_1KI': 0.40422217484184436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1751.0713453888893, 'W': 139.15}
[40.83, 45.04, 40.3, 39.72, 40.5, 40.2, 39.92, 39.73, 39.77, 39.65, 40.37, 39.93, 39.69, 39.68, 39.78, 42.28, 40.08, 40.21, 40.05, 39.52]
727.065
36.35325
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 26430, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.683592081069946, 'TIME_S_1KI': 0.40422217484184436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1751.0713453888893, 'W': 139.15, 'J_1KI': 66.2531723567495, 'W_1KI': 5.264850548618994, 'W_D': 102.79675, 'J_D': 1293.6000238886477, 'W_D_1KI': 3.889396519107075, 'J_D_1KI': 0.14715840026890184}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2078, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.368424892425537, "TIME_S_1KI": 4.989617368828458, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1768.3314125061036, "W": 128.56, "J_1KI": 850.9775806092895, "W_1KI": 61.86717998075073, "W_D": 92.98825, "J_D": 1279.0451421046257, "W_D_1KI": 44.74891722810394, "J_D_1KI": 21.534608868192468}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2189, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 11.089002132415771, "TIME_S_1KI": 5.065784436919037, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1911.132792377472, "W": 128.4, "J_1KI": 873.062033977831, "W_1KI": 58.65692096847876, "W_D": 91.88425000000001, "J_D": 1367.6246361215713, "W_D_1KI": 41.9754454088625, "J_D_1KI": 19.175626043335996}

57
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 0.505284309387207}
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 0.5332725048065186}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.0979, 0.4410, 0.6794, ..., 0.5232, 0.5066, 0.8230])
tensor([0.9382, 0.7018, 0.2068, ..., 0.4302, 0.0724, 0.0423])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 0.505284309387207 seconds
Time: 0.5332725048065186 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2078', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.368424892425537}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1968', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 9.439205408096313}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.1753, 0.8799, 0.4934, ..., 0.3593, 0.7648, 0.6193])
tensor([0.1799, 0.9063, 0.8899, ..., 0.5959, 0.6594, 0.3795])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -42,7 +42,10 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.368424892425537 seconds
Time: 9.439205408096313 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2189', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 11.089002132415771}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.1753, 0.8799, 0.4934, ..., 0.3593, 0.7648, 0.6193])
tensor([0.0588, 0.9972, 0.8707, ..., 0.0858, 0.7679, 0.3977])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -62,13 +65,33 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.368424892425537 seconds
Time: 11.089002132415771 seconds
[41.45, 39.25, 39.16, 39.53, 39.32, 39.64, 39.36, 39.48, 39.45, 39.21]
[128.56]
13.754911422729492
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2078, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.368424892425537, 'TIME_S_1KI': 4.989617368828458, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1768.3314125061036, 'W': 128.56}
[41.45, 39.25, 39.16, 39.53, 39.32, 39.64, 39.36, 39.48, 39.45, 39.21, 39.72, 39.17, 39.05, 38.92, 39.31, 39.68, 39.47, 38.94, 39.39, 44.25]
711.4350000000001
35.57175
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2078, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.368424892425537, 'TIME_S_1KI': 4.989617368828458, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1768.3314125061036, 'W': 128.56, 'J_1KI': 850.9775806092895, 'W_1KI': 61.86717998075073, 'W_D': 92.98825, 'J_D': 1279.0451421046257, 'W_D_1KI': 44.74891722810394, 'J_D_1KI': 21.534608868192468}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.0588, 0.9972, 0.8707, ..., 0.0858, 0.7679, 0.3977])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 11.089002132415771 seconds
[40.51, 39.73, 39.73, 39.62, 39.92, 39.21, 39.51, 40.17, 39.68, 39.2]
[128.4]
14.884211778640747
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2189, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 11.089002132415771, 'TIME_S_1KI': 5.065784436919037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.132792377472, 'W': 128.4}
[40.51, 39.73, 39.73, 39.62, 39.92, 39.21, 39.51, 40.17, 39.68, 39.2, 40.41, 39.78, 40.36, 39.6, 55.78, 39.29, 39.36, 39.22, 39.24, 40.11]
730.315
36.515750000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2189, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 11.089002132415771, 'TIME_S_1KI': 5.065784436919037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.132792377472, 'W': 128.4, 'J_1KI': 873.062033977831, 'W_1KI': 58.65692096847876, 'W_D': 91.88425000000001, 'J_D': 1367.6246361215713, 'W_D_1KI': 41.9754454088625, 'J_D_1KI': 19.175626043335996}

2
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2652, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064, "TIME_S_1KI": 4.079358610092784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1727.0440453481674, "W": 123.26, "J_1KI": 651.2232448522501, "W_1KI": 46.47812971342383, "W_D": 87.892, "J_D": 1231.4891711320877, "W_D_1KI": 33.14177978883861, "J_D_1KI": 12.49690037286524}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2836, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.750332117080688, "TIME_S_1KI": 3.7906671780961525, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1919.3007157278062, "W": 123.61, "J_1KI": 676.7632989167158, "W_1KI": 43.58603667136812, "W_D": 87.49625, "J_D": 1358.5601104158163, "W_D_1KI": 30.851992242595205, "J_D_1KI": 10.878699662410156}

59
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.9585680961608887}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 0.40934228897094727}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.7261, 0.8238, 0.5826, ..., 0.6988, 0.4899, 0.5621])
tensor([0.9755, 0.6702, 0.0483, ..., 0.0474, 0.5158, 0.9707])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 3.9585680961608887 seconds
Time: 0.40934228897094727 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2652', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2565', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 9.495455026626587}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.8039, 0.1348, 0.9933, ..., 0.2390, 0.5536, 0.8375])
tensor([0.3860, 0.9003, 0.5840, ..., 0.1645, 0.2674, 0.3373])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -42,7 +42,10 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.818459033966064 seconds
Time: 9.495455026626587 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2836', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.750332117080688}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.8039, 0.1348, 0.9933, ..., 0.2390, 0.5536, 0.8375])
tensor([0.2910, 0.5006, 0.8630, ..., 0.4355, 0.5106, 0.0457])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -62,13 +65,33 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.818459033966064 seconds
Time: 10.750332117080688 seconds
[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93]
[123.26]
14.011390924453735
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26}
[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93, 39.84, 39.42, 39.65, 39.42, 38.97, 39.03, 39.01, 38.92, 39.07, 38.77]
707.36
35.368
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26, 'J_1KI': 651.2232448522501, 'W_1KI': 46.47812971342383, 'W_D': 87.892, 'J_D': 1231.4891711320877, 'W_D_1KI': 33.14177978883861, 'J_D_1KI': 12.49690037286524}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.2910, 0.5006, 0.8630, ..., 0.4355, 0.5106, 0.0457])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.750332117080688 seconds
[40.92, 40.18, 39.97, 39.85, 39.82, 39.64, 41.26, 40.35, 39.81, 40.97]
[123.61]
15.527066707611084
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2836, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.750332117080688, 'TIME_S_1KI': 3.7906671780961525, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1919.3007157278062, 'W': 123.61}
[40.92, 40.18, 39.97, 39.85, 39.82, 39.64, 41.26, 40.35, 39.81, 40.97, 40.91, 40.87, 40.2, 40.27, 39.65, 39.86, 39.82, 39.57, 39.82, 39.87]
722.2750000000001
36.11375
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2836, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.750332117080688, 'TIME_S_1KI': 3.7906671780961525, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1919.3007157278062, 'W': 123.61, 'J_1KI': 676.7632989167158, 'W_1KI': 43.58603667136812, 'W_D': 87.49625, 'J_D': 1358.5601104158163, 'W_D_1KI': 30.851992242595205, 'J_D_1KI': 10.878699662410156}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125, "TIME_S_1KI": 1.3436493423661153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.0154501628876, "W": 88.97, "J_1KI": 160.2630512642138, "W_1KI": 10.959595959595958, "W_D": 72.9665, "J_D": 1066.9949853243827, "W_D_1KI": 8.988236018723823, "J_D_1KI": 1.1071983270169774}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7987, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.616116523742676, "TIME_S_1KI": 1.329174473988065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1292.274513721466, "W": 89.32, "J_1KI": 161.79723472160586, "W_1KI": 11.183172655565293, "W_D": 72.34549999999999, "J_D": 1046.6888248145578, "W_D_1KI": 9.05790659822211, "J_D_1KI": 1.134081206738714}

58
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.293351411819458}
['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/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.14266586303710938}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.2979, 0.5597, 0.8769, ..., 0.0942, 0.8424, 0.4292])
tensor([0.1268, 0.3496, 0.2391, ..., 0.9527, 0.4632, 0.7126])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 1.293351411819458 seconds
Time: 0.14266586303710938 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '8118', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125}
['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', '7359', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.674143314361572}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6448, 0.4532, 0.0841, ..., 0.5791, 0.6160, 0.9399])
tensor([0.7090, 0.5442, 0.0933, ..., 0.6597, 0.1230, 0.9571])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -40,7 +40,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.907745361328125 seconds
Time: 9.674143314361572 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', '7987', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.616116523742676}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6448, 0.4532, 0.0841, ..., 0.5791, 0.6160, 0.9399])
tensor([0.5407, 0.5721, 0.8153, ..., 0.1294, 0.2603, 0.5077])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -59,13 +62,32 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.907745361328125 seconds
Time: 10.616116523742676 seconds
[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98]
[88.97]
14.623080253601074
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97}
[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98, 18.52, 17.6, 17.66, 17.43, 18.0, 17.58, 17.75, 17.82, 17.89, 17.62]
320.06999999999994
16.003499999999995
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97, 'J_1KI': 160.2630512642138, 'W_1KI': 10.959595959595958, 'W_D': 72.9665, 'J_D': 1066.9949853243827, 'W_D_1KI': 8.988236018723823, 'J_D_1KI': 1.1071983270169774}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.5407, 0.5721, 0.8153, ..., 0.1294, 0.2603, 0.5077])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.616116523742676 seconds
[19.03, 18.55, 19.15, 18.47, 22.1, 18.8, 18.61, 18.73, 18.49, 18.69]
[89.32]
14.467918872833252
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7987, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.616116523742676, 'TIME_S_1KI': 1.329174473988065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.274513721466, 'W': 89.32}
[19.03, 18.55, 19.15, 18.47, 22.1, 18.8, 18.61, 18.73, 18.49, 18.69, 19.41, 18.36, 19.31, 18.4, 18.44, 18.42, 18.7, 18.54, 18.61, 18.49]
339.49
16.9745
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7987, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.616116523742676, 'TIME_S_1KI': 1.329174473988065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.274513721466, 'W': 89.32, 'J_1KI': 161.79723472160586, 'W_1KI': 11.183172655565293, 'W_D': 72.34549999999999, 'J_D': 1046.6888248145578, 'W_D_1KI': 9.05790659822211, 'J_D_1KI': 1.134081206738714}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12165, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207, "TIME_S_1KI": 0.8646259864465327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.3463970065116, "W": 89.63, "J_1KI": 105.98819539716494, "W_1KI": 7.3678586107685975, "W_D": 73.37299999999999, "J_D": 1055.4860335552692, "W_D_1KI": 6.031483764899301, "J_D_1KI": 0.49580631030820393}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12041, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.56082010269165, "TIME_S_1KI": 0.877071680316556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1296.8684873652458, "W": 90.11, "J_1KI": 107.70438396854462, "W_1KI": 7.483597707831575, "W_D": 73.30125, "J_D": 1054.955956158936, "W_D_1KI": 6.087638069927746, "J_D_1KI": 0.5055757885497671}

59
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.8631081581115723}
['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/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.09853219985961914}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.5077, 0.7328, 0.3933, ..., 0.4074, 0.1030, 0.0500])
tensor([0.2753, 0.6481, 0.6704, ..., 0.6276, 0.5741, 0.4730])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 0.8631081581115723 seconds
Time: 0.09853219985961914 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12165', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207}
['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', '10656', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 9.291776657104492}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.9100, 0.1506, 0.3829, ..., 0.6719, 0.7400, 0.8631])
tensor([0.9610, 0.5342, 0.1151, ..., 0.3000, 0.0945, 0.0583])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -42,7 +42,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.51817512512207 seconds
Time: 9.291776657104492 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', '12041', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.56082010269165}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.9100, 0.1506, 0.3829, ..., 0.6719, 0.7400, 0.8631])
tensor([0.3062, 0.0914, 0.7526, ..., 0.1745, 0.1449, 0.1864])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -62,13 +65,33 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.51817512512207 seconds
Time: 10.56082010269165 seconds
[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97]
[89.63]
14.385210275650024
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63}
[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97, 18.65, 17.64, 17.75, 18.23, 17.75, 18.05, 17.76, 17.53, 18.1, 17.73]
325.14
16.256999999999998
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63, 'J_1KI': 105.98819539716494, 'W_1KI': 7.3678586107685975, 'W_D': 73.37299999999999, 'J_D': 1055.4860335552692, 'W_D_1KI': 6.031483764899301, 'J_D_1KI': 0.49580631030820393}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.3062, 0.0914, 0.7526, ..., 0.1745, 0.1449, 0.1864])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.56082010269165 seconds
[18.99, 18.61, 18.75, 18.43, 18.78, 18.82, 18.59, 18.55, 18.54, 18.9]
[90.11]
14.392059564590454
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12041, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.56082010269165, 'TIME_S_1KI': 0.877071680316556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.8684873652458, 'W': 90.11}
[18.99, 18.61, 18.75, 18.43, 18.78, 18.82, 18.59, 18.55, 18.54, 18.9, 18.63, 18.39, 18.89, 18.37, 19.26, 18.44, 19.38, 18.53, 18.39, 18.39]
336.17500000000007
16.808750000000003
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12041, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.56082010269165, 'TIME_S_1KI': 0.877071680316556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.8684873652458, 'W': 90.11, 'J_1KI': 107.70438396854462, 'W_1KI': 7.483597707831575, 'W_D': 73.30125, 'J_D': 1054.955956158936, 'W_D_1KI': 6.087638069927746, 'J_D_1KI': 0.5055757885497671}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13328, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792, "TIME_S_1KI": 0.8063470007849484, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1285.765242910385, "W": 89.74, "J_1KI": 96.47098161092326, "W_1KI": 6.733193277310924, "W_D": 73.49574999999999, "J_D": 1053.0229646939038, "W_D_1KI": 5.51438700480192, "J_D_1KI": 0.4137445231694118}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13046, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.388671159744263, "TIME_S_1KI": 0.7963108354855329, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1281.0084666013718, "W": 90.5, "J_1KI": 98.1916653841309, "W_1KI": 6.936992181511575, "W_D": 73.38575, "J_D": 1038.759857214272, "W_D_1KI": 5.625153303694619, "J_D_1KI": 0.4311783921274428}

58
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.7877652645111084}
['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/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.09250688552856445}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0377, 0.4846, 0.1087, ..., 0.8181, 0.0416, 0.1571])
tensor([0.0435, 0.9348, 0.5413, ..., 0.5401, 0.4750, 0.4498])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 0.7877652645111084 seconds
Time: 0.09250688552856445 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13328', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792}
['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', '11350', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.134806632995605}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5687, 0.0271, 0.0300, ..., 0.3524, 0.7739, 0.4785])
tensor([0.6783, 0.0346, 0.4116, ..., 0.7049, 0.0194, 0.7182])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -40,7 +40,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.746992826461792 seconds
Time: 9.134806632995605 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', '13046', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.388671159744263}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5687, 0.0271, 0.0300, ..., 0.3524, 0.7739, 0.4785])
tensor([0.5592, 0.7531, 0.9775, ..., 0.0921, 0.1322, 0.1526])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -59,13 +62,32 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.746992826461792 seconds
Time: 10.388671159744263 seconds
[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87]
[89.74]
14.327671527862549
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74}
[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87, 19.34, 17.9, 18.16, 17.6, 17.86, 17.8, 17.9, 17.65, 17.71, 17.86]
324.885
16.24425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74, 'J_1KI': 96.47098161092326, 'W_1KI': 6.733193277310924, 'W_D': 73.49574999999999, 'J_D': 1053.0229646939038, 'W_D_1KI': 5.51438700480192, 'J_D_1KI': 0.4137445231694118}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5592, 0.7531, 0.9775, ..., 0.0921, 0.1322, 0.1526])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.388671159744263 seconds
[18.71, 18.75, 18.72, 18.74, 19.74, 18.72, 19.74, 18.76, 18.74, 18.58]
[90.5]
14.154789686203003
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13046, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.388671159744263, 'TIME_S_1KI': 0.7963108354855329, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.0084666013718, 'W': 90.5}
[18.71, 18.75, 18.72, 18.74, 19.74, 18.72, 19.74, 18.76, 18.74, 18.58, 19.31, 18.55, 18.5, 18.69, 18.63, 18.55, 18.55, 22.04, 19.36, 18.41]
342.28499999999997
17.11425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13046, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.388671159744263, 'TIME_S_1KI': 0.7963108354855329, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.0084666013718, 'W': 90.5, 'J_1KI': 98.1916653841309, 'W_1KI': 6.936992181511575, 'W_D': 73.38575, 'J_D': 1038.759857214272, 'W_D_1KI': 5.625153303694619, 'J_D_1KI': 0.4311783921274428}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json
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@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5897, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956, "TIME_S_1KI": 1.790718565719783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.1635441350936, "W": 89.05999999999999, "J_1KI": 220.30923251400603, "W_1KI": 15.102594539596403, "W_D": 72.89299999999999, "J_D": 1063.327287476301, "W_D_1KI": 12.361031032728503, "J_D_1KI": 2.096155847503562}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5448, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.082828044891357, "TIME_S_1KI": 1.8507393621313064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.8753697156906, "W": 89.14, "J_1KI": 224.8302807848184, "W_1KI": 16.36196769456681, "W_D": 72.40325, "J_D": 994.8951942154765, "W_D_1KI": 13.289877019089573, "J_D_1KI": 2.439404739186779}

36
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7802655696868896}
['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/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.19272136688232422}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5518, 0.1807, 0.4021, ..., 0.3397, 0.2107, 0.4589])
tensor([0.2224, 0.5236, 0.7159, ..., 0.9943, 0.3868, 0.1466])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 1.7802655696868896 seconds
Time: 0.19272136688232422 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5897', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956}
['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', '5448', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.082828044891357}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5366, 0.8162, 0.5634, ..., 0.9410, 0.0469, 0.5938])
tensor([0.0110, 0.2878, 0.5327, ..., 0.8557, 0.9333, 0.0080])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -42,7 +42,7 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.55986738204956 seconds
Time: 10.082828044891357 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5366, 0.8162, 0.5634, ..., 0.9410, 0.0469, 0.5938])
tensor([0.0110, 0.2878, 0.5327, ..., 0.8557, 0.9333, 0.0080])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -62,13 +62,13 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.55986738204956 seconds
Time: 10.082828044891357 seconds
[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71]
[89.06]
14.587508916854858
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999}
[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71, 18.09, 17.83, 17.84, 17.57, 17.77, 17.94, 21.22, 17.67, 17.65, 17.92]
323.34000000000003
16.167
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999, 'J_1KI': 220.30923251400603, 'W_1KI': 15.102594539596403, 'W_D': 72.89299999999999, 'J_D': 1063.327287476301, 'W_D_1KI': 12.361031032728503, 'J_D_1KI': 2.096155847503562}
[18.89, 18.39, 18.55, 18.95, 18.6, 18.36, 18.56, 18.31, 18.6, 18.45]
[89.14]
13.741029500961304
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5448, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.082828044891357, 'TIME_S_1KI': 1.8507393621313064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.8753697156906, 'W': 89.14}
[18.89, 18.39, 18.55, 18.95, 18.6, 18.36, 18.56, 18.31, 18.6, 18.45, 18.93, 18.53, 18.74, 18.34, 18.45, 18.76, 18.71, 18.55, 18.87, 18.66]
334.735
16.73675
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5448, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.082828044891357, 'TIME_S_1KI': 1.8507393621313064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.8753697156906, 'W': 89.14, 'J_1KI': 224.8302807848184, 'W_1KI': 16.36196769456681, 'W_D': 72.40325, 'J_D': 994.8951942154765, 'W_D_1KI': 13.289877019089573, 'J_D_1KI': 2.439404739186779}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13697, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639, "TIME_S_1KI": 0.7868405283824662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1316.45538356781, "W": 89.82, "J_1KI": 96.11268040941886, "W_1KI": 6.557640359202745, "W_D": 73.69574999999999, "J_D": 1080.128777928829, "W_D_1KI": 5.3804300211725185, "J_D_1KI": 0.3928181369038854}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13367, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.583873987197876, "TIME_S_1KI": 0.7917912760677696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.8493892431259, "W": 90.11, "J_1KI": 97.3179763030692, "W_1KI": 6.741228398294306, "W_D": 73.2445, "J_D": 1057.3750204241276, "W_D_1KI": 5.479501758060897, "J_D_1KI": 0.40992756475356446}

58
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7665784358978271}
['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/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.09272050857543945}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.5208, 0.6283, 0.9927, ..., 0.7747, 0.7207, 0.3302])
tensor([0.0339, 0.6880, 0.1064, ..., 0.6126, 0.2965, 0.3985])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -18,10 +18,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 0.7665784358978271 seconds
Time: 0.09272050857543945 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13697', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639}
['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', '11324', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 8.894986152648926}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -31,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9293, 0.9172, 0.4372, ..., 0.0528, 0.4444, 0.3291])
tensor([0.5644, 0.9664, 0.6231, ..., 0.9445, 0.2321, 0.8017])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -40,7 +40,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.777354717254639 seconds
Time: 8.894986152648926 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', '13367', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.583873987197876}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -50,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9293, 0.9172, 0.4372, ..., 0.0528, 0.4444, 0.3291])
tensor([0.7898, 0.8103, 0.3970, ..., 0.0544, 0.2392, 0.7912])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -59,13 +62,32 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.777354717254639 seconds
Time: 10.583873987197876 seconds
[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56]
[89.82]
14.656595230102539
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82}
[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56, 18.11, 17.79, 18.09, 17.72, 17.66, 18.49, 17.86, 18.34, 17.71, 17.93]
322.485
16.12425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82, 'J_1KI': 96.11268040941886, 'W_1KI': 6.557640359202745, 'W_D': 73.69574999999999, 'J_D': 1080.128777928829, 'W_D_1KI': 5.3804300211725185, 'J_D_1KI': 0.3928181369038854}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.7898, 0.8103, 0.3970, ..., 0.0544, 0.2392, 0.7912])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.583873987197876 seconds
[21.26, 18.98, 18.46, 18.99, 18.56, 18.42, 18.58, 18.46, 18.67, 18.35]
[90.11]
14.436237812042236
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.583873987197876, 'TIME_S_1KI': 0.7917912760677696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.8493892431259, 'W': 90.11}
[21.26, 18.98, 18.46, 18.99, 18.56, 18.42, 18.58, 18.46, 18.67, 18.35, 19.05, 18.59, 18.53, 18.7, 18.43, 18.95, 18.7, 18.49, 18.92, 19.1]
337.31
16.8655
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.583873987197876, 'TIME_S_1KI': 0.7917912760677696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.8493892431259, 'W': 90.11, 'J_1KI': 97.3179763030692, 'W_1KI': 6.741228398294306, 'W_D': 73.2445, 'J_D': 1057.3750204241276, 'W_D_1KI': 5.479501758060897, 'J_D_1KI': 0.40992756475356446}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1333, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.536967515945435, "TIME_S_1KI": 7.9047018124121795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1980.6726791381834, "W": 66.6, "J_1KI": 1485.8759783482246, "W_1KI": 49.962490622655665, "W_D": 49.48649999999999, "J_D": 1471.7200981407163, "W_D_1KI": 37.12415603900975, "J_D_1KI": 27.8500795491446}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1377, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.288378953933716, "TIME_S_1KI": 7.471589654272851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1961.5668319988251, "W": 67.86, "J_1KI": 1424.5220275953704, "W_1KI": 49.28104575163399, "W_D": 50.79175, "J_D": 1468.190570869088, "W_D_1KI": 36.885802469135804, "J_D_1KI": 26.787075140984605}

42
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.output
View File

@ -1,5 +1,5 @@
['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/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 1.0501856803894043}
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 1.0888726711273193}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5509, 0.1350, 0.4677, ..., 0.8467, 0.1931, 0.8337])
tensor([0.6685, 0.4820, 0.6395, ..., 0.5644, 0.3840, 0.3347])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 1.0501856803894043 seconds
Time: 1.0888726711273193 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', '999', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 7.86853814125061}
['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', '964', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 7.347489833831787}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.0083, 0.1850, 0.6844, ..., 0.3666, 0.9050, 0.1703])
tensor([0.7199, 0.3993, 0.5389, ..., 0.6870, 0.8284, 0.4024])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -42,10 +42,10 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 7.86853814125061 seconds
Time: 7.347489833831787 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', '1333', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.536967515945435}
['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', '1377', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.288378953933716}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -56,7 +56,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5281, 0.2288, 0.8014, ..., 0.5844, 0.6890, 0.8626])
tensor([0.2598, 0.3405, 0.6773, ..., 0.0548, 0.4020, 0.2184])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -65,7 +65,7 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.536967515945435 seconds
Time: 10.288378953933716 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -76,7 +76,7 @@ tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5281, 0.2288, 0.8014, ..., 0.5844, 0.6890, 0.8626])
tensor([0.2598, 0.3405, 0.6773, ..., 0.0548, 0.4020, 0.2184])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
@ -85,13 +85,13 @@ Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.536967515945435 seconds
Time: 10.288378953933716 seconds
[19.73, 18.62, 18.78, 18.65, 18.85, 18.62, 18.87, 18.75, 18.73, 18.48]
[66.6]
29.739830017089844
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1333, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.536967515945435, 'TIME_S_1KI': 7.9047018124121795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.6726791381834, 'W': 66.6}
[19.73, 18.62, 18.78, 18.65, 18.85, 18.62, 18.87, 18.75, 18.73, 18.48, 20.53, 18.58, 18.75, 19.13, 18.65, 18.58, 22.49, 18.38, 18.94, 19.06]
342.27
17.1135
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1333, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.536967515945435, 'TIME_S_1KI': 7.9047018124121795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.6726791381834, 'W': 66.6, 'J_1KI': 1485.8759783482246, 'W_1KI': 49.962490622655665, 'W_D': 49.48649999999999, 'J_D': 1471.7200981407163, 'W_D_1KI': 37.12415603900975, 'J_D_1KI': 27.8500795491446}
[19.04, 18.51, 18.73, 18.77, 19.0, 18.32, 18.72, 18.77, 18.58, 18.97]
[67.86]
28.906083583831787
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1377, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.288378953933716, 'TIME_S_1KI': 7.471589654272851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.5668319988251, 'W': 67.86}
[19.04, 18.51, 18.73, 18.77, 19.0, 18.32, 18.72, 18.77, 18.58, 18.97, 18.91, 18.46, 22.85, 19.67, 18.44, 19.0, 18.73, 18.43, 18.63, 18.59]
341.365
17.06825
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1377, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.288378953933716, 'TIME_S_1KI': 7.471589654272851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.5668319988251, 'W': 67.86, 'J_1KI': 1424.5220275953704, 'W_1KI': 49.28104575163399, 'W_D': 50.79175, 'J_D': 1468.190570869088, 'W_D_1KI': 36.885802469135804, 'J_D_1KI': 26.787075140984605}

2
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1887, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512, "TIME_S_1KI": 5.603405680047436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.276729001999, "W": 85.01, "J_1KI": 764.8525325924743, "W_1KI": 45.050344462109166, "W_D": 68.79925, "J_D": 1168.055011149168, "W_D_1KI": 36.459591944886064, "J_D_1KI": 19.321458370368873}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1799, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.745137691497803, "TIME_S_1KI": 5.972839183711953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1417.5379355335235, "W": 85.32, "J_1KI": 787.9588302020697, "W_1KI": 47.426347971095055, "W_D": 68.48425, "J_D": 1137.822578077376, "W_D_1KI": 38.06795441912174, "J_D_1KI": 21.16061946588201}

36
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 5.56341028213501}
['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/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 0.5835962295532227}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.2549, 0.6086, 0.7138, ..., 0.3139, 0.6424, 0.7605])
tensor([0.9384, 0.5825, 0.8299, ..., 0.1374, 0.7029, 0.7608])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -19,10 +19,10 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 5.56341028213501 seconds
Time: 0.5835962295532227 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1887', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512}
['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', '1799', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.745137691497803}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -33,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3061, 0.1365, 0.6683, ..., 0.9071, 0.4159, 0.8227])
tensor([0.9127, 0.2326, 0.0313, ..., 0.3186, 0.6832, 0.4053])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -42,7 +42,7 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.573626518249512 seconds
Time: 10.745137691497803 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -53,7 +53,7 @@ tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3061, 0.1365, 0.6683, ..., 0.9071, 0.4159, 0.8227])
tensor([0.9127, 0.2326, 0.0313, ..., 0.3186, 0.6832, 0.4053])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
@ -62,13 +62,13 @@ Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.573626518249512 seconds
Time: 10.745137691497803 seconds
[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61]
[85.01]
16.977728843688965
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01}
[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61, 18.14, 17.76, 17.8, 22.06, 18.45, 17.61, 17.74, 18.01, 17.71, 17.56]
324.21500000000003
16.21075
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01, 'J_1KI': 764.8525325924743, 'W_1KI': 45.050344462109166, 'W_D': 68.79925, 'J_D': 1168.055011149168, 'W_D_1KI': 36.459591944886064, 'J_D_1KI': 19.321458370368873}
[18.8, 18.79, 18.58, 18.75, 18.78, 18.86, 18.86, 19.02, 18.49, 18.88]
[85.32]
16.614368677139282
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1799, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.745137691497803, 'TIME_S_1KI': 5.972839183711953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5379355335235, 'W': 85.32}
[18.8, 18.79, 18.58, 18.75, 18.78, 18.86, 18.86, 19.02, 18.49, 18.88, 18.92, 18.66, 18.86, 18.59, 18.74, 18.39, 18.7, 18.44, 18.58, 18.65]
336.715
16.835749999999997
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1799, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.745137691497803, 'TIME_S_1KI': 5.972839183711953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5379355335235, 'W': 85.32, 'J_1KI': 787.9588302020697, 'W_1KI': 47.426347971095055, 'W_D': 68.48425, 'J_D': 1137.822578077376, 'W_D_1KI': 38.06795441912174, 'J_D_1KI': 21.16061946588201}

1
pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1345, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325, "TIME_S_1KI": 7.663848231716227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 549.730076084137, "W": 38.131256134653135, "J_1KI": 408.7212461592096, "W_1KI": 28.350376308292294, "W_D": 16.28825613465314, "J_D": 234.82426732969293, "W_D_1KI": 12.110227609407538, "J_D_1KI": 9.003886698444267}

71
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@ -0,0 +1,71 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 7.806152820587158}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.5486, 0.8485, 0.8195, ..., 0.3778, 0.3275, 0.7623])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 7.806152820587158 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1345 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6343, 0.9450, 0.3421, ..., 0.5967, 0.9759, 0.2168])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.307875871658325 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6343, 0.9450, 0.3421, ..., 0.5967, 0.9759, 0.2168])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.307875871658325 seconds
[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28]
[24.24, 24.0, 24.0, 24.8, 25.52, 28.6, 36.84, 43.04, 50.0, 55.48, 57.44, 57.44, 57.68, 57.56]
14.416783809661865
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135}
[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28, 24.12, 24.24, 24.0, 23.96, 23.96, 24.08, 24.08, 24.08, 24.08, 24.08]
436.85999999999996
21.842999999999996
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135, 'J_1KI': 408.7212461592096, 'W_1KI': 28.350376308292294, 'W_D': 16.28825613465314, 'J_D': 234.82426732969293, 'W_D_1KI': 12.110227609407538, 'J_D_1KI': 9.003886698444267}

0
pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json → pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.json
View File

0
pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output → pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.output
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1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1801, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053, "TIME_S_1KI": 5.764393856762384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8333181858063, "W": 39.805332297033694, "J_1KI": 323.6165009360391, "W_1KI": 22.10179472350566, "W_D": 18.366332297033694, "J_D": 268.92151824545857, "W_D_1KI": 10.197852469202495, "J_D_1KI": 5.66232785630344}

74
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@ -0,0 +1,74 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 5.829137325286865}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8248, 0.9604, 0.9464, ..., 0.7437, 0.8759, 0.5369])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 5.829137325286865 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1801 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8790, 0.0885, 0.6163, ..., 0.1605, 0.4532, 0.8862])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.381673336029053 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.8790, 0.0885, 0.6163, ..., 0.1605, 0.4532, 0.8862])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.381673336029053 seconds
[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72]
[23.88, 24.0, 24.28, 26.28, 27.2, 34.12, 40.64, 47.44, 53.0, 57.04, 57.04, 56.96, 57.52, 57.24]
14.642091512680054
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694}
[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72, 23.68, 24.04, 24.0, 24.12, 24.0, 23.92, 24.08, 24.04, 23.76, 23.88]
428.78000000000003
21.439
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694, 'J_1KI': 323.6165009360391, 'W_1KI': 22.10179472350566, 'W_D': 18.366332297033694, 'J_D': 268.92151824545857, 'W_D_1KI': 10.197852469202495, 'J_D_1KI': 5.66232785630344}

1
pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 2142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005, "TIME_S_1KI": 4.896873296921104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 527.8806602096558, "W": 35.901030267154354, "J_1KI": 246.44288525194017, "W_1KI": 16.760518332004835, "W_D": 14.631030267154355, "J_D": 215.1313725399972, "W_D_1KI": 6.830546343209316, "J_D_1KI": 3.1888638390332944}

71
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 4.9012720584869385}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0783, 0.8612, 0.3161, ..., 0.8531, 0.6998, 0.6080])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 4.9012720584869385 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 2142 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5677, 0.4069, 0.3735, ..., 0.4488, 0.2885, 0.1400])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.489102602005005 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5677, 0.4069, 0.3735, ..., 0.4488, 0.2885, 0.1400])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.489102602005005 seconds
[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24]
[23.28, 23.4, 23.56, 24.68, 26.6, 30.12, 36.56, 41.44, 45.96, 49.92, 50.84, 50.96, 50.76, 51.04]
14.703774690628052
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354}
[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24, 23.6, 23.64, 23.64, 23.68, 23.68, 23.68, 23.76, 23.64, 23.64, 23.52]
425.4
21.27
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354, 'J_1KI': 246.44288525194017, 'W_1KI': 16.760518332004835, 'W_D': 14.631030267154355, 'J_D': 215.1313725399972, 'W_D_1KI': 6.830546343209316, 'J_D_1KI': 3.1888638390332944}

1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262, "TIME_S_1KI": 12.712969779968262, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 642.9576612091064, "W": 41.13704135154824, "J_1KI": 642.9576612091064, "W_1KI": 41.13704135154824, "W_D": 19.36904135154824, "J_D": 302.73138558578484, "W_D_1KI": 19.36904135154824, "J_D_1KI": 19.36904135154824}

51
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@ -0,0 +1,51 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7918, 0.6380, 0.4821, ..., 0.8085, 0.1927, 0.4528])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 12.712969779968262 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7918, 0.6380, 0.4821, ..., 0.8085, 0.1927, 0.4528])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 12.712969779968262 seconds
[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24]
[23.96, 23.92, 24.76, 26.04, 30.04, 34.84, 41.8, 41.8, 48.8, 54.68, 57.92, 59.36, 59.6, 60.0, 59.68]
15.629652500152588
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824}
[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24, 24.24, 24.16, 24.08, 24.12, 24.12, 24.0, 23.92, 24.0, 24.0, 24.08]
435.36
21.768
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824, 'J_1KI': 642.9576612091064, 'W_1KI': 41.13704135154824, 'W_D': 19.36904135154824, 'J_D': 302.73138558578484, 'W_D_1KI': 19.36904135154824, 'J_D_1KI': 19.36904135154824}

1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1694, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093, "TIME_S_1KI": 6.164034958291081, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 563.8270891189575, "W": 38.54107447042048, "J_1KI": 332.8377149462559, "W_1KI": 22.751519758217523, "W_D": 16.675074470420483, "J_D": 243.94386582851405, "W_D_1KI": 9.843609486670887, "J_D_1KI": 5.81086746556723}

93
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@ -0,0 +1,93 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 7.140163421630859}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.1948, 0.1869, 0.5638, ..., 0.6155, 0.6170, 0.7726])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 7.140163421630859 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1470 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 9.107451438903809}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.1872, 0.8693, 0.3135, ..., 0.4431, 0.3648, 0.2379])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 9.107451438903809 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1694 -m matrices/389000+_cols/mario002.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.6416, 0.1879, 0.5321, ..., 0.0693, 0.5314, 0.2281])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.441875219345093 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.6416, 0.1879, 0.5321, ..., 0.0693, 0.5314, 0.2281])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.441875219345093 seconds
[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2]
[24.04, 23.88, 24.16, 26.0, 26.68, 32.8, 39.08, 45.28, 50.52, 54.72, 54.84, 55.48, 55.4, 55.76]
14.629251956939697
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048}
[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2, 24.0, 23.96, 24.08, 24.08, 24.44, 24.72, 24.6, 24.56, 24.48, 24.32]
437.32
21.866
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048, 'J_1KI': 332.8377149462559, 'W_1KI': 22.751519758217523, 'W_D': 16.675074470420483, 'J_D': 243.94386582851405, 'W_D_1KI': 9.843609486670887, 'J_D_1KI': 5.81086746556723}

1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.460163831710815, "TIME_S_1KI": 37.89914431779281, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 556.1444871425629, "W": 33.16277212022306, "J_1KI": 2015.016257762909, "W_1KI": 120.15497145008355, "W_D": 17.98077212022306, "J_D": 301.54015029191976, "W_D_1KI": 65.14772507327196, "J_D_1KI": 236.04248214953608}

74
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['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/389000+_cols/msdoor.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 3.7967092990875244}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.9111, 0.9233, 0.5316, ..., 0.5920, 0.1518, 0.8793])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 3.7967092990875244 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 276 -m matrices/389000+_cols/msdoor.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.460163831710815}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.6477, 0.4686, 0.6140, ..., 0.5698, 0.0750, 0.2417])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.460163831710815 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.6477, 0.4686, 0.6140, ..., 0.5698, 0.0750, 0.2417])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.460163831710815 seconds
[16.64, 16.68, 16.6, 16.4, 16.68, 16.76, 16.96, 17.0, 17.04, 17.04]
[16.68, 16.64, 16.8, 20.28, 21.28, 25.56, 26.76, 29.96, 35.88, 41.68, 46.2, 50.76, 50.72, 50.92, 50.4, 50.36]
16.77014470100403
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.460163831710815, 'TIME_S_1KI': 37.89914431779281, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 556.1444871425629, 'W': 33.16277212022306}
[16.64, 16.68, 16.6, 16.4, 16.68, 16.76, 16.96, 17.0, 17.04, 17.04, 16.92, 17.2, 17.16, 17.16, 17.04, 16.8, 17.0, 16.76, 16.76, 16.68]
303.64
15.181999999999999
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.460163831710815, 'TIME_S_1KI': 37.89914431779281, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 556.1444871425629, 'W': 33.16277212022306, 'J_1KI': 2015.016257762909, 'W_1KI': 120.15497145008355, 'W_D': 17.98077212022306, 'J_D': 301.54015029191976, 'W_D_1KI': 65.14772507327196, 'J_D_1KI': 236.04248214953608}

1
pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444, "TIME_S_1KI": 33.385778188705444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1758.8809212875371, "W": 47.990539203077404, "J_1KI": 1758.8809212875371, "W_1KI": 47.990539203077404, "W_D": 26.191539203077404, "J_D": 959.9350073668963, "W_D_1KI": 26.191539203077404, "J_D_1KI": 26.191539203077404}

51
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@ -0,0 +1,51 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3716, 0.7020, 0.0579, ..., 0.5562, 0.4218, 0.2724])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 33.385778188705444 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3716, 0.7020, 0.0579, ..., 0.5562, 0.4218, 0.2724])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 33.385778188705444 seconds
[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08]
[24.24, 24.16, 24.36, 28.92, 31.16, 35.28, 37.04, 40.44, 46.16, 49.16, 54.2, 56.72, 56.48, 56.72, 56.72, 56.48, 57.24, 56.52, 56.44, 56.56, 56.52, 57.2, 56.84, 56.8, 56.92, 56.92, 58.08, 57.8, 57.88, 57.44, 57.4, 56.88, 56.44, 57.16, 57.12]
36.65057635307312
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404}
[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08, 24.24, 24.24, 24.24, 24.28, 24.48, 24.36, 24.4, 24.16, 24.16, 24.32]
435.98
21.799
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404, 'J_1KI': 1758.8809212875371, 'W_1KI': 47.990539203077404, 'W_D': 26.191539203077404, 'J_D': 959.9350073668963, 'W_D_1KI': 26.191539203077404, 'J_D_1KI': 26.191539203077404}

1
pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20402, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435, "TIME_S_1KI": 0.5215651123515065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.5667917442322, "W": 146.96, "J_1KI": 97.3711788914926, "W_1KI": 7.203215371042055, "W_D": 111.28150000000001, "J_D": 1504.2741728054286, "W_D_1KI": 5.454440741103813, "J_D_1KI": 0.2673483355114113}

93
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@ -0,0 +1,93 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.5630712509155273}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.4842, 0.5105, 0.4860, ..., 0.7675, 0.4934, 0.1706])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 0.5630712509155273 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18647', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.59645700454712}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6749, 0.4854, 0.2428, ..., 0.8655, 0.6324, 0.8376])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 9.59645700454712 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20402', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0724, 0.4329, 0.4595, ..., 0.8349, 0.8167, 0.6766])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.640971422195435 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0724, 0.4329, 0.4595, ..., 0.8349, 0.8167, 0.6766])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.640971422195435 seconds
[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95]
[146.96]
13.517738103866577
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96}
[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95, 39.85, 39.43, 39.2, 39.39, 39.57, 39.13, 39.11, 38.95, 44.59, 39.27]
713.5699999999999
35.6785
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96, 'J_1KI': 97.3711788914926, 'W_1KI': 7.203215371042055, 'W_D': 111.28150000000001, 'J_D': 1504.2741728054286, 'W_D_1KI': 5.454440741103813, 'J_D_1KI': 0.2673483355114113}

0
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json → pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.json
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0
pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output → pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.output
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1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30984, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501, "TIME_S_1KI": 0.33298414897894074, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.101525504589, "W": 149.37, "J_1KI": 61.84164489751449, "W_1KI": 4.820875290472502, "W_D": 113.45200000000001, "J_D": 1455.3494695825577, "W_D_1KI": 3.661631809966435, "J_D_1KI": 0.11817815033457382}

97
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.39348506927490234}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.1720, 0.8662, 0.8556, ..., 0.0402, 0.8663, 0.3929])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 0.39348506927490234 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26684', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 9.042525291442871}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.7375, 0.5933, 0.9050, ..., 0.8578, 0.6740, 0.2052])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 9.042525291442871 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30984', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.0633, 0.8834, 0.2857, ..., 0.1984, 0.6858, 0.2922])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.317180871963501 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.0633, 0.8834, 0.2857, ..., 0.1984, 0.6858, 0.2922])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.317180871963501 seconds
[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22]
[149.37]
12.827887296676636
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37}
[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22, 40.56, 39.23, 39.36, 44.65, 39.73, 38.95, 39.17, 38.94, 39.11, 39.04]
718.3599999999999
35.91799999999999
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37, 'J_1KI': 61.84164489751449, 'W_1KI': 4.820875290472502, 'W_D': 113.45200000000001, 'J_D': 1455.3494695825577, 'W_D_1KI': 3.661631809966435, 'J_D_1KI': 0.11817815033457382}

1
pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30366, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355, "TIME_S_1KI": 0.33961335307625473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1775.0306821584702, "W": 137.59, "J_1KI": 58.454543968862225, "W_1KI": 4.531054468813805, "W_D": 102.3395, "J_D": 1320.2685696399212, "W_D_1KI": 3.370200223934664, "J_D_1KI": 0.11098597852646591}

93
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@ -0,0 +1,93 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3785414695739746}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.2846, 0.0684, 0.3415, ..., 0.3157, 0.0663, 0.9624])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 0.3785414695739746 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '27738', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.591154098510742}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.1194, 0.5235, 0.5697, ..., 0.7322, 0.5132, 0.4627])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 9.591154098510742 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30366', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.4535, 0.4285, 0.5680, ..., 0.4151, 0.8586, 0.5793])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.31269907951355 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.4535, 0.4285, 0.5680, ..., 0.4151, 0.8586, 0.5793])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.31269907951355 seconds
[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57]
[137.59]
12.900869846343994
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59}
[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57, 39.44, 38.8, 39.85, 39.35, 38.9, 38.8, 38.74, 39.19, 39.29, 38.76]
705.01
35.2505
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59, 'J_1KI': 58.454543968862225, 'W_1KI': 4.531054468813805, 'W_D': 102.3395, 'J_D': 1320.2685696399212, 'W_D_1KI': 3.370200223934664, 'J_D_1KI': 0.11098597852646591}

1
pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19495, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956, "TIME_S_1KI": 0.5495852401666869, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2110.003728497028, "W": 158.07, "J_1KI": 108.23307147971418, "W_1KI": 8.108232880225698, "W_D": 122.3625, "J_D": 1633.3607340306044, "W_D_1KI": 6.276609387022313, "J_D_1KI": 0.32195995829814383}

97
pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.output Normal file
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@ -0,0 +1,97 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.6046795845031738}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.7423, 0.4233, 0.1707, ..., 0.4030, 0.8937, 0.1151])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 0.6046795845031738 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17364', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 9.352032661437988}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.3036, 0.7996, 0.4739, ..., 0.0238, 0.6033, 0.9918])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 9.352032661437988 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19495', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1117, 0.6424, 0.8924, ..., 0.5333, 0.0312, 0.4242])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.71416425704956 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1117, 0.6424, 0.8924, ..., 0.5333, 0.0312, 0.4242])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.71416425704956 seconds
[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37]
[158.07]
13.34854006767273
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07}
[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37, 41.26, 39.31, 39.83, 39.61, 39.66, 39.59, 39.58, 39.11, 39.31, 39.11]
714.15
35.707499999999996
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07, 'J_1KI': 108.23307147971418, 'W_1KI': 8.108232880225698, 'W_D': 122.3625, 'J_D': 1633.3607340306044, 'W_D_1KI': 6.276609387022313, 'J_D_1KI': 0.32195995829814383}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 23986, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974, "TIME_S_1KI": 0.4254478952221285, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1608.246218369007, "W": 138.89, "J_1KI": 67.04937123192725, "W_1KI": 5.79046110230968, "W_D": 103.45274999999998, "J_D": 1197.9083732981082, "W_D_1KI": 4.313047194196614, "J_D_1KI": 0.1798151919534985}

71
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@ -0,0 +1,71 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.43773889541625977}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9846, 0.2787, 0.2893, ..., 0.3452, 0.1271, 0.5089])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 0.43773889541625977 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23986', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.2099, 0.5726, 0.9552, ..., 0.7541, 0.8652, 0.1203])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.204793214797974 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.2099, 0.5726, 0.9552, ..., 0.7541, 0.8652, 0.1203])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.204793214797974 seconds
[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21]
[138.89]
11.579280138015747
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89}
[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21, 41.54, 39.4, 39.48, 39.09, 39.42, 38.94, 39.43, 39.44, 39.0, 41.44]
708.7450000000001
35.437250000000006
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89, 'J_1KI': 67.04937123192725, 'W_1KI': 5.79046110230968, 'W_D': 103.45274999999998, 'J_D': 1197.9083732981082, 'W_D_1KI': 4.313047194196614, 'J_D_1KI': 0.1798151919534985}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2078, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.368424892425537, "TIME_S_1KI": 4.989617368828458, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1768.3314125061036, "W": 128.56, "J_1KI": 850.9775806092895, "W_1KI": 61.86717998075073, "W_D": 92.98825, "J_D": 1279.0451421046257, "W_D_1KI": 44.74891722810394, "J_D_1KI": 21.534608868192468}

74
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@ -0,0 +1,74 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 0.505284309387207}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.0979, 0.4410, 0.6794, ..., 0.5232, 0.5066, 0.8230])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 0.505284309387207 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2078', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.368424892425537}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.1753, 0.8799, 0.4934, ..., 0.3593, 0.7648, 0.6193])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.368424892425537 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.1753, 0.8799, 0.4934, ..., 0.3593, 0.7648, 0.6193])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.368424892425537 seconds
[41.45, 39.25, 39.16, 39.53, 39.32, 39.64, 39.36, 39.48, 39.45, 39.21]
[128.56]
13.754911422729492
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2078, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.368424892425537, 'TIME_S_1KI': 4.989617368828458, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1768.3314125061036, 'W': 128.56}
[41.45, 39.25, 39.16, 39.53, 39.32, 39.64, 39.36, 39.48, 39.45, 39.21, 39.72, 39.17, 39.05, 38.92, 39.31, 39.68, 39.47, 38.94, 39.39, 44.25]
711.4350000000001
35.57175
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2078, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.368424892425537, 'TIME_S_1KI': 4.989617368828458, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1768.3314125061036, 'W': 128.56, 'J_1KI': 850.9775806092895, 'W_1KI': 61.86717998075073, 'W_D': 92.98825, 'J_D': 1279.0451421046257, 'W_D_1KI': 44.74891722810394, 'J_D_1KI': 21.534608868192468}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2652, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064, "TIME_S_1KI": 4.079358610092784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1727.0440453481674, "W": 123.26, "J_1KI": 651.2232448522501, "W_1KI": 46.47812971342383, "W_D": 87.892, "J_D": 1231.4891711320877, "W_D_1KI": 33.14177978883861, "J_D_1KI": 12.49690037286524}

74
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@ -0,0 +1,74 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.9585680961608887}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.7261, 0.8238, 0.5826, ..., 0.6988, 0.4899, 0.5621])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 3.9585680961608887 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2652', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.8039, 0.1348, 0.9933, ..., 0.2390, 0.5536, 0.8375])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.818459033966064 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.8039, 0.1348, 0.9933, ..., 0.2390, 0.5536, 0.8375])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.818459033966064 seconds
[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93]
[123.26]
14.011390924453735
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26}
[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93, 39.84, 39.42, 39.65, 39.42, 38.97, 39.03, 39.01, 38.92, 39.07, 38.77]
707.36
35.368
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26, 'J_1KI': 651.2232448522501, 'W_1KI': 46.47812971342383, 'W_D': 87.892, 'J_D': 1231.4891711320877, 'W_D_1KI': 33.14177978883861, 'J_D_1KI': 12.49690037286524}

1
pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125, "TIME_S_1KI": 1.3436493423661153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.0154501628876, "W": 88.97, "J_1KI": 160.2630512642138, "W_1KI": 10.959595959595958, "W_D": 72.9665, "J_D": 1066.9949853243827, "W_D_1KI": 8.988236018723823, "J_D_1KI": 1.1071983270169774}

71
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@ -0,0 +1,71 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.293351411819458}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.2979, 0.5597, 0.8769, ..., 0.0942, 0.8424, 0.4292])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 1.293351411819458 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '8118', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6448, 0.4532, 0.0841, ..., 0.5791, 0.6160, 0.9399])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.907745361328125 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.6448, 0.4532, 0.0841, ..., 0.5791, 0.6160, 0.9399])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.907745361328125 seconds
[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98]
[88.97]
14.623080253601074
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97}
[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98, 18.52, 17.6, 17.66, 17.43, 18.0, 17.58, 17.75, 17.82, 17.89, 17.62]
320.06999999999994
16.003499999999995
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97, 'J_1KI': 160.2630512642138, 'W_1KI': 10.959595959595958, 'W_D': 72.9665, 'J_D': 1066.9949853243827, 'W_D_1KI': 8.988236018723823, 'J_D_1KI': 1.1071983270169774}

0
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json → pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.json
View File

0
pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output → pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.output
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1
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{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12165, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207, "TIME_S_1KI": 0.8646259864465327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.3463970065116, "W": 89.63, "J_1KI": 105.98819539716494, "W_1KI": 7.3678586107685975, "W_D": 73.37299999999999, "J_D": 1055.4860335552692, "W_D_1KI": 6.031483764899301, "J_D_1KI": 0.49580631030820393}

74
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.8631081581115723}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.5077, 0.7328, 0.3933, ..., 0.4074, 0.1030, 0.0500])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 0.8631081581115723 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12165', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.9100, 0.1506, 0.3829, ..., 0.6719, 0.7400, 0.8631])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.51817512512207 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.9100, 0.1506, 0.3829, ..., 0.6719, 0.7400, 0.8631])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.51817512512207 seconds
[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97]
[89.63]
14.385210275650024
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63}
[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97, 18.65, 17.64, 17.75, 18.23, 17.75, 18.05, 17.76, 17.53, 18.1, 17.73]
325.14
16.256999999999998
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63, 'J_1KI': 105.98819539716494, 'W_1KI': 7.3678586107685975, 'W_D': 73.37299999999999, 'J_D': 1055.4860335552692, 'W_D_1KI': 6.031483764899301, 'J_D_1KI': 0.49580631030820393}

1
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{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13328, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792, "TIME_S_1KI": 0.8063470007849484, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1285.765242910385, "W": 89.74, "J_1KI": 96.47098161092326, "W_1KI": 6.733193277310924, "W_D": 73.49574999999999, "J_D": 1053.0229646939038, "W_D_1KI": 5.51438700480192, "J_D_1KI": 0.4137445231694118}

71
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.7877652645111084}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0377, 0.4846, 0.1087, ..., 0.8181, 0.0416, 0.1571])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 0.7877652645111084 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13328', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5687, 0.0271, 0.0300, ..., 0.3524, 0.7739, 0.4785])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.746992826461792 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.5687, 0.0271, 0.0300, ..., 0.3524, 0.7739, 0.4785])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.746992826461792 seconds
[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87]
[89.74]
14.327671527862549
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74}
[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87, 19.34, 17.9, 18.16, 17.6, 17.86, 17.8, 17.9, 17.65, 17.71, 17.86]
324.885
16.24425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74, 'J_1KI': 96.47098161092326, 'W_1KI': 6.733193277310924, 'W_D': 73.49574999999999, 'J_D': 1053.0229646939038, 'W_D_1KI': 5.51438700480192, 'J_D_1KI': 0.4137445231694118}

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5897, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956, "TIME_S_1KI": 1.790718565719783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.1635441350936, "W": 89.05999999999999, "J_1KI": 220.30923251400603, "W_1KI": 15.102594539596403, "W_D": 72.89299999999999, "J_D": 1063.327287476301, "W_D_1KI": 12.361031032728503, "J_D_1KI": 2.096155847503562}

74
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7802655696868896}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5518, 0.1807, 0.4021, ..., 0.3397, 0.2107, 0.4589])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 1.7802655696868896 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5897', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5366, 0.8162, 0.5634, ..., 0.9410, 0.0469, 0.5938])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.55986738204956 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
6226531, 6226538]),
col_indices=tensor([ 0, 1, 10383, ..., 400315, 400318,
400319]),
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.5366, 0.8162, 0.5634, ..., 0.9410, 0.0469, 0.5938])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
Shape: torch.Size([400320, 400320])
Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 10.55986738204956 seconds
[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71]
[89.06]
14.587508916854858
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999}
[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71, 18.09, 17.83, 17.84, 17.57, 17.77, 17.94, 21.22, 17.67, 17.65, 17.92]
323.34000000000003
16.167
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999, 'J_1KI': 220.30923251400603, 'W_1KI': 15.102594539596403, 'W_D': 72.89299999999999, 'J_D': 1063.327287476301, 'W_D_1KI': 12.361031032728503, 'J_D_1KI': 2.096155847503562}

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13697, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639, "TIME_S_1KI": 0.7868405283824662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1316.45538356781, "W": 89.82, "J_1KI": 96.11268040941886, "W_1KI": 6.557640359202745, "W_D": 73.69574999999999, "J_D": 1080.128777928829, "W_D_1KI": 5.3804300211725185, "J_D_1KI": 0.3928181369038854}

71
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@ -0,0 +1,71 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7665784358978271}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.5208, 0.6283, 0.9927, ..., 0.7747, 0.7207, 0.3302])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 0.7665784358978271 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13697', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9293, 0.9172, 0.4372, ..., 0.0528, 0.4444, 0.3291])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.777354717254639 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.9293, 0.9172, 0.4372, ..., 0.0528, 0.4444, 0.3291])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.777354717254639 seconds
[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56]
[89.82]
14.656595230102539
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82}
[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56, 18.11, 17.79, 18.09, 17.72, 17.66, 18.49, 17.86, 18.34, 17.71, 17.93]
322.485
16.12425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82, 'J_1KI': 96.11268040941886, 'W_1KI': 6.557640359202745, 'W_D': 73.69574999999999, 'J_D': 1080.128777928829, 'W_D_1KI': 5.3804300211725185, 'J_D_1KI': 0.3928181369038854}

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1333, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.536967515945435, "TIME_S_1KI": 7.9047018124121795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1980.6726791381834, "W": 66.6, "J_1KI": 1485.8759783482246, "W_1KI": 49.962490622655665, "W_D": 49.48649999999999, "J_D": 1471.7200981407163, "W_D_1KI": 37.12415603900975, "J_D_1KI": 27.8500795491446}

97
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@ -0,0 +1,97 @@
['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/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 1.0501856803894043}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5509, 0.1350, 0.4677, ..., 0.8467, 0.1931, 0.8337])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 1.0501856803894043 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', '999', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 7.86853814125061}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.0083, 0.1850, 0.6844, ..., 0.3666, 0.9050, 0.1703])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 7.86853814125061 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', '1333', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.536967515945435}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5281, 0.2288, 0.8014, ..., 0.5844, 0.6890, 0.8626])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.536967515945435 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 32, 64, ..., 20240851,
20240893, 20240935]),
col_indices=tensor([ 0, 1, 2, ..., 415860, 415861,
415862]),
values=tensor([1732682.0000, 32540.3633, 24619.0176, ...,
-97620.4609, -360329.0312, 2075205.5000]),
size=(415863, 415863), nnz=20240935, layout=torch.sparse_csr)
tensor([0.5281, 0.2288, 0.8014, ..., 0.5844, 0.6890, 0.8626])
Matrix Type: SuiteSparse
Matrix: msdoor
Matrix Format: csr
Shape: torch.Size([415863, 415863])
Rows: 415863
Size: 172942034769
NNZ: 20240935
Density: 0.00011703883921012015
Time: 10.536967515945435 seconds
[19.73, 18.62, 18.78, 18.65, 18.85, 18.62, 18.87, 18.75, 18.73, 18.48]
[66.6]
29.739830017089844
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1333, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.536967515945435, 'TIME_S_1KI': 7.9047018124121795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.6726791381834, 'W': 66.6}
[19.73, 18.62, 18.78, 18.65, 18.85, 18.62, 18.87, 18.75, 18.73, 18.48, 20.53, 18.58, 18.75, 19.13, 18.65, 18.58, 22.49, 18.38, 18.94, 19.06]
342.27
17.1135
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1333, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.536967515945435, 'TIME_S_1KI': 7.9047018124121795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.6726791381834, 'W': 66.6, 'J_1KI': 1485.8759783482246, 'W_1KI': 49.962490622655665, 'W_D': 49.48649999999999, 'J_D': 1471.7200981407163, 'W_D_1KI': 37.12415603900975, 'J_D_1KI': 27.8500795491446}

1
pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1887, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512, "TIME_S_1KI": 5.603405680047436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.276729001999, "W": 85.01, "J_1KI": 764.8525325924743, "W_1KI": 45.050344462109166, "W_D": 68.79925, "J_D": 1168.055011149168, "W_D_1KI": 36.459591944886064, "J_D_1KI": 19.321458370368873}

74
pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.output Normal file
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@ -0,0 +1,74 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 5.56341028213501}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.2549, 0.6086, 0.7138, ..., 0.3139, 0.6424, 0.7605])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 5.56341028213501 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1887', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3061, 0.1365, 0.6683, ..., 0.9071, 0.4159, 0.8227])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.573626518249512 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 24, 48, ..., 12968181,
12968191, 12968200]),
col_indices=tensor([ 0, 1, 8, ..., 392905, 392906,
392907]),
values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ...,
0.0000e+00, 0.0000e+00, 2.1156e-17]),
size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
tensor([0.3061, 0.1365, 0.6683, ..., 0.9071, 0.4159, 0.8227])
Matrix Type: SuiteSparse
Matrix: test1
Matrix Format: csr
Shape: torch.Size([392908, 392908])
Rows: 392908
Size: 154376696464
NNZ: 12968200
Density: 8.400361127706946e-05
Time: 10.573626518249512 seconds
[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61]
[85.01]
16.977728843688965
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01}
[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61, 18.14, 17.76, 17.8, 22.06, 18.45, 17.61, 17.74, 18.01, 17.71, 17.56]
324.21500000000003
16.21075
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01, 'J_1KI': 764.8525325924743, 'W_1KI': 45.050344462109166, 'W_D': 68.79925, 'J_D': 1168.055011149168, 'W_D_1KI': 36.459591944886064, 'J_D_1KI': 19.321458370368873}

2
pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 88.16403460502625, "TIME_S_1KI": 88.16403460502625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2373.6426183891294, "W": 25.696559171496023, "J_1KI": 2373.6426183891294, "W_1KI": 25.696559171496023, "W_D": 4.897559171496024, "J_D": 452.3973461956977, "W_D_1KI": 4.897559171496024, "J_D_1KI": 4.897559171496024}
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 120, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.429105520248413, "TIME_S_1KI": 86.90921266873677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.42850420951834, "W": 24.281319647505853, "J_1KI": 2961.904201745986, "W_1KI": 202.3443303958821, "W_D": 5.817319647505855, "J_D": 85.15357694053642, "W_D_1KI": 48.47766372921546, "J_D_1KI": 403.9805310767955}

50
pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 88.16403460502625}
['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/389000+_cols/amazon0312.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 8.718183279037476}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0536, 0.3063, 0.9163, ..., 0.7704, 0.9774, 0.8233])
tensor([0.1092, 0.3043, 0.4990, ..., 0.2314, 0.3307, 0.6358])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -18,7 +18,10 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 88.16403460502625 seconds
Time: 8.718183279037476 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 120 -m matrices/389000+_cols/amazon0312.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.429105520248413}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -28,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.0536, 0.3063, 0.9163, ..., 0.7704, 0.9774, 0.8233])
tensor([0.3453, 0.3987, 0.3782, ..., 0.4624, 0.9597, 0.8792])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
@ -37,13 +40,32 @@ Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 88.16403460502625 seconds
Time: 10.429105520248413 seconds
[22.96, 23.04, 23.08, 23.12, 23.16, 23.24, 23.24, 22.92, 22.8, 22.84]
[22.88, 23.08, 23.44, 27.52, 29.2, 30.24, 30.68, 28.56, 27.76, 26.76, 26.68, 26.84, 26.68, 26.6, 26.56, 26.56, 26.52, 26.72, 26.88, 26.8, 26.92, 26.88, 26.8, 27.0, 27.0, 26.96, 27.08, 26.92, 27.04, 27.08, 27.0, 26.88, 26.72, 26.84, 26.96, 27.04, 27.16, 27.16, 27.28, 27.44, 27.32, 27.44, 27.6, 27.56, 27.44, 27.44, 27.36, 27.72, 27.84, 28.0, 27.96, 27.92, 27.6, 27.56, 27.68, 27.4, 27.4, 27.12, 27.12, 27.12, 27.28, 27.32, 27.36, 27.24, 27.2, 27.16, 27.24, 27.2, 27.24, 27.32, 27.24, 27.28, 27.2, 26.92, 26.92, 27.2, 27.24, 27.32, 27.24, 27.24, 26.96, 26.96, 27.04, 26.84, 26.88, 26.96, 27.4, 27.48]
92.37200212478638
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 88.16403460502625, 'TIME_S_1KI': 88.16403460502625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2373.6426183891294, 'W': 25.696559171496023}
[22.96, 23.04, 23.08, 23.12, 23.16, 23.24, 23.24, 22.92, 22.8, 22.84, 23.16, 22.88, 22.76, 23.04, 23.12, 23.44, 23.56, 23.36, 23.24, 23.0]
415.98
20.799
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 88.16403460502625, 'TIME_S_1KI': 88.16403460502625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2373.6426183891294, 'W': 25.696559171496023, 'J_1KI': 2373.6426183891294, 'W_1KI': 25.696559171496023, 'W_D': 4.897559171496024, 'J_D': 452.3973461956977, 'W_D_1KI': 4.897559171496024, 'J_D_1KI': 4.897559171496024}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 3200428,
3200438, 3200440]),
col_indices=tensor([ 1, 2, 3, ..., 400724, 6009,
400707]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727),
nnz=3200440, layout=torch.sparse_csr)
tensor([0.3453, 0.3987, 0.3782, ..., 0.4624, 0.9597, 0.8792])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: csr
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.429105520248413 seconds
[20.36, 20.52, 20.56, 20.48, 20.56, 20.52, 20.56, 20.48, 20.56, 20.84]
[20.76, 20.88, 24.4, 26.6, 26.6, 28.72, 29.88, 30.8, 27.04, 25.92, 25.2, 25.28, 25.28, 25.0]
14.637940168380737
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 120, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.429105520248413, 'TIME_S_1KI': 86.90921266873677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.42850420951834, 'W': 24.281319647505853}
[20.36, 20.52, 20.56, 20.48, 20.56, 20.52, 20.56, 20.48, 20.56, 20.84, 20.68, 20.64, 20.32, 20.68, 20.64, 20.44, 20.52, 20.44, 20.44, 19.96]
369.28
18.464
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 120, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.429105520248413, 'TIME_S_1KI': 86.90921266873677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.42850420951834, 'W': 24.281319647505853, 'J_1KI': 2961.904201745986, 'W_1KI': 202.3443303958821, 'W_D': 5.817319647505855, 'J_D': 85.15357694053642, 'W_D_1KI': 48.47766372921546, 'J_D_1KI': 403.9805310767955}

2
pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 62.08789658546448, "TIME_S_1KI": 62.08789658546448, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1791.450892791748, "W": 25.109871656394702, "J_1KI": 1791.450892791748, "W_1KI": 25.109871656394702, "W_D": 4.4798716563947, "J_D": 319.61414173126184, "W_D_1KI": 4.4798716563947, "J_D_1KI": 4.4798716563947}
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 169, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.493204355239868, "TIME_S_1KI": 62.08996659905248, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.01729221343993, "W": 23.209796141384285, "J_1KI": 2017.8538000795263, "W_1KI": 137.33607184251056, "W_D": 4.4517961413842855, "J_D": 65.40942696666718, "W_D_1KI": 26.341989002273877, "J_D_1KI": 155.8697574099046}

51
pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 62.08789658546448}
['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/389000+_cols/helm2d03.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 6.207566738128662}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.2732, 0.1117, 0.4132, ..., 0.8859, 0.7833, 0.1406])
tensor([0.8666, 0.0286, 0.3503, ..., 0.4652, 0.6397, 0.7369])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -19,7 +19,10 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 62.08789658546448 seconds
Time: 6.207566738128662 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 169 -m matrices/389000+_cols/helm2d03.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.493204355239868}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -30,7 +33,7 @@ tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.2732, 0.1117, 0.4132, ..., 0.8859, 0.7833, 0.1406])
tensor([0.9401, 0.6405, 0.7589, ..., 0.2451, 0.0732, 0.7376])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
@ -39,13 +42,33 @@ Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 62.08789658546448 seconds
Time: 10.493204355239868 seconds
[23.16, 22.92, 22.92, 22.96, 22.84, 23.04, 23.12, 23.2, 23.36, 23.44]
[23.36, 23.32, 23.32, 24.2, 25.04, 27.48, 28.36, 28.84, 28.4, 27.24, 26.92, 26.76, 26.72, 26.8, 26.68, 26.6, 26.6, 26.56, 26.52, 26.56, 26.48, 26.48, 26.44, 26.6, 26.6, 26.56, 26.4, 26.48, 26.56, 26.36, 26.28, 26.2, 26.44, 26.6, 26.68, 27.2, 27.2, 27.04, 27.04, 26.84, 26.64, 26.64, 26.44, 26.4, 26.48, 26.48, 26.16, 26.52, 26.72, 26.8, 26.84, 26.84, 26.72, 26.64, 26.6, 26.8, 26.72, 26.8, 26.96, 27.0, 27.2, 27.32, 27.16, 26.88, 26.72, 26.68, 26.68, 26.68]
71.34448623657227
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 62.08789658546448, 'TIME_S_1KI': 62.08789658546448, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.450892791748, 'W': 25.109871656394702}
[23.16, 22.92, 22.92, 22.96, 22.84, 23.04, 23.12, 23.2, 23.36, 23.44, 23.08, 23.08, 23.04, 22.76, 22.48, 22.48, 22.48, 22.64, 22.96, 22.96]
412.6
20.630000000000003
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 62.08789658546448, 'TIME_S_1KI': 62.08789658546448, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.450892791748, 'W': 25.109871656394702, 'J_1KI': 1791.450892791748, 'W_1KI': 25.109871656394702, 'W_D': 4.4798716563947, 'J_D': 319.61414173126184, 'W_D_1KI': 4.4798716563947, 'J_D_1KI': 4.4798716563947}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 14, ..., 2741921,
2741928, 2741935]),
col_indices=tensor([ 0, 98273, 133833, ..., 392252, 392254,
392256]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
3.5476]), size=(392257, 392257), nnz=2741935,
layout=torch.sparse_csr)
tensor([0.9401, 0.6405, 0.7589, ..., 0.2451, 0.0732, 0.7376])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: csr
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.493204355239868 seconds
[20.76, 20.92, 20.92, 21.24, 21.12, 20.84, 21.04, 21.04, 21.04, 20.84]
[20.92, 20.88, 20.88, 24.24, 26.4, 27.56, 28.48, 26.48, 26.08, 24.8, 24.84, 25.12, 25.12, 24.88]
14.692817211151123
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 169, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.493204355239868, 'TIME_S_1KI': 62.08996659905248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.01729221343993, 'W': 23.209796141384285}
[20.76, 20.92, 20.92, 21.24, 21.12, 20.84, 21.04, 21.04, 21.04, 20.84, 20.72, 20.68, 20.56, 20.56, 20.72, 20.8, 20.92, 20.72, 20.6, 20.56]
375.15999999999997
18.758
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 169, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.493204355239868, 'TIME_S_1KI': 62.08996659905248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.01729221343993, 'W': 23.209796141384285, 'J_1KI': 2017.8538000795263, 'W_1KI': 137.33607184251056, 'W_D': 4.4517961413842855, 'J_D': 65.40942696666718, 'W_D_1KI': 26.341989002273877, 'J_D_1KI': 155.8697574099046}

2
pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 32.617069482803345, "TIME_S_1KI": 32.617069482803345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 877.9408810043335, "W": 25.358346526187013, "J_1KI": 877.9408810043335, "W_1KI": 25.358346526187013, "W_D": 4.655346526187017, "J_D": 161.17450821805022, "W_D_1KI": 4.655346526187017, "J_D_1KI": 4.655346526187017}
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 347, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.460747718811035, "TIME_S_1KI": 30.146247028273876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.59504707336424, "W": 23.01384037822555, "J_1KI": 967.1327004996087, "W_1KI": 66.3223065654915, "W_D": 4.754840378225545, "J_D": 69.3365754837989, "W_D_1KI": 13.70271002370474, "J_D_1KI": 39.489077878111644}

72
pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 32.617069482803345}
['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/389000+_cols/language.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 3.2151429653167725}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.1808, 0.0389, 0.7706, ..., 0.1715, 0.5157, 0.4224])
tensor([0.5846, 0.7759, 0.0823, ..., 0.4637, 0.9935, 0.7724])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -18,7 +18,10 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 32.617069482803345 seconds
Time: 3.2151429653167725 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 326 -m matrices/389000+_cols/language.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.836416482925415}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -28,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.1808, 0.0389, 0.7706, ..., 0.1715, 0.5157, 0.4224])
tensor([0.8049, 0.0272, 0.8104, ..., 0.9156, 0.2376, 0.7249])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
@ -37,13 +40,54 @@ Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 32.617069482803345 seconds
Time: 9.836416482925415 seconds
[23.16, 23.16, 22.96, 23.16, 22.92, 22.92, 23.0, 22.92, 22.84, 23.16]
[23.24, 23.4, 24.8, 25.6, 27.28, 28.0, 28.0, 28.48, 27.76, 27.96, 26.76, 26.64, 26.76, 26.88, 26.88, 26.96, 26.96, 27.2, 27.0, 27.12, 27.08, 26.68, 27.08, 27.16, 27.08, 27.08, 27.28, 27.0, 27.0, 27.24, 27.16, 27.36, 27.36]
34.62137722969055
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 32.617069482803345, 'TIME_S_1KI': 32.617069482803345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 877.9408810043335, 'W': 25.358346526187013}
[23.16, 23.16, 22.96, 23.16, 22.92, 22.92, 23.0, 22.92, 22.84, 23.16, 22.96, 23.28, 23.08, 23.16, 23.04, 22.88, 22.76, 22.88, 22.96, 23.0]
414.05999999999995
20.702999999999996
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 32.617069482803345, 'TIME_S_1KI': 32.617069482803345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 877.9408810043335, 'W': 25.358346526187013, 'J_1KI': 877.9408810043335, 'W_1KI': 25.358346526187013, 'W_D': 4.655346526187017, 'J_D': 161.17450821805022, 'W_D_1KI': 4.655346526187017, 'J_D_1KI': 4.655346526187017}
['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 347 -m matrices/389000+_cols/language.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.460747718811035}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0958, 0.3528, 0.8462, ..., 0.3065, 0.0799, 0.0392])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.460747718811035 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 3, ..., 1216330,
1216332, 1216334]),
col_indices=tensor([ 0, 0, 1, ..., 399128, 399125,
399129]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
tensor([0.0958, 0.3528, 0.8462, ..., 0.3065, 0.0799, 0.0392])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: csr
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.460747718811035 seconds
[19.96, 20.0, 20.08, 20.2, 20.2, 20.24, 20.44, 20.28, 20.32, 20.08]
[20.2, 20.08, 20.76, 24.32, 26.04, 27.52, 28.56, 26.32, 26.36, 24.8, 24.8, 24.8, 24.64, 24.48]
14.582314014434814
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 347, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.460747718811035, 'TIME_S_1KI': 30.146247028273876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.59504707336424, 'W': 23.01384037822555}
[19.96, 20.0, 20.08, 20.2, 20.2, 20.24, 20.44, 20.28, 20.32, 20.08, 20.76, 20.48, 20.32, 20.24, 20.24, 20.16, 20.36, 20.36, 20.56, 20.6]
365.18000000000006
18.259000000000004
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 347, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.460747718811035, 'TIME_S_1KI': 30.146247028273876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.59504707336424, 'W': 23.01384037822555, 'J_1KI': 967.1327004996087, 'W_1KI': 66.3223065654915, 'W_D': 4.754840378225545, 'J_D': 69.3365754837989, 'W_D_1KI': 13.70271002370474, 'J_D_1KI': 39.489077878111644}

2
pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 136.75263905525208, "TIME_S_1KI": 136.75263905525208, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3809.28440010071, "W": 25.30736544046405, "J_1KI": 3809.28440010071, "W_1KI": 25.30736544046405, "W_D": 4.417365440464049, "J_D": 664.905294132235, "W_D_1KI": 4.417365440464049, "J_D_1KI": 4.417365440464049}
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 13.512069940567017, "TIME_S_1KI": 135.12069940567017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 413.0026450538635, "W": 21.46095871437624, "J_1KI": 4130.026450538635, "W_1KI": 214.6095871437624, "W_D": 2.87995871437624, "J_D": 55.42299309706687, "W_D_1KI": 28.799587143762402, "J_D_1KI": 287.99587143762403}

28
pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 136.75263905525208}
['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/389000+_cols/marine1.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 13.512069940567017}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -10,7 +10,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1033, 0.2543, 0.2854, ..., 0.8643, 0.3799, 0.0773])
tensor([0.1479, 0.2625, 0.3301, ..., 0.4383, 0.0178, 0.9375])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -19,7 +19,7 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 136.75263905525208 seconds
Time: 13.512069940567017 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -30,7 +30,7 @@ tensor(crow_indices=tensor([ 0, 7, 18, ..., 6226522,
values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00, ...,
-6.8099e-01, -6.4187e-01, 1.7595e+01]),
size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
tensor([0.1033, 0.2543, 0.2854, ..., 0.8643, 0.3799, 0.0773])
tensor([0.1479, 0.2625, 0.3301, ..., 0.4383, 0.0178, 0.9375])
Matrix Type: SuiteSparse
Matrix: marine1
Matrix Format: csr
@ -39,13 +39,13 @@ Rows: 400320
Size: 160256102400
NNZ: 6226538
Density: 3.885367175883594e-05
Time: 136.75263905525208 seconds
Time: 13.512069940567017 seconds
[23.6, 23.2, 23.36, 23.44, 23.4, 23.52, 23.52, 23.4, 23.16, 22.92]
[23.12, 23.0, 23.0, 25.76, 27.64, 28.96, 29.28, 28.32, 27.36, 27.36, 27.0, 26.64, 26.6, 26.6, 26.76, 26.64, 26.6, 26.6, 26.4, 26.48, 26.56, 26.76, 26.96, 27.0, 27.04, 26.84, 26.92, 26.64, 26.64, 26.8, 26.88, 26.88, 26.88, 27.04, 27.12, 26.96, 26.96, 26.84, 26.6, 26.6, 26.24, 26.04, 26.0, 26.24, 26.48, 26.88, 27.2, 27.36, 27.16, 27.24, 27.16, 27.04, 27.04, 27.04, 26.88, 26.8, 26.48, 26.68, 26.64, 26.72, 26.68, 26.84, 26.6, 26.48, 26.36, 26.4, 26.28, 26.48, 26.6, 26.48, 26.48, 26.8, 26.76, 26.72, 26.76, 26.76, 26.68, 26.52, 26.48, 26.56, 26.6, 26.52, 26.28, 26.32, 26.24, 26.44, 26.52, 26.6, 26.44, 26.52, 26.56, 26.4, 26.56, 26.64, 26.92, 27.04, 26.92, 26.92, 26.96, 26.92, 26.84, 26.88, 27.16, 27.08, 26.96, 26.72, 26.56, 26.44, 26.4, 26.4, 26.64, 26.72, 26.84, 26.96, 27.2, 26.88, 26.8, 26.8, 26.92, 26.92, 27.08, 27.28, 27.2, 27.0, 26.92, 26.8, 26.64, 26.92, 26.72, 26.76, 26.8, 26.8, 26.76, 26.84, 26.88, 26.72, 26.6, 26.64, 26.52, 26.8, 26.96, 26.96, 27.16]
150.5207805633545
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 136.75263905525208, 'TIME_S_1KI': 136.75263905525208, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3809.28440010071, 'W': 25.30736544046405}
[23.6, 23.2, 23.36, 23.44, 23.4, 23.52, 23.52, 23.4, 23.16, 22.92, 23.28, 23.28, 23.28, 23.16, 23.08, 22.96, 22.84, 22.8, 22.92, 23.16]
417.8
20.89
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 136.75263905525208, 'TIME_S_1KI': 136.75263905525208, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3809.28440010071, 'W': 25.30736544046405, 'J_1KI': 3809.28440010071, 'W_1KI': 25.30736544046405, 'W_D': 4.417365440464049, 'J_D': 664.905294132235, 'W_D_1KI': 4.417365440464049, 'J_D_1KI': 4.417365440464049}
[20.4, 20.32, 20.44, 20.48, 20.88, 20.88, 20.96, 20.56, 20.44, 20.52]
[20.16, 20.28, 20.48, 22.16, 23.56, 24.76, 26.16, 26.08, 25.8, 25.0, 24.68, 24.28, 24.28, 24.2, 24.36, 24.32, 24.36, 24.4]
19.244370698928833
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 13.512069940567017, 'TIME_S_1KI': 135.12069940567017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 413.0026450538635, 'W': 21.46095871437624}
[20.4, 20.32, 20.44, 20.48, 20.88, 20.88, 20.96, 20.56, 20.44, 20.52, 20.28, 20.32, 20.32, 20.52, 20.64, 21.04, 21.12, 20.92, 20.84, 20.68]
371.62
18.581
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 13.512069940567017, 'TIME_S_1KI': 135.12069940567017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 413.0026450538635, 'W': 21.46095871437624, 'J_1KI': 4130.026450538635, 'W_1KI': 214.6095871437624, 'W_D': 2.87995871437624, 'J_D': 55.42299309706687, 'W_D_1KI': 28.799587143762402, 'J_D_1KI': 287.99587143762403}

2
pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 51.17483377456665, "TIME_S_1KI": 51.17483377456665, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1410.4799920654298, "W": 25.376735316064863, "J_1KI": 1410.4799920654298, "W_1KI": 25.376735316064863, "W_D": 4.522735316064864, "J_D": 251.3809437370302, "W_D_1KI": 4.522735316064864, "J_D_1KI": 4.522735316064864}
{"CPU": "Altra", "CORES": 1, "ITERATIONS": 205, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.534985065460205, "TIME_S_1KI": 51.39017105102539, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.8878989028931, "W": 22.516071230483117, "J_1KI": 1609.2092629409417, "W_1KI": 109.83449380723472, "W_D": 4.122071230483119, "J_D": 60.39336984825137, "W_D_1KI": 20.107664538942046, "J_D_1KI": 98.08616848264414}

50
pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output
View File

@ -1,5 +1,5 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 51.17483377456665}
['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/389000+_cols/mario002.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 5.110914945602417}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -9,7 +9,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.0328, 0.3187, 0.7172, ..., 0.3931, 0.0888, 0.1198])
tensor([0.5986, 0.3919, 0.1592, ..., 0.0014, 0.5481, 0.1891])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -18,7 +18,10 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 51.17483377456665 seconds
Time: 5.110914945602417 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 205 -m matrices/389000+_cols/mario002.mtx -c 1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.534985065460205}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
@ -28,7 +31,7 @@ tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.0328, 0.3187, 0.7172, ..., 0.3931, 0.0888, 0.1198])
tensor([0.3875, 0.0731, 0.2406, ..., 0.0958, 0.8606, 0.5862])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
@ -37,13 +40,32 @@ Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 51.17483377456665 seconds
Time: 10.534985065460205 seconds
[23.16, 23.16, 23.16, 23.28, 23.28, 23.48, 23.44, 23.4, 23.44, 23.44]
[23.4, 23.48, 23.92, 24.84, 26.8, 27.6, 28.64, 28.08, 28.08, 26.88, 26.8, 26.8, 26.76, 26.76, 26.8, 26.64, 26.68, 26.48, 26.72, 26.96, 27.12, 27.2, 27.08, 27.12, 27.2, 27.12, 26.88, 27.04, 27.04, 27.2, 27.36, 27.24, 27.2, 27.2, 27.12, 27.12, 27.2, 26.92, 26.88, 26.88, 26.92, 26.84, 26.6, 26.84, 26.68, 26.72, 26.96, 26.72, 26.64, 26.84, 26.76, 27.0, 27.2]
55.58161735534668
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 51.17483377456665, 'TIME_S_1KI': 51.17483377456665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.4799920654298, 'W': 25.376735316064863}
[23.16, 23.16, 23.16, 23.28, 23.28, 23.48, 23.44, 23.4, 23.44, 23.44, 23.08, 22.96, 22.96, 23.04, 23.08, 22.96, 23.04, 23.12, 22.88, 23.12]
417.08
20.854
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 51.17483377456665, 'TIME_S_1KI': 51.17483377456665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.4799920654298, 'W': 25.376735316064863, 'J_1KI': 1410.4799920654298, 'W_1KI': 25.376735316064863, 'W_D': 4.522735316064864, 'J_D': 251.3809437370302, 'W_D_1KI': 4.522735316064864, 'J_D_1KI': 4.522735316064864}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 2101236,
2101239, 2101242]),
col_indices=tensor([ 0, 1027, 1028, ..., 196606, 233926,
234127]),
values=tensor([ 1., 0., 0., ..., -1., -1., -1.]),
size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
tensor([0.3875, 0.0731, 0.2406, ..., 0.0958, 0.8606, 0.5862])
Matrix Type: SuiteSparse
Matrix: mario002
Matrix Format: csr
Shape: torch.Size([389874, 389874])
Rows: 389874
Size: 152001735876
NNZ: 2101242
Density: 1.3823802655215408e-05
Time: 10.534985065460205 seconds
[19.88, 20.0, 20.0, 20.32, 20.44, 20.52, 20.52, 20.4, 20.44, 20.36]
[20.36, 20.44, 21.44, 22.44, 24.4, 25.2, 26.12, 26.12, 25.88, 25.6, 24.52, 24.72, 24.6, 24.52]
14.651219367980957
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 205, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.534985065460205, 'TIME_S_1KI': 51.39017105102539, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.8878989028931, 'W': 22.516071230483117}
[19.88, 20.0, 20.0, 20.32, 20.44, 20.52, 20.52, 20.4, 20.44, 20.36, 20.6, 20.72, 20.72, 20.6, 20.76, 20.48, 20.28, 20.44, 20.56, 20.52]
367.88
18.394
{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 205, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.534985065460205, 'TIME_S_1KI': 51.39017105102539, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.8878989028931, 'W': 22.516071230483117, 'J_1KI': 1609.2092629409417, 'W_1KI': 109.83449380723472, 'W_D': 4.122071230483119, 'J_D': 60.39336984825137, 'W_D_1KI': 20.107664538942046, 'J_D_1KI': 98.08616848264414}

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