From e22addba445e8eb05efe7b8523c6cdb84e6618f2 Mon Sep 17 00:00:00 2001 From: cephi Date: Tue, 17 Dec 2024 20:55:35 -0500 Subject: [PATCH] 389000+ data --- .../altra_16_csr_10_10_10_amazon0312.json | 2 +- .../altra_16_csr_10_10_10_amazon0312.output | 36 +++--- .../altra_16_csr_10_10_10_helm2d03.json | 2 +- .../altra_16_csr_10_10_10_helm2d03.output | 36 +++--- .../altra_16_csr_10_10_10_language.json | 2 +- .../altra_16_csr_10_10_10_language.output | 36 +++--- .../altra_16_csr_10_10_10_marine1.json | 2 +- .../altra_16_csr_10_10_10_marine1.output | 51 ++++++-- .../altra_16_csr_10_10_10_mario002.json | 2 +- .../altra_16_csr_10_10_10_mario002.output | 58 +++------ .../altra_16_csr_10_10_10_msdoor.json | 2 +- .../altra_16_csr_10_10_10_msdoor.output | 35 ++--- .../altra_16_csr_10_10_10_test1.json | 2 +- .../altra_16_csr_10_10_10_test1.output | 51 ++++++-- ...epyc_7313p_16_csr_10_10_10_amazon0312.json | 2 +- ...yc_7313p_16_csr_10_10_10_amazon0312.output | 66 ++++++---- 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.../altra_1_csr_10_10_10_test1.json | 1 + .../altra_1_csr_10_10_10_test1.output | 51 ++++++++ .../epyc_7313p_1_csr_10_10_10_amazon0312.json | 1 + ...pyc_7313p_1_csr_10_10_10_amazon0312.output | 71 +++++++++++ .../epyc_7313p_1_csr_10_10_10_darcy003.json | 0 .../epyc_7313p_1_csr_10_10_10_darcy003.output | 0 .../epyc_7313p_1_csr_10_10_10_helm2d03.json | 1 + .../epyc_7313p_1_csr_10_10_10_helm2d03.output | 74 +++++++++++ .../epyc_7313p_1_csr_10_10_10_language.json | 1 + .../epyc_7313p_1_csr_10_10_10_language.output | 71 +++++++++++ .../epyc_7313p_1_csr_10_10_10_marine1.json | 1 + .../epyc_7313p_1_csr_10_10_10_marine1.output | 74 +++++++++++ .../epyc_7313p_1_csr_10_10_10_mario002.json | 1 + .../epyc_7313p_1_csr_10_10_10_mario002.output | 71 +++++++++++ .../epyc_7313p_1_csr_10_10_10_msdoor.json | 1 + .../epyc_7313p_1_csr_10_10_10_msdoor.output | 74 +++++++++++ .../epyc_7313p_1_csr_10_10_10_test1.json | 1 + .../epyc_7313p_1_csr_10_10_10_test1.output | 51 ++++++++ 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.../altra_max_csr_10_10_10_helm2d03.json | 2 +- .../altra_max_csr_10_10_10_helm2d03.output | 51 ++++++-- .../altra_max_csr_10_10_10_language.json | 2 +- .../altra_max_csr_10_10_10_language.output | 72 +++++++++-- .../altra_max_csr_10_10_10_marine1.json | 2 +- .../altra_max_csr_10_10_10_marine1.output | 74 +++++++++-- .../altra_max_csr_10_10_10_mario002.json | 2 +- .../altra_max_csr_10_10_10_mario002.output | 50 ++++++-- .../altra_max_csr_10_10_10_msdoor.json | 2 +- .../altra_max_csr_10_10_10_msdoor.output | 26 ++-- .../altra_max_csr_10_10_10_test1.json | 2 +- .../altra_max_csr_10_10_10_test1.output | 51 ++++++-- ...pyc_7313p_max_csr_10_10_10_amazon0312.json | 2 +- ...c_7313p_max_csr_10_10_10_amazon0312.output | 44 +++---- .../epyc_7313p_max_csr_10_10_10_helm2d03.json | 2 +- ...pyc_7313p_max_csr_10_10_10_helm2d03.output | 44 +++---- .../epyc_7313p_max_csr_10_10_10_language.json | 2 +- ...pyc_7313p_max_csr_10_10_10_language.output | 44 +++---- .../epyc_7313p_max_csr_10_10_10_marine1.json | 2 +- ...epyc_7313p_max_csr_10_10_10_marine1.output | 44 +++---- .../epyc_7313p_max_csr_10_10_10_mario002.json | 2 +- ...pyc_7313p_max_csr_10_10_10_mario002.output | 58 ++++++--- .../epyc_7313p_max_csr_10_10_10_msdoor.json | 2 +- .../epyc_7313p_max_csr_10_10_10_msdoor.output | 80 +++--------- .../epyc_7313p_max_csr_10_10_10_test1.json | 2 +- .../epyc_7313p_max_csr_10_10_10_test1.output | 36 +++--- ...xeon_4216_max_csr_10_10_10_amazon0312.json | 2 +- ...on_4216_max_csr_10_10_10_amazon0312.output | 58 ++++++--- .../xeon_4216_max_csr_10_10_10_helm2d03.json | 2 +- ...xeon_4216_max_csr_10_10_10_helm2d03.output | 59 ++++++--- .../xeon_4216_max_csr_10_10_10_language.json | 2 +- ...xeon_4216_max_csr_10_10_10_language.output | 58 ++++++--- .../xeon_4216_max_csr_10_10_10_marine1.json | 2 +- .../xeon_4216_max_csr_10_10_10_marine1.output | 59 ++++++--- .../xeon_4216_max_csr_10_10_10_mario002.json | 2 +- ...xeon_4216_max_csr_10_10_10_mario002.output | 58 ++++++--- 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.../altra_max_csr_10_10_10_test1.output | 51 ++++++++ ...pyc_7313p_max_csr_10_10_10_amazon0312.json | 1 + ...c_7313p_max_csr_10_10_10_amazon0312.output | 93 ++++++++++++++ .../epyc_7313p_max_csr_10_10_10_darcy003.json | 0 ...pyc_7313p_max_csr_10_10_10_darcy003.output | 0 .../epyc_7313p_max_csr_10_10_10_helm2d03.json | 1 + ...pyc_7313p_max_csr_10_10_10_helm2d03.output | 97 ++++++++++++++ .../epyc_7313p_max_csr_10_10_10_language.json | 1 + ...pyc_7313p_max_csr_10_10_10_language.output | 93 ++++++++++++++ .../epyc_7313p_max_csr_10_10_10_marine1.json | 1 + ...epyc_7313p_max_csr_10_10_10_marine1.output | 97 ++++++++++++++ .../epyc_7313p_max_csr_10_10_10_mario002.json | 1 + ...pyc_7313p_max_csr_10_10_10_mario002.output | 71 +++++++++++ .../epyc_7313p_max_csr_10_10_10_msdoor.json | 1 + .../epyc_7313p_max_csr_10_10_10_msdoor.output | 120 ++++++++++++++++++ .../epyc_7313p_max_csr_10_10_10_test1.json | 1 + .../epyc_7313p_max_csr_10_10_10_test1.output | 74 +++++++++++ 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pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.json create mode 100644 pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.output diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json index bf1ce83..0aef545 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output index 7077482..242d745 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json index 4535749..70e1727 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output index c022936..0c90985 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json index 319d17a..d0b27b4 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output index e43ddab..12627b5 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json index ca7dd60..37eb920 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output index 30a4718..dfef9cd 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json index de53ae3..06a7d47 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output index 4db99ef..e99b992 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.json index a25f779..3fc3c17 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.output index 77e0068..197e798 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_msdoor.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json index 45ba869..a71a86d 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output index fec9ae3..cbf04f7 100644 --- a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json index 6a184df..d380ae0 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output index 1947bf8..d079fe5 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json index 0316f37..b321712 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output index 257335e..86d0c0c 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json index 26cda3b..7385c7d 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output index a5b64fc..0606762 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json index c6e3e05..bec6821 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output index d7a731b..c2f9be4 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json index aeda75b..e4d6cba 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output index 6816aec..8355fb2 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.json index 2318b6a..87f835c 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.output index e7f18c5..d82a2fe 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_msdoor.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json index ce7af8e..10ff30c 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output index 4cb795c..bff0482 100644 --- a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json index a5b23aa..39e971d 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output index cece27c..d395499 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json index 6b89e52..ffa4569 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output index 9f3e3b4..1bf997f 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json index 3630ff9..f1f4329 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output index 437027f..9d27ae2 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json index 64c9afa..5adb4ae 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output index 17a33fe..e8224ae 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json index ebecca4..b049afe 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output index 4381db9..0a33c0e 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.json index 73affda..035ac69 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.output index 0ffdfda..7bba5a9 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_msdoor.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json index 480eeda..81f0537 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output index 9043b10..489e091 100644 --- a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..bf1ce83 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..7077482 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..4535749 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..c022936 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_helm2d03.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.json new file mode 100644 index 0000000..319d17a --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.output new file mode 100644 index 0000000..e43ddab --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_language.output @@ -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/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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.json new file mode 100644 index 0000000..ca7dd60 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.output new file mode 100644 index 0000000..30a4718 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.json new file mode 100644 index 0000000..de53ae3 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.output new file mode 100644 index 0000000..4db99ef --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..a25f779 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..77e0068 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_msdoor.output @@ -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 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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.json new file mode 100644 index 0000000..45ba869 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.output new file mode 100644 index 0000000..fec9ae3 --- /dev/null +++ b/pytorch/output_389000+_16core_old/altra_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..6a184df --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..1947bf8 --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..0316f37 --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..257335e --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_helm2d03.output @@ -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/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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.json new file mode 100644 index 0000000..26cda3b --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.output new file mode 100644 index 0000000..a5b64fc --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_language.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.json new file mode 100644 index 0000000..c6e3e05 --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.output new file mode 100644 index 0000000..d7a731b --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.json new file mode 100644 index 0000000..aeda75b --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.output new file mode 100644 index 0000000..6816aec --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..2318b6a --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..e7f18c5 --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_msdoor.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.json new file mode 100644 index 0000000..ce7af8e --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.output new file mode 100644 index 0000000..4cb795c --- /dev/null +++ b/pytorch/output_389000+_16core_old/epyc_7313p_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..a5b23aa --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..cece27c --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..6b89e52 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..9f3e3b4 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_helm2d03.output @@ -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/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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.json new file mode 100644 index 0000000..3630ff9 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.output new file mode 100644 index 0000000..437027f --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_language.output @@ -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/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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.json new file mode 100644 index 0000000..64c9afa --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.output new file mode 100644 index 0000000..17a33fe --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_marine1.output @@ -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/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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.json new file mode 100644 index 0000000..ebecca4 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.output new file mode 100644 index 0000000..4381db9 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..73affda --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..0ffdfda --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_msdoor.output @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.json new file mode 100644 index 0000000..480eeda --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.json @@ -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} diff --git a/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.output new file mode 100644 index 0000000..9043b10 --- /dev/null +++ b/pytorch/output_389000+_16core_old/xeon_4216_16_csr_10_10_10_test1.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json index f65875c..b3e7f92 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output index 59f928c..5e49c74 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json index b96c66c..cdb7dcd 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output index 13ebb65..f160e2f 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json index e4470f0..25ea01e 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output index 8ed6535..4bc3a63 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json index 91cb90d..f165f10 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output index 5e3841d..43dcd3d 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json index 931b93e..02b5b8c 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output index 103b1d5..80cc5f2 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output @@ -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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.json index 56d1bf6..7c091f1 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "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": 43.16092610359192, "TIME_S_1KI": 431.6092610359192, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.33110124588, "W": 22.848960166729626, "J_1KI": 11893.3110124588, "W_1KI": 228.48960166729626, "W_D": 4.571960166729628, "J_D": 237.97907564592364, "W_D_1KI": 45.71960166729628, "J_D_1KI": 457.19601667296286} +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "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": 43.16734552383423, "TIME_S_1KI": 431.6734552383423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1166.0047177505492, "W": 22.820710139365556, "J_1KI": 11660.047177505492, "W_1KI": 228.20710139365553, "W_D": 4.429710139365554, "J_D": 226.3322608816622, "W_D_1KI": 44.29710139365554, "J_D_1KI": 442.9710139365554} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.output index ab564a0..ed363e4 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_msdoor.output @@ -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 100 -m matrices/389000+_cols/msdoor.mtx -c 1'] -{"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": 43.16092610359192} +{"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": 43.16734552383423} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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, 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([0.0523, 0.1527, 0.8721, ..., 0.0519, 0.6042, 0.4109]) +tensor([0.1692, 0.3089, 0.0633, ..., 0.5748, 0.9456, 0.4795]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,7 +19,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 43.16092610359192 seconds +Time: 43.16734552383423 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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, 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([0.0523, 0.1527, 0.8721, ..., 0.0519, 0.6042, 0.4109]) +tensor([0.1692, 0.3089, 0.0633, ..., 0.5748, 0.9456, 0.4795]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -39,13 +39,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 43.16092610359192 seconds +Time: 43.16734552383423 seconds -[20.12, 19.92, 20.36, 20.44, 20.2, 20.04, 20.08, 19.84, 20.04, 20.04] -[20.2, 20.44, 20.76, 24.08, 25.44, 27.84, 28.48, 28.32, 27.16, 26.68, 25.2, 25.2, 24.44, 24.64, 24.56, 24.44, 24.4, 24.6, 24.4, 24.4, 24.36, 24.32, 24.28, 24.44, 24.56, 24.44, 24.44, 24.56, 24.52, 24.72, 25.24, 25.12, 25.36, 25.36, 25.56, 25.16, 25.12, 25.12, 24.84, 24.48, 24.44, 24.16, 24.12, 24.36, 24.32, 24.44, 24.28, 24.32, 24.12] -52.05186986923218 -{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16092610359192, 'TIME_S_1KI': 431.6092610359192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.33110124588, 'W': 22.848960166729626} -[20.12, 19.92, 20.36, 20.44, 20.2, 20.04, 20.08, 19.84, 20.04, 20.04, 20.28, 20.36, 20.48, 20.44, 20.44, 20.6, 20.72, 20.64, 20.48, 20.48] -365.53999999999996 -18.276999999999997 -{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16092610359192, 'TIME_S_1KI': 431.6092610359192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.33110124588, 'W': 22.848960166729626, 'J_1KI': 11893.3110124588, 'W_1KI': 228.48960166729626, 'W_D': 4.571960166729628, 'J_D': 237.97907564592364, 'W_D_1KI': 45.71960166729628, 'J_D_1KI': 457.19601667296286} +[20.12, 20.2, 20.12, 20.12, 20.2, 20.2, 20.48, 20.6, 20.88, 20.76] +[20.76, 20.68, 23.4, 24.2, 26.24, 27.2, 29.08, 28.4, 27.84, 26.88, 26.16, 25.24, 24.4, 24.52, 24.4, 24.28, 24.24, 24.28, 24.44, 24.44, 24.12, 24.36, 24.32, 24.32, 24.2, 24.6, 24.72, 24.48, 24.6, 24.64, 24.44, 24.52, 24.32, 24.16, 24.2, 24.48, 24.76, 24.84, 24.88, 24.56, 24.48, 24.44, 24.24, 24.52, 24.56, 24.56, 24.44, 24.48] +51.094146966934204 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16734552383423, 'TIME_S_1KI': 431.6734552383423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1166.0047177505492, 'W': 22.820710139365556} +[20.12, 20.2, 20.12, 20.12, 20.2, 20.2, 20.48, 20.6, 20.88, 20.76, 20.8, 20.68, 20.44, 20.24, 20.28, 20.12, 20.48, 20.64, 20.92, 20.76] +367.82000000000005 +18.391000000000002 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16734552383423, 'TIME_S_1KI': 431.6734552383423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1166.0047177505492, 'W': 22.820710139365556, 'J_1KI': 11660.047177505492, 'W_1KI': 228.20710139365553, 'W_D': 4.429710139365554, 'J_D': 226.3322608816622, 'W_D_1KI': 44.29710139365554, 'J_D_1KI': 442.9710139365554} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json index 4bb27e2..fc63e4f 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 1, "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": 283.81978273391724, "TIME_S_1KI": 283.81978273391724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7375.577878112791, "W": 25.373727151984518, "J_1KI": 7375.577878112791, "W_1KI": 25.373727151984518, "W_D": 4.492727151984518, "J_D": 1305.9358129017337, "W_D_1KI": 4.492727151984518, "J_D_1KI": 4.492727151984518} +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "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": 27.73076057434082, "TIME_S_1KI": 277.3076057434082, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.0920621681213, "W": 22.583246727596133, "J_1KI": 8460.920621681213, "W_1KI": 225.83246727596133, "W_D": 4.128246727596132, "J_D": 154.666723922491, "W_D_1KI": 41.28246727596132, "J_D_1KI": 412.8246727596132} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output index c57d8f0..cf7dd4e 100644 --- a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output @@ -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/test1.mtx -c 1'] -{"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": 283.81978273391724} +['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/test1.mtx -c 1'] +{"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": 27.73076057434082} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.4069, 0.7437, 0.0499, ..., 0.6858, 0.0232, 0.7224]) +tensor([0.6803, 0.4155, 0.1837, ..., 0.1437, 0.2033, 0.7119]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,7 +19,7 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 283.81978273391724 seconds +Time: 27.73076057434082 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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, 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([0.4069, 0.7437, 0.0499, ..., 0.6858, 0.0232, 0.7224]) +tensor([0.6803, 0.4155, 0.1837, ..., 0.1437, 0.2033, 0.7119]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -39,13 +39,13 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 283.81978273391724 seconds +Time: 27.73076057434082 seconds -[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36] -[23.36, 23.0, 23.52, 26.52, 28.0, 29.08, 29.6, 28.4, 27.72, 27.56, 27.4, 26.84, 27.08, 27.28, 27.24, 27.04, 26.76, 26.76, 26.68, 26.48, 26.44, 26.48, 26.36, 26.2, 26.48, 26.48, 26.64, 26.64, 26.68, 26.52, 26.52, 26.68, 26.64, 26.68, 26.68, 26.68, 26.64, 26.64, 26.64, 26.92, 26.84, 26.6, 26.72, 26.68, 26.44, 26.76, 26.96, 26.8, 26.8, 26.96, 27.0, 26.92, 26.96, 26.96, 26.64, 26.52, 26.36, 26.56, 26.6, 26.72, 26.72, 26.72, 26.64, 26.6, 26.52, 26.64, 26.52, 26.48, 26.44, 26.44, 26.48, 26.56, 26.76, 26.64, 26.76, 26.72, 26.76, 26.8, 26.76, 26.64, 26.44, 26.44, 26.44, 26.44, 26.64, 26.56, 26.76, 26.76, 26.68, 26.68, 26.72, 26.72, 26.8, 26.8, 26.64, 26.4, 26.52, 26.56, 26.68, 26.68, 26.96, 26.72, 26.64, 26.76, 26.76, 26.84, 26.76, 26.96, 26.8, 26.76, 26.68, 26.72, 26.8, 27.12, 27.08, 26.92, 26.68, 26.28, 26.2, 26.44, 26.4, 26.68, 26.88, 26.92, 27.08, 26.96, 26.96, 26.88, 26.84, 26.68, 26.64, 26.68, 26.52, 26.48, 26.28, 26.28, 26.24, 26.44, 26.76, 27.04, 26.92, 27.0, 27.0, 27.0, 26.84, 26.92, 26.88, 26.92, 26.92, 26.8, 26.8, 26.84, 26.92, 26.92, 27.08, 26.92, 27.0, 26.92, 26.8, 26.8, 26.96, 26.84, 26.92, 26.84, 26.68, 26.6, 26.68, 26.72, 26.96, 26.76, 26.76, 26.6, 26.4, 26.28, 26.2, 26.44, 26.8, 26.76, 26.92, 27.04, 26.8, 26.6, 26.6, 26.32, 26.24, 26.4, 26.28, 26.36, 26.64, 26.64, 26.6, 26.68, 26.68, 26.44, 26.24, 26.12, 26.36, 26.44, 26.68, 26.8, 26.92, 26.96, 26.92, 26.88, 26.88, 26.76, 26.76, 26.8, 26.68, 26.72, 26.64, 26.52, 26.64, 26.92, 26.92, 26.76, 26.6, 26.6, 26.56, 26.36, 26.36, 26.64, 26.6, 26.72, 26.76, 26.84, 26.72, 27.04, 26.72, 26.6, 26.88, 26.76, 26.72, 26.64, 26.8, 27.04, 27.04, 26.96, 27.04, 27.04, 26.8, 26.68, 26.88, 26.84, 27.0, 26.92, 26.96, 27.0, 26.76, 26.8, 26.88, 26.88, 26.96, 27.0, 27.12, 27.32, 27.32, 27.32, 27.32, 27.24, 26.88, 26.6, 26.68, 26.64, 26.92, 27.08, 27.28, 27.04, 26.92, 27.08, 27.4, 27.56, 27.6, 27.48, 27.2, 26.88] -290.67774844169617 -{'CPU': 'Altra', 'CORES': 1, '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': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518} -[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36, 23.48, 23.28, 23.2, 23.24, 23.24, 23.32, 23.6, 23.8, 23.52, 23.52] -417.62 -20.881 -{'CPU': 'Altra', 'CORES': 1, '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': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518, 'J_1KI': 7375.577878112791, 'W_1KI': 25.373727151984518, 'W_D': 4.492727151984518, 'J_D': 1305.9358129017337, 'W_D_1KI': 4.492727151984518, 'J_D_1KI': 4.492727151984518} +[20.68, 20.76, 20.72, 20.72, 20.6, 20.56, 20.52, 20.56, 20.56, 20.56] +[20.52, 20.6, 20.68, 23.72, 25.64, 27.64, 28.56, 27.28, 26.4, 26.36, 24.88, 24.52, 24.52, 24.64, 24.72, 24.48, 24.32, 23.88, 24.04, 24.12, 24.28, 24.6, 24.48, 24.16, 24.08, 23.92, 24.0, 24.16, 24.6, 24.64, 24.52, 24.56, 24.56, 24.56, 24.56] +37.46547484397888 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 27.73076057434082, 'TIME_S_1KI': 277.3076057434082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.0920621681213, 'W': 22.583246727596133} +[20.68, 20.76, 20.72, 20.72, 20.6, 20.56, 20.52, 20.56, 20.56, 20.56, 20.4, 20.4, 20.28, 20.28, 20.64, 20.52, 20.48, 20.4, 20.24, 20.08] +369.1 +18.455000000000002 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 27.73076057434082, 'TIME_S_1KI': 277.3076057434082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.0920621681213, 'W': 22.583246727596133, 'J_1KI': 8460.920621681213, 'W_1KI': 225.83246727596133, 'W_D': 4.128246727596132, 'J_D': 154.666723922491, 'W_D_1KI': 41.28246727596132, 'J_D_1KI': 412.8246727596132} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json index 4f2ba5e..d2dc5b9 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1665, "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": 10.677687168121338, "TIME_S_1KI": 6.413025326199002, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 984.0334419322014, "W": 72.51, "J_1KI": 591.0110762355564, "W_1KI": 43.54954954954955, "W_D": 37.19775000000001, "J_D": 504.81078423160335, "W_D_1KI": 22.340990990991, "J_D_1KI": 13.418012607201801} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1640, "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": 10.312610626220703, "TIME_S_1KI": 6.288177211110185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 973.239358716011, "W": 72.61, "J_1KI": 593.4386333634213, "W_1KI": 44.27439024390244, "W_D": 37.234249999999996, "J_D": 499.0750253721475, "W_D_1KI": 22.703810975609755, "J_D_1KI": 13.843787180249851} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output index 78545de..860a286 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 6.305053472518921} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 0.6839416027069092} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6406, 0.1468, 0.7280, ..., 0.0181, 0.2043, 0.6040]) +tensor([0.5819, 0.9081, 0.4203, ..., 0.7148, 0.8057, 0.4552]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 6.305053472518921 seconds +Time: 0.6839416027069092 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1665', '-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": 10.677687168121338} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1535', '-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": 9.822561979293823} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7736, 0.6337, 0.2989, ..., 0.0625, 0.4089, 0.3233]) +tensor([0.4460, 0.0313, 0.4053, ..., 0.8937, 0.3451, 0.2285]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -40,7 +40,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.677687168121338 seconds +Time: 9.822561979293823 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1640', '-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": 10.312610626220703} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7736, 0.6337, 0.2989, ..., 0.0625, 0.4089, 0.3233]) +tensor([0.4869, 0.2442, 0.5473, ..., 0.5030, 0.2690, 0.4740]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -59,13 +62,32 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.677687168121338 seconds +Time: 10.312610626220703 seconds -[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39] -[72.51] -13.571003198623657 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, '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': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51} -[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39, 39.83, 38.29, 40.69, 40.97, 41.34, 39.2, 38.48, 38.73, 38.45, 38.46] -706.2449999999999 -35.31224999999999 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, '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': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51, 'J_1KI': 591.0110762355564, 'W_1KI': 43.54954954954955, 'W_D': 37.19775000000001, 'J_D': 504.81078423160335, 'W_D_1KI': 22.340990990991, 'J_D_1KI': 13.418012607201801} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4869, 0.2442, 0.5473, ..., 0.5030, 0.2690, 0.4740]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.312610626220703 seconds + +[39.92, 39.02, 39.56, 39.36, 39.17, 39.85, 38.95, 38.96, 39.22, 40.46] +[72.61] +13.4036545753479 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1640, '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': 10.312610626220703, 'TIME_S_1KI': 6.288177211110185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.239358716011, 'W': 72.61} +[39.92, 39.02, 39.56, 39.36, 39.17, 39.85, 38.95, 38.96, 39.22, 40.46, 39.59, 39.86, 39.7, 39.26, 38.95, 38.95, 39.34, 38.85, 38.88, 39.3] +707.5150000000001 +35.375750000000004 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1640, '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': 10.312610626220703, 'TIME_S_1KI': 6.288177211110185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.239358716011, 'W': 72.61, 'J_1KI': 593.4386333634213, 'W_1KI': 44.27439024390244, 'W_D': 37.234249999999996, 'J_D': 499.0750253721475, 'W_D_1KI': 22.703810975609755, 'J_D_1KI': 13.843787180249851} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json index 6c16e41..f7ee805 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2881, "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": 10.489487886428833, "TIME_S_1KI": 3.640919085882969, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 982.9177148509026, "W": 72.89, "J_1KI": 341.1724105695601, "W_1KI": 25.30024297119056, "W_D": 38.247499999999995, "J_D": 515.765472612977, "W_D_1KI": 13.275772301284276, "J_D_1KI": 4.6080431451871835} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2807, "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": 10.296238899230957, "TIME_S_1KI": 3.668058033213736, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 989.6942229676247, "W": 74.09, "J_1KI": 352.5807705620323, "W_1KI": 26.39472746704667, "W_D": 38.78175, "J_D": 518.0466180533767, "W_D_1KI": 13.816084788029926, "J_D_1KI": 4.922010968304213} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output index 3aa1656..2462558 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.64394474029541} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 0.37395262718200684} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2874, 0.3706, 0.3465, ..., 0.0468, 0.1058, 0.2863]) +tensor([0.0564, 0.2513, 0.2473, ..., 0.9627, 0.1279, 0.7809]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 3.64394474029541 seconds +Time: 0.37395262718200684 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2881', '-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": 10.489487886428833} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2807', '-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": 10.296238899230957} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4950, 0.0177, 0.5787, ..., 0.9424, 0.3532, 0.5521]) +tensor([0.9506, 0.0019, 0.6916, ..., 0.0358, 0.6672, 0.3300]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -42,7 +42,7 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.489487886428833 seconds +Time: 10.296238899230957 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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, 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([0.4950, 0.0177, 0.5787, ..., 0.9424, 0.3532, 0.5521]) +tensor([0.9506, 0.0019, 0.6916, ..., 0.0358, 0.6672, 0.3300]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -62,13 +62,13 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.489487886428833 seconds +Time: 10.296238899230957 seconds -[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77] -[72.89] -13.484946012496948 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, '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': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89} -[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77, 39.12, 38.21, 38.45, 38.24, 39.01, 38.22, 38.93, 38.26, 38.73, 38.6] -692.8500000000001 -34.642500000000005 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, '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': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89, 'J_1KI': 341.1724105695601, 'W_1KI': 25.30024297119056, 'W_D': 38.247499999999995, 'J_D': 515.765472612977, 'W_D_1KI': 13.275772301284276, 'J_D_1KI': 4.6080431451871835} +[39.65, 39.87, 39.06, 38.75, 39.2, 39.07, 39.13, 39.03, 38.78, 38.69] +[74.09] +13.358000040054321 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2807, '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': 10.296238899230957, 'TIME_S_1KI': 3.668058033213736, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 989.6942229676247, 'W': 74.09} +[39.65, 39.87, 39.06, 38.75, 39.2, 39.07, 39.13, 39.03, 38.78, 38.69, 39.76, 39.3, 38.89, 38.7, 39.08, 38.75, 38.83, 38.9, 40.51, 42.53] +706.165 +35.30825 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2807, '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': 10.296238899230957, 'TIME_S_1KI': 3.668058033213736, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 989.6942229676247, 'W': 74.09, 'J_1KI': 352.5807705620323, 'W_1KI': 26.39472746704667, 'W_D': 38.78175, 'J_D': 518.0466180533767, 'W_D_1KI': 13.816084788029926, 'J_D_1KI': 4.922010968304213} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json index d92fabc..f3254cb 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3433, "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": 10.349842309951782, "TIME_S_1KI": 3.01480987764398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.6259407162667, "W": 65.83, "J_1KI": 252.43983126020004, "W_1KI": 19.175648121176813, "W_D": 31.058999999999997, "J_D": 408.8794636595249, "W_D_1KI": 9.047189047480337, "J_D_1KI": 2.635359466204584} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3356, "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": 10.099922180175781, "TIME_S_1KI": 3.0095119726387907, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.0190241909028, "W": 66.12, "J_1KI": 254.17730160634767, "W_1KI": 19.702026221692492, "W_D": 30.810000000000002, "J_D": 397.48209521055225, "W_D_1KI": 9.180572109654351, "J_D_1KI": 2.7355697585382455} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output index 994626a..661bea9 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.058311939239502} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 0.3128049373626709} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8774, 0.7244, 0.5547, ..., 0.2046, 0.1297, 0.0114]) +tensor([0.5520, 0.3932, 0.1887, ..., 0.6944, 0.9841, 0.0162]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 3.058311939239502 seconds +Time: 0.3128049373626709 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '3433', '-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": 10.349842309951782} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '3356', '-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": 10.099922180175781} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8074, 0.3012, 0.9549, ..., 0.2881, 0.3396, 0.4512]) +tensor([0.9507, 0.2454, 0.3603, ..., 0.3391, 0.0838, 0.0705]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -40,7 +40,7 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.349842309951782 seconds +Time: 10.099922180175781 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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 +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([0.8074, 0.3012, 0.9549, ..., 0.2881, 0.3396, 0.4512]) +tensor([0.9507, 0.2454, 0.3603, ..., 0.3391, 0.0838, 0.0705]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -59,13 +59,13 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.349842309951782 seconds +Time: 10.099922180175781 seconds -[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47] -[65.83] -13.164604902267456 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, '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': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83} -[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47, 39.36, 38.28, 38.75, 39.51, 38.42, 38.7, 38.49, 38.22, 39.26, 38.62] -695.4200000000001 -34.771 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, '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': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83, 'J_1KI': 252.43983126020004, 'W_1KI': 19.175648121176813, 'W_D': 31.058999999999997, 'J_D': 408.8794636595249, 'W_D_1KI': 9.047189047480337, 'J_D_1KI': 2.635359466204584} +[40.47, 38.86, 38.85, 38.76, 39.25, 39.43, 38.81, 39.05, 39.27, 38.75] +[66.12] +12.901074171066284 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3356, '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': 10.099922180175781, 'TIME_S_1KI': 3.0095119726387907, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.0190241909028, 'W': 66.12} +[40.47, 38.86, 38.85, 38.76, 39.25, 39.43, 38.81, 39.05, 39.27, 38.75, 39.77, 38.78, 39.49, 38.75, 39.19, 40.2, 39.76, 39.63, 39.15, 38.95] +706.2 +35.31 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3356, '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': 10.099922180175781, 'TIME_S_1KI': 3.0095119726387907, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.0190241909028, 'W': 66.12, 'J_1KI': 254.17730160634767, 'W_1KI': 19.702026221692492, 'W_D': 30.810000000000002, 'J_D': 397.48209521055225, 'W_D_1KI': 9.180572109654351, 'J_D_1KI': 2.7355697585382455} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json index 7b79af7..088a303 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1403, "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": 10.47270679473877, "TIME_S_1KI": 7.4645094759364, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1076.08722530365, "W": 76.53000000000002, "J_1KI": 766.9901819698147, "W_1KI": 54.54739843193158, "W_D": 40.18550000000002, "J_D": 565.0477354297641, "W_D_1KI": 28.642551674982194, "J_D_1KI": 20.415218585161934} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1365, "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": 10.184973239898682, "TIME_S_1KI": 7.461518857068632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1064.5539513111116, "W": 77.18, "J_1KI": 779.8930046235249, "W_1KI": 56.54212454212455, "W_D": 41.312500000000014, "J_D": 569.8287783563139, "W_D_1KI": 30.265567765567777, "J_D_1KI": 22.17257711763207} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output index f00e48c..f7108c2 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 7.480671405792236} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 0.7687945365905762} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.5771, 0.6006, 0.1014, ..., 0.3420, 0.9665, 0.9706]) +tensor([0.2036, 0.4469, 0.2777, ..., 0.0070, 0.1510, 0.0548]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 7.480671405792236 seconds +Time: 0.7687945365905762 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1403', '-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": 10.47270679473877} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1365', '-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": 10.184973239898682} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9737, 0.1599, 0.8628, ..., 0.5469, 0.5754, 0.2289]) +tensor([0.2030, 0.8570, 0.3291, ..., 0.5380, 0.6598, 0.8012]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -42,7 +42,7 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.47270679473877 seconds +Time: 10.184973239898682 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9737, 0.1599, 0.8628, ..., 0.5469, 0.5754, 0.2289]) +tensor([0.2030, 0.8570, 0.3291, ..., 0.5380, 0.6598, 0.8012]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -62,13 +62,13 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.47270679473877 seconds +Time: 10.184973239898682 seconds -[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92] -[76.53] -14.060985565185547 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, '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': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002} -[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92, 39.54, 38.42, 38.88, 38.56, 45.29, 38.95, 39.28, 38.53, 39.92, 38.52] -726.89 -36.3445 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, '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': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002, 'J_1KI': 766.9901819698147, 'W_1KI': 54.54739843193158, 'W_D': 40.18550000000002, 'J_D': 565.0477354297641, 'W_D_1KI': 28.642551674982194, 'J_D_1KI': 20.415218585161934} +[41.34, 38.86, 39.46, 39.49, 39.27, 38.84, 38.92, 44.27, 38.84, 38.82] +[77.18] +13.793132305145264 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1365, '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': 10.184973239898682, 'TIME_S_1KI': 7.461518857068632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1064.5539513111116, 'W': 77.18} +[41.34, 38.86, 39.46, 39.49, 39.27, 38.84, 38.92, 44.27, 38.84, 38.82, 40.0, 39.23, 39.62, 44.49, 39.36, 39.18, 39.52, 38.83, 39.69, 38.8] +717.3499999999999 +35.86749999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1365, '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': 10.184973239898682, 'TIME_S_1KI': 7.461518857068632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1064.5539513111116, 'W': 77.18, 'J_1KI': 779.8930046235249, 'W_1KI': 56.54212454212455, 'W_D': 41.312500000000014, 'J_D': 569.8287783563139, 'W_D_1KI': 30.265567765567777, 'J_D_1KI': 22.17257711763207} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json index 8c1f364..090444c 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2778, "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": 10.518349885940552, "TIME_S_1KI": 3.786303054694223, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 942.0412521362306, "W": 70.4, "J_1KI": 339.1077221512709, "W_1KI": 25.341972642188626, "W_D": 35.57825000000001, "J_D": 476.0820906081797, "W_D_1KI": 12.807145428365734, "J_D_1KI": 4.61020353792863} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2670, "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": 10.004925012588501, "TIME_S_1KI": 3.747162926063109, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 905.6854737257956, "W": 69.71, "J_1KI": 339.20804259393094, "W_1KI": 26.108614232209735, "W_D": 34.362249999999996, "J_D": 446.44083588486905, "W_D_1KI": 12.869756554307115, "J_D_1KI": 4.820133540938994} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output index c39c36c..503b8bf 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.778977870941162} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 0.39322471618652344} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3385, 0.4156, 0.4762, ..., 0.6246, 0.7256, 0.2909]) +tensor([0.1004, 0.1366, 0.1464, ..., 0.8763, 0.0205, 0.7475]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 3.778977870941162 seconds +Time: 0.39322471618652344 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2778', '-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": 10.518349885940552} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2670', '-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": 10.004925012588501} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.1706, 0.4199, 0.8169, ..., 0.9237, 0.2859, 0.4340]) +tensor([0.5757, 0.5449, 0.2932, ..., 0.7612, 0.6570, 0.6018]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -40,7 +40,7 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.518349885940552 seconds +Time: 10.004925012588501 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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 +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([0.1706, 0.4199, 0.8169, ..., 0.9237, 0.2859, 0.4340]) +tensor([0.5757, 0.5449, 0.2932, ..., 0.7612, 0.6570, 0.6018]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -59,13 +59,13 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.518349885940552 seconds +Time: 10.004925012588501 seconds -[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56] -[70.4] -13.381267786026001 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, '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': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4} -[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56, 40.19, 38.52, 38.44, 38.38, 38.77, 38.32, 38.58, 39.02, 38.41, 38.83] -696.435 -34.821749999999994 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, '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': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4, 'J_1KI': 339.1077221512709, 'W_1KI': 25.341972642188626, 'W_D': 35.57825000000001, 'J_D': 476.0820906081797, 'W_D_1KI': 12.807145428365734, 'J_D_1KI': 4.61020353792863} +[39.49, 38.86, 39.03, 38.89, 39.33, 39.23, 39.8, 38.83, 40.47, 41.29] +[69.71] +12.992188692092896 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2670, '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': 10.004925012588501, 'TIME_S_1KI': 3.747162926063109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 905.6854737257956, 'W': 69.71} +[39.49, 38.86, 39.03, 38.89, 39.33, 39.23, 39.8, 38.83, 40.47, 41.29, 40.69, 39.0, 38.86, 39.28, 38.91, 39.09, 38.85, 38.87, 39.28, 39.28] +706.9549999999999 +35.34775 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2670, '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': 10.004925012588501, 'TIME_S_1KI': 3.747162926063109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 905.6854737257956, 'W': 69.71, 'J_1KI': 339.20804259393094, 'W_1KI': 26.108614232209735, 'W_D': 34.362249999999996, 'J_D': 446.44083588486905, 'W_D_1KI': 12.869756554307115, 'J_D_1KI': 4.820133540938994} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.json index ea20071..d36d069 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362, "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": 10.810595989227295, "TIME_S_1KI": 29.863524832119598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1183.753153848648, "W": 76.66, "J_1KI": 3270.036336598475, "W_1KI": 211.76795580110496, "W_D": 41.3065, "J_D": 637.8385031235218, "W_D_1KI": 114.10635359116023, "J_D_1KI": 315.21092152254204} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 355, "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": 10.373745918273926, "TIME_S_1KI": 29.221819488095566, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1160.6874260902405, "W": 77.13, "J_1KI": 3269.542045324621, "W_1KI": 217.2676056338028, "W_D": 41.695249999999994, "J_D": 627.4491430401802, "W_D_1KI": 117.4514084507042, "J_D_1KI": 330.8490378893076} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.output index 291751e..5cc406b 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_msdoor.output @@ -1,5 +1,5 @@ ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] -{"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": 2.8945302963256836} +{"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": 2.952639102935791} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0122, 0.2882, 0.1159, ..., 0.4750, 0.0340, 0.6603]) +tensor([0.2865, 0.7562, 0.9509, ..., 0.3118, 0.3729, 0.3715]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 2.8945302963256836 seconds +Time: 2.952639102935791 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '362', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] -{"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": 10.810595989227295} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '355', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 10.373745918273926} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2571, 0.3344, 0.1194, ..., 0.8187, 0.7037, 0.4726]) +tensor([0.5382, 0.6312, 0.7540, ..., 0.2627, 0.1266, 0.9909]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -42,7 +42,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.810595989227295 seconds +Time: 10.373745918273926 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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, 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([0.2571, 0.3344, 0.1194, ..., 0.8187, 0.7037, 0.4726]) +tensor([0.5382, 0.6312, 0.7540, ..., 0.2627, 0.1266, 0.9909]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -62,13 +62,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.810595989227295 seconds +Time: 10.373745918273926 seconds -[41.31, 42.41, 38.93, 38.76, 38.75, 38.61, 39.07, 40.32, 39.22, 39.05] -[76.66] -15.441601276397705 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362, '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': 10.810595989227295, 'TIME_S_1KI': 29.863524832119598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.753153848648, 'W': 76.66} -[41.31, 42.41, 38.93, 38.76, 38.75, 38.61, 39.07, 40.32, 39.22, 39.05, 39.97, 39.08, 38.84, 38.82, 38.78, 38.68, 38.9, 38.82, 39.3, 39.23] -707.0699999999999 -35.3535 -{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362, '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': 10.810595989227295, 'TIME_S_1KI': 29.863524832119598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.753153848648, 'W': 76.66, 'J_1KI': 3270.036336598475, 'W_1KI': 211.76795580110496, 'W_D': 41.3065, 'J_D': 637.8385031235218, 'W_D_1KI': 114.10635359116023, 'J_D_1KI': 315.21092152254204} +[39.38, 39.25, 38.86, 38.69, 44.07, 38.72, 39.34, 39.13, 39.38, 38.95] +[77.13] +15.048456192016602 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 355, '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': 10.373745918273926, 'TIME_S_1KI': 29.221819488095566, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.6874260902405, 'W': 77.13} +[39.38, 39.25, 38.86, 38.69, 44.07, 38.72, 39.34, 39.13, 39.38, 38.95, 39.37, 38.87, 38.89, 39.16, 39.59, 39.16, 39.32, 39.15, 38.78, 38.97] +708.695 +35.43475 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 355, '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': 10.373745918273926, 'TIME_S_1KI': 29.221819488095566, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.6874260902405, 'W': 77.13, 'J_1KI': 3269.542045324621, 'W_1KI': 217.2676056338028, 'W_D': 41.695249999999994, 'J_D': 627.4491430401802, 'W_D_1KI': 117.4514084507042, 'J_D_1KI': 330.8490378893076} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json index 48065b0..bf10bf9 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 1, "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": 20.74174404144287, "TIME_S_1KI": 20.74174404144287, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1964.3499762821195, "W": 78.38, "J_1KI": 1964.3499762821195, "W_1KI": 78.38, "W_D": 43.477, "J_D": 1089.6152579588888, "W_D_1KI": 43.477, "J_D_1KI": 43.477} +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 502, "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": 10.82210373878479, "TIME_S_1KI": 21.557975575268507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1149.8520458102225, "W": 77.57, "J_1KI": 2290.5419239247462, "W_1KI": 154.5219123505976, "W_D": 41.91624999999999, "J_D": 621.3418308004735, "W_D_1KI": 83.4985059760956, "J_D_1KI": 166.3316852113458} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output index 7a3f429..5a6bd47 100644 --- a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] -{"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": 20.74174404144287} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 2.0887391567230225} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3044, 0.7914, 0.5459, ..., 0.2990, 0.3126, 0.6970]) +tensor([0.8930, 0.0340, 0.0432, ..., 0.4641, 0.5881, 0.0340]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 20.74174404144287 seconds +Time: 2.0887391567230225 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '502', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 10.82210373878479} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) 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([0.3044, 0.7914, 0.5459, ..., 0.2990, 0.3126, 0.6970]) +tensor([0.7184, 0.5462, 0.5814, ..., 0.1401, 0.5638, 0.4110]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -39,13 +42,33 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 20.74174404144287 seconds +Time: 10.82210373878479 seconds -[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49] -[78.38] -25.061877727508545 -{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38} -[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49, 39.91, 38.51, 38.34, 38.55, 38.51, 38.42, 38.42, 38.4, 38.39, 38.48] -698.06 -34.903 -{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38, 'J_1KI': 1964.3499762821195, 'W_1KI': 78.38, 'W_D': 43.477, 'J_D': 1089.6152579588888, 'W_D_1KI': 43.477, 'J_D_1KI': 43.477} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7184, 0.5462, 0.5814, ..., 0.1401, 0.5638, 0.4110]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.82210373878479 seconds + +[39.57, 38.73, 38.96, 38.8, 39.06, 38.73, 38.9, 39.18, 40.12, 38.69] +[77.57] +14.823411703109741 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 502, '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': 10.82210373878479, 'TIME_S_1KI': 21.557975575268507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1149.8520458102225, 'W': 77.57} +[39.57, 38.73, 38.96, 38.8, 39.06, 38.73, 38.9, 39.18, 40.12, 38.69, 39.45, 38.87, 39.11, 39.12, 38.98, 39.5, 39.73, 39.35, 39.18, 55.8] +713.075 +35.65375 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 502, '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': 10.82210373878479, 'TIME_S_1KI': 21.557975575268507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1149.8520458102225, 'W': 77.57, 'J_1KI': 2290.5419239247462, 'W_1KI': 154.5219123505976, 'W_D': 41.91624999999999, 'J_D': 621.3418308004735, 'W_D_1KI': 83.4985059760956, 'J_D_1KI': 166.3316852113458} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json index d26e6d4..3b63197 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "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": 12.424208641052246, "TIME_S_1KI": 12.424208641052246, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 879.7217792987824, "W": 52.38, "J_1KI": 879.7217792987824, "W_1KI": 52.38, "W_D": 36.156000000000006, "J_D": 607.2397986316682, "W_D_1KI": 36.156000000000006, "J_D_1KI": 36.156000000000006} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 820, "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": 10.18184781074524, "TIME_S_1KI": 12.41688757407956, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 788.3529852104186, "W": 53.97, "J_1KI": 961.4060795249007, "W_1KI": 65.8170731707317, "W_D": 36.9555, "J_D": 539.8180238084793, "W_D_1KI": 45.06768292682927, "J_D_1KI": 54.96058893515765} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output index 3f99e16..80f920d 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 12.424208641052246} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/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": 1.278935194015503} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6069, 0.8095, 0.9925, ..., 0.5957, 0.3239, 0.4137]) +tensor([0.3655, 0.1574, 0.0876, ..., 0.3684, 0.8305, 0.4002]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -18,7 +18,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 12.424208641052246 seconds +Time: 1.278935194015503 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '820', '-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": 10.18184781074524} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) 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([0.6069, 0.8095, 0.9925, ..., 0.5957, 0.3239, 0.4137]) +tensor([0.3819, 0.2931, 0.0632, ..., 0.0380, 0.4030, 0.3119]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -37,13 +40,32 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 12.424208641052246 seconds +Time: 10.18184781074524 seconds -[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01] -[52.38] -16.79499387741089 -{'CPU': 'Xeon 4216', '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': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38} -[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01, 18.12, 18.25, 18.06, 18.22, 18.08, 17.89, 17.8, 17.75, 19.11, 17.94] -324.48 -16.224 -{'CPU': 'Xeon 4216', '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': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38, 'J_1KI': 879.7217792987824, 'W_1KI': 52.38, 'W_D': 36.156000000000006, 'J_D': 607.2397986316682, 'W_D_1KI': 36.156000000000006, 'J_D_1KI': 36.156000000000006} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3819, 0.2931, 0.0632, ..., 0.0380, 0.4030, 0.3119]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.18184781074524 seconds + +[18.84, 18.5, 18.58, 19.43, 21.6, 18.48, 18.79, 19.02, 18.54, 18.42] +[53.97] +14.607244491577148 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 820, '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': 10.18184781074524, 'TIME_S_1KI': 12.41688757407956, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.3529852104186, 'W': 53.97} +[18.84, 18.5, 18.58, 19.43, 21.6, 18.48, 18.79, 19.02, 18.54, 18.42, 19.56, 18.42, 19.19, 18.66, 18.71, 18.56, 18.5, 18.94, 18.71, 18.5] +340.28999999999996 +17.014499999999998 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 820, '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': 10.18184781074524, 'TIME_S_1KI': 12.41688757407956, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.3529852104186, 'W': 53.97, 'J_1KI': 961.4060795249007, 'W_1KI': 65.8170731707317, 'W_D': 36.9555, 'J_D': 539.8180238084793, 'W_D_1KI': 45.06768292682927, 'J_D_1KI': 54.96058893515765} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json index 2466ba0..bae7907 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1567, "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": 10.482280731201172, "TIME_S_1KI": 6.6893942126363575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 809.832340335846, "W": 53.88, "J_1KI": 516.8043014268321, "W_1KI": 34.38417358008934, "W_D": 37.52475, "J_D": 564.0080941540002, "W_D_1KI": 23.94687300574346, "J_D_1KI": 15.2819866022613} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1545, "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": 10.512819290161133, "TIME_S_1KI": 6.804413780039568, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 819.1745208263397, "W": 55.35, "J_1KI": 530.2100458422912, "W_1KI": 35.8252427184466, "W_D": 37.771, "J_D": 559.0070609960557, "W_D_1KI": 24.44724919093851, "J_D_1KI": 15.823462259507128} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output index a8b8852..8ecee2a 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.698031663894653} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/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": 0.7233202457427979} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6880, 0.8256, 0.6674, ..., 0.8572, 0.2017, 0.9423]) +tensor([0.8020, 0.1387, 0.7798, ..., 0.5974, 0.0348, 0.0014]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 6.698031663894653 seconds +Time: 0.7233202457427979 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1567', '-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": 10.482280731201172} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1451', '-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": 9.855806350708008} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9695, 0.5429, 0.1111, ..., 0.2474, 0.2323, 0.6789]) +tensor([0.8302, 0.3070, 0.4546, ..., 0.0736, 0.6420, 0.8757]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -42,7 +42,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.482280731201172 seconds +Time: 9.855806350708008 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1545', '-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": 10.512819290161133} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9695, 0.5429, 0.1111, ..., 0.2474, 0.2323, 0.6789]) +tensor([0.4173, 0.3562, 0.7536, ..., 0.2369, 0.7055, 0.2115]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -62,13 +65,33 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.482280731201172 seconds +Time: 10.512819290161133 seconds -[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9] -[53.88] -15.030295848846436 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, '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': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88} -[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9, 18.29, 18.08, 17.89, 18.0, 17.96, 17.67, 18.29, 17.73, 17.87, 17.69] -327.105 -16.35525 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, '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': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88, 'J_1KI': 516.8043014268321, 'W_1KI': 34.38417358008934, 'W_D': 37.52475, 'J_D': 564.0080941540002, 'W_D_1KI': 23.94687300574346, 'J_D_1KI': 15.2819866022613} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4173, 0.3562, 0.7536, ..., 0.2369, 0.7055, 0.2115]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.512819290161133 seconds + +[30.77, 19.7, 19.13, 18.49, 18.57, 18.43, 19.23, 22.01, 19.47, 18.55] +[55.35] +14.799901008605957 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1545, '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': 10.512819290161133, 'TIME_S_1KI': 6.804413780039568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 819.1745208263397, 'W': 55.35} +[30.77, 19.7, 19.13, 18.49, 18.57, 18.43, 19.23, 22.01, 19.47, 18.55, 19.01, 18.24, 22.78, 19.39, 18.33, 18.9, 18.54, 18.37, 18.49, 18.69] +351.58000000000004 +17.579 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1545, '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': 10.512819290161133, 'TIME_S_1KI': 6.804413780039568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 819.1745208263397, 'W': 55.35, 'J_1KI': 530.2100458422912, 'W_1KI': 35.8252427184466, 'W_D': 37.771, 'J_D': 559.0070609960557, 'W_D_1KI': 24.44724919093851, 'J_D_1KI': 15.823462259507128} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json index 9d30cd5..ece163f 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1711, "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": 10.39021348953247, "TIME_S_1KI": 6.072597013169182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.5677815818786, "W": 52.04, "J_1KI": 443.34762219864325, "W_1KI": 30.414962010520163, "W_D": 35.8595, "J_D": 522.710633428812, "W_D_1KI": 20.95821157218001, "J_D_1KI": 12.249100860420812} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1673, "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": 10.127207279205322, "TIME_S_1KI": 6.053321744892601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 764.4165148162842, "W": 53.92, "J_1KI": 456.9136370689087, "W_1KI": 32.229527794381355, "W_D": 25.34025, "J_D": 359.2452817057371, "W_D_1KI": 15.146592946802153, "J_D_1KI": 9.053552269457354} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output index 7a78a51..4b778d5 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.135177135467529} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/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": 0.6275899410247803} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0547, 0.0947, 0.9321, ..., 0.9094, 0.0107, 0.8738]) +tensor([0.1758, 0.6208, 0.0545, ..., 0.3721, 0.3880, 0.9549]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 6.135177135467529 seconds +Time: 0.6275899410247803 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1711', '-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": 10.39021348953247} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1673', '-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": 10.127207279205322} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6001, 0.7097, 0.4908, ..., 0.7271, 0.7976, 0.2970]) +tensor([0.4380, 0.5239, 0.9550, ..., 0.2942, 0.2191, 0.4280]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -40,7 +40,7 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.39021348953247 seconds +Time: 10.127207279205322 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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 +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([0.6001, 0.7097, 0.4908, ..., 0.7271, 0.7976, 0.2970]) +tensor([0.4380, 0.5239, 0.9550, ..., 0.2942, 0.2191, 0.4280]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -59,13 +59,13 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.39021348953247 seconds +Time: 10.127207279205322 seconds -[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07] -[52.04] -14.576629161834717 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, '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': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04} -[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07, 18.72, 17.93, 17.72, 18.0, 18.39, 17.97, 18.72, 17.86, 18.44, 17.78] -323.60999999999996 -16.1805 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, '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': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04, 'J_1KI': 443.34762219864325, 'W_1KI': 30.414962010520163, 'W_D': 35.8595, 'J_D': 522.710633428812, 'W_D_1KI': 20.95821157218001, 'J_D_1KI': 12.249100860420812} +[18.86, 18.47, 18.64, 18.55, 18.47, 18.2, 22.86, 18.61, 18.66, 19.25] +[53.92] +14.17686414718628 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1673, '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': 10.127207279205322, 'TIME_S_1KI': 6.053321744892601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 764.4165148162842, 'W': 53.92} +[18.86, 18.47, 18.64, 18.55, 18.47, 18.2, 22.86, 18.61, 18.66, 19.25, 51.45, 43.46, 41.55, 44.68, 49.47, 43.65, 43.04, 43.66, 43.61, 42.47] +571.595 +28.57975 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1673, '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': 10.127207279205322, 'TIME_S_1KI': 6.053321744892601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 764.4165148162842, 'W': 53.92, 'J_1KI': 456.9136370689087, 'W_1KI': 32.229527794381355, 'W_D': 25.34025, 'J_D': 359.2452817057371, 'W_D_1KI': 15.146592946802153, 'J_D_1KI': 9.053552269457354} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json index bc13ead..d15be7c 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "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": 14.3206205368042, "TIME_S_1KI": 14.3206205368042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1077.2933460760116, "W": 53.62, "J_1KI": 1077.2933460760116, "W_1KI": 53.62, "W_D": 37.37875, "J_D": 750.9861741819977, "W_D_1KI": 37.37875, "J_D_1KI": 37.37875} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 719, "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": 10.343138456344604, "TIME_S_1KI": 14.385449869742148, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 878.6838740038871, "W": 55.07, "J_1KI": 1222.0916189205661, "W_1KI": 76.59248956884562, "W_D": 38.10575, "J_D": 608.0063198079467, "W_D_1KI": 52.998261474269825, "J_D_1KI": 73.71107298229461} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output index ba23280..a902ba3 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 14.3206205368042} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/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": 1.4596614837646484} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4405, 0.4136, 0.9296, ..., 0.1477, 0.1453, 0.8762]) +tensor([0.7281, 0.5422, 0.4686, ..., 0.1025, 0.2298, 0.5923]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 14.3206205368042 seconds +Time: 1.4596614837646484 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '719', '-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": 10.343138456344604} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) 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([0.4405, 0.4136, 0.9296, ..., 0.1477, 0.1453, 0.8762]) +tensor([0.0847, 0.2368, 0.8507, ..., 0.8802, 0.2552, 0.0209]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -39,13 +42,33 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 14.3206205368042 seconds +Time: 10.343138456344604 seconds -[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76] -[53.62] -20.091259717941284 -{'CPU': 'Xeon 4216', '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': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62} -[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76, 18.27, 17.65, 17.53, 17.72, 17.84, 17.65, 17.87, 17.77, 17.96, 17.61] -324.825 -16.24125 -{'CPU': 'Xeon 4216', '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': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62, 'J_1KI': 1077.2933460760116, 'W_1KI': 53.62, 'W_D': 37.37875, 'J_D': 750.9861741819977, 'W_D_1KI': 37.37875, 'J_D_1KI': 37.37875} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0847, 0.2368, 0.8507, ..., 0.8802, 0.2552, 0.0209]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.343138456344604 seconds + +[18.92, 18.32, 18.55, 18.52, 18.87, 18.41, 18.45, 18.32, 18.9, 18.37] +[55.07] +15.955763101577759 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 719, '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': 10.343138456344604, 'TIME_S_1KI': 14.385449869742148, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.6838740038871, 'W': 55.07} +[18.92, 18.32, 18.55, 18.52, 18.87, 18.41, 18.45, 18.32, 18.9, 18.37, 19.29, 19.89, 18.65, 19.69, 18.74, 18.5, 18.48, 18.44, 18.81, 22.91] +339.28499999999997 +16.96425 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 719, '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': 10.343138456344604, 'TIME_S_1KI': 14.385449869742148, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.6838740038871, 'W': 55.07, 'J_1KI': 1222.0916189205661, 'W_1KI': 76.59248956884562, 'W_D': 38.10575, 'J_D': 608.0063198079467, 'W_D_1KI': 52.998261474269825, 'J_D_1KI': 73.71107298229461} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json index f7e4602..251aef3 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1598, "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": 10.48720407485962, "TIME_S_1KI": 6.562705929198761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 768.8178126811981, "W": 53.0, "J_1KI": 481.1125235802241, "W_1KI": 33.16645807259074, "W_D": 36.777, "J_D": 533.4870320184231, "W_D_1KI": 23.01439299123905, "J_D_1KI": 14.40199811717087} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1602, "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": 10.24695611000061, "TIME_S_1KI": 6.396352128589644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 809.632248544693, "W": 54.6, "J_1KI": 505.3884198156635, "W_1KI": 34.082397003745314, "W_D": 37.800250000000005, "J_D": 560.5183407152296, "W_D_1KI": 23.595661672908868, "J_D_1KI": 14.728877448757096} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output index 323fb21..66ee10e 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.568377256393433} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/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": 0.6552350521087646} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.1410, 0.8504, 0.4141, ..., 0.6370, 0.5152, 0.1646]) +tensor([0.8970, 0.9682, 0.7696, ..., 0.0273, 0.3187, 0.2626]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 6.568377256393433 seconds +Time: 0.6552350521087646 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1598', '-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": 10.48720407485962} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1602', '-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": 10.24695611000061} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9888, 0.3844, 0.2800, ..., 0.8268, 0.5179, 0.1169]) +tensor([0.2925, 0.1387, 0.2315, ..., 0.8108, 0.0246, 0.8819]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -40,7 +40,7 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.48720407485962 seconds +Time: 10.24695611000061 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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 +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([0.9888, 0.3844, 0.2800, ..., 0.8268, 0.5179, 0.1169]) +tensor([0.2925, 0.1387, 0.2315, ..., 0.8108, 0.0246, 0.8819]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -59,13 +59,13 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.48720407485962 seconds +Time: 10.24695611000061 seconds -[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72] -[53.0] -14.505996465682983 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, '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': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0} -[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72, 18.42, 18.18, 17.97, 17.99, 17.82, 17.72, 17.94, 18.12, 18.4, 18.76] -324.4599999999999 -16.222999999999995 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, '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': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0, 'J_1KI': 481.1125235802241, 'W_1KI': 33.16645807259074, 'W_D': 36.777, 'J_D': 533.4870320184231, 'W_D_1KI': 23.01439299123905, 'J_D_1KI': 14.40199811717087} +[19.16, 18.46, 19.06, 18.45, 18.37, 18.56, 18.55, 18.63, 18.58, 18.45] +[54.6] +14.828429460525513 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1602, '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': 10.24695611000061, 'TIME_S_1KI': 6.396352128589644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.632248544693, 'W': 54.6} +[19.16, 18.46, 19.06, 18.45, 18.37, 18.56, 18.55, 18.63, 18.58, 18.45, 19.21, 18.35, 18.69, 18.68, 18.78, 18.89, 18.6, 18.75, 18.88, 18.61] +335.995 +16.79975 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1602, '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': 10.24695611000061, 'TIME_S_1KI': 6.396352128589644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.632248544693, 'W': 54.6, 'J_1KI': 505.3884198156635, 'W_1KI': 34.082397003745314, 'W_D': 37.800250000000005, 'J_D': 560.5183407152296, 'W_D_1KI': 23.595661672908868, 'J_D_1KI': 14.728877448757096} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.json index cf5b335..c29e256 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 181, "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": 10.025615215301514, "TIME_S_1KI": 55.390139311058086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1684.3236584544181, "W": 50.05, "J_1KI": 9305.655571571371, "W_1KI": 276.5193370165745, "W_D": 32.91325, "J_D": 1107.6236893431544, "W_D_1KI": 181.84116022099448, "J_D_1KI": 1004.6472940386435} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 187, "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": 10.411778688430786, "TIME_S_1KI": 55.67796090069939, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1670.0780740284918, "W": 49.91, "J_1KI": 8930.898791596213, "W_1KI": 266.89839572192517, "W_D": 32.8435, "J_D": 1099.002388786912, "W_D_1KI": 175.6336898395722, "J_D_1KI": 939.217592724985} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.output index a2042c2..558de62 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_msdoor.output @@ -1,5 +1,5 @@ ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] -{"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": 5.796452283859253} +{"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": 5.608172655105591} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.5016, 0.6412, 0.4729, ..., 0.2262, 0.2092, 0.6154]) +tensor([0.6499, 0.8019, 0.4553, ..., 0.2323, 0.1803, 0.4732]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 5.796452283859253 seconds +Time: 5.608172655105591 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '181', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] -{"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": 10.025615215301514} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '187', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 10.411778688430786} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6635, 0.3125, 0.6322, ..., 0.2814, 0.2894, 0.9983]) +tensor([0.9311, 0.0046, 0.4885, ..., 0.6365, 0.9324, 0.0600]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -42,7 +42,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.025615215301514 seconds +Time: 10.411778688430786 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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, 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([0.6635, 0.3125, 0.6322, ..., 0.2814, 0.2894, 0.9983]) +tensor([0.9311, 0.0046, 0.4885, ..., 0.6365, 0.9324, 0.0600]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -62,13 +62,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.025615215301514 seconds +Time: 10.411778688430786 seconds -[20.79, 18.75, 18.84, 18.78, 18.62, 18.79, 22.54, 18.49, 18.38, 20.09] -[50.05] -33.652820348739624 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 181, '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': 10.025615215301514, 'TIME_S_1KI': 55.390139311058086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1684.3236584544181, 'W': 50.05} -[20.79, 18.75, 18.84, 18.78, 18.62, 18.79, 22.54, 18.49, 18.38, 20.09, 18.86, 18.34, 18.49, 18.63, 18.54, 19.75, 19.38, 18.65, 18.68, 18.43] -342.735 -17.13675 -{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 181, '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': 10.025615215301514, 'TIME_S_1KI': 55.390139311058086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1684.3236584544181, 'W': 50.05, 'J_1KI': 9305.655571571371, 'W_1KI': 276.5193370165745, 'W_D': 32.91325, 'J_D': 1107.6236893431544, 'W_D_1KI': 181.84116022099448, 'J_D_1KI': 1004.6472940386435} +[19.33, 19.31, 18.89, 19.77, 18.83, 18.72, 18.52, 18.55, 18.79, 22.98] +[49.91] +33.46179270744324 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 187, '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': 10.411778688430786, 'TIME_S_1KI': 55.67796090069939, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.0780740284918, 'W': 49.91} +[19.33, 19.31, 18.89, 19.77, 18.83, 18.72, 18.52, 18.55, 18.79, 22.98, 19.06, 18.76, 18.78, 18.28, 18.52, 18.3, 19.02, 18.46, 19.75, 18.79] +341.33 +17.066499999999998 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 187, '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': 10.411778688430786, 'TIME_S_1KI': 55.67796090069939, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.0780740284918, 'W': 49.91, 'J_1KI': 8930.898791596213, 'W_1KI': 266.89839572192517, 'W_D': 32.8435, 'J_D': 1099.002388786912, 'W_D_1KI': 175.6336898395722, 'J_D_1KI': 939.217592724985} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json index 54da32a..d010e7f 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 1, "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": 37.58896088600159, "TIME_S_1KI": 37.58896088600159, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2465.35528427124, "W": 53.12, "J_1KI": 2465.35528427124, "W_1KI": 53.12, "W_D": 36.98599999999999, "J_D": 1716.5593099408145, "W_D_1KI": 36.98599999999999, "J_D_1KI": 36.98599999999999} +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 274, "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": 10.574675798416138, "TIME_S_1KI": 38.59370729348956, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1042.1511521768568, "W": 53.86999999999999, "J_1KI": 3803.471358309697, "W_1KI": 196.60583941605836, "W_D": 36.99249999999999, "J_D": 715.6446351754664, "W_D_1KI": 135.00912408759123, "J_D_1KI": 492.7340295167563} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output index 7c7841e..544fcef 100644 --- a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] -{"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": 37.58896088600159} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 3.8308138847351074} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9581, 0.4048, 0.0262, ..., 0.9819, 0.7450, 0.5527]) +tensor([0.5832, 0.9113, 0.4756, ..., 0.2850, 0.1622, 0.5534]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 37.58896088600159 seconds +Time: 3.8308138847351074 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '274', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 10.574675798416138} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) 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([0.9581, 0.4048, 0.0262, ..., 0.9819, 0.7450, 0.5527]) +tensor([0.9230, 0.8444, 0.2049, ..., 0.9536, 0.8258, 0.5495]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -39,13 +42,33 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 37.58896088600159 seconds +Time: 10.574675798416138 seconds -[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58] -[53.12] -46.41105580329895 -{'CPU': 'Xeon 4216', 'CORES': 1, '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': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12} -[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58, 18.32, 17.63, 17.7, 18.87, 17.96, 17.88, 17.96, 17.6, 17.83, 17.79] -322.68000000000006 -16.134000000000004 -{'CPU': 'Xeon 4216', 'CORES': 1, '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': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12, 'J_1KI': 2465.35528427124, 'W_1KI': 53.12, 'W_D': 36.98599999999999, 'J_D': 1716.5593099408145, 'W_D_1KI': 36.98599999999999, 'J_D_1KI': 36.98599999999999} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9230, 0.8444, 0.2049, ..., 0.9536, 0.8258, 0.5495]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.574675798416138 seconds + +[18.85, 18.31, 18.74, 18.49, 18.54, 18.36, 18.56, 18.91, 18.5, 22.3] +[53.87] +19.34566831588745 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 274, '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': 10.574675798416138, 'TIME_S_1KI': 38.59370729348956, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.1511521768568, 'W': 53.86999999999999} +[18.85, 18.31, 18.74, 18.49, 18.54, 18.36, 18.56, 18.91, 18.5, 22.3, 19.93, 18.23, 19.41, 18.55, 18.82, 18.83, 18.58, 18.49, 18.57, 18.24] +337.54999999999995 +16.877499999999998 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 274, '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': 10.574675798416138, 'TIME_S_1KI': 38.59370729348956, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.1511521768568, 'W': 53.86999999999999, 'J_1KI': 3803.471358309697, 'W_1KI': 196.60583941605836, 'W_D': 36.99249999999999, 'J_D': 715.6446351754664, 'W_D_1KI': 135.00912408759123, 'J_D_1KI': 492.7340295167563} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..f65875c --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..59f928c --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_amazon0312.output @@ -0,0 +1,49 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0536, 0.3063, 0.9163, ..., 0.7704, 0.9774, 0.8233]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 88.16403460502625 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0536, 0.3063, 0.9163, ..., 0.7704, 0.9774, 0.8233]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 88.16403460502625 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} diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..b96c66c --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..13ebb65 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_helm2d03.output @@ -0,0 +1,51 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.2732, 0.1117, 0.4132, ..., 0.8859, 0.7833, 0.1406]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 62.08789658546448 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.2732, 0.1117, 0.4132, ..., 0.8859, 0.7833, 0.1406]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 62.08789658546448 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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.json new file mode 100644 index 0000000..e4470f0 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.output new file mode 100644 index 0000000..8ed6535 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_language.output @@ -0,0 +1,49 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1808, 0.0389, 0.7706, ..., 0.1715, 0.5157, 0.4224]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 32.617069482803345 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1808, 0.0389, 0.7706, ..., 0.1715, 0.5157, 0.4224]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 32.617069482803345 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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.json new file mode 100644 index 0000000..91cb90d --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.output new file mode 100644 index 0000000..5e3841d --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_marine1.output @@ -0,0 +1,51 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1033, 0.2543, 0.2854, ..., 0.8643, 0.3799, 0.0773]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 136.75263905525208 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1033, 0.2543, 0.2854, ..., 0.8643, 0.3799, 0.0773]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 136.75263905525208 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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.json new file mode 100644 index 0000000..931b93e --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.json @@ -0,0 +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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.output new file mode 100644 index 0000000..103b1d5 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_mario002.output @@ -0,0 +1,49 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0328, 0.3187, 0.7172, ..., 0.3931, 0.0888, 0.1198]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 51.17483377456665 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0328, 0.3187, 0.7172, ..., 0.3931, 0.0888, 0.1198]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 51.17483377456665 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} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..56d1bf6 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "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": 43.16092610359192, "TIME_S_1KI": 431.6092610359192, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.33110124588, "W": 22.848960166729626, "J_1KI": 11893.3110124588, "W_1KI": 228.48960166729626, "W_D": 4.571960166729628, "J_D": 237.97907564592364, "W_D_1KI": 45.71960166729628, "J_D_1KI": 457.19601667296286} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..ab564a0 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_msdoor.output @@ -0,0 +1,51 @@ +['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/msdoor.mtx -c 1'] +{"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": 43.16092610359192} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0523, 0.1527, 0.8721, ..., 0.0519, 0.6042, 0.4109]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 43.16092610359192 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0523, 0.1527, 0.8721, ..., 0.0519, 0.6042, 0.4109]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 43.16092610359192 seconds + +[20.12, 19.92, 20.36, 20.44, 20.2, 20.04, 20.08, 19.84, 20.04, 20.04] +[20.2, 20.44, 20.76, 24.08, 25.44, 27.84, 28.48, 28.32, 27.16, 26.68, 25.2, 25.2, 24.44, 24.64, 24.56, 24.44, 24.4, 24.6, 24.4, 24.4, 24.36, 24.32, 24.28, 24.44, 24.56, 24.44, 24.44, 24.56, 24.52, 24.72, 25.24, 25.12, 25.36, 25.36, 25.56, 25.16, 25.12, 25.12, 24.84, 24.48, 24.44, 24.16, 24.12, 24.36, 24.32, 24.44, 24.28, 24.32, 24.12] +52.05186986923218 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16092610359192, 'TIME_S_1KI': 431.6092610359192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.33110124588, 'W': 22.848960166729626} +[20.12, 19.92, 20.36, 20.44, 20.2, 20.04, 20.08, 19.84, 20.04, 20.04, 20.28, 20.36, 20.48, 20.44, 20.44, 20.6, 20.72, 20.64, 20.48, 20.48] +365.53999999999996 +18.276999999999997 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, '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': 43.16092610359192, 'TIME_S_1KI': 431.6092610359192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.33110124588, 'W': 22.848960166729626, 'J_1KI': 11893.3110124588, 'W_1KI': 228.48960166729626, 'W_D': 4.571960166729628, 'J_D': 237.97907564592364, 'W_D_1KI': 45.71960166729628, 'J_D_1KI': 457.19601667296286} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.json new file mode 100644 index 0000000..4bb27e2 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "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": 283.81978273391724, "TIME_S_1KI": 283.81978273391724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7375.577878112791, "W": 25.373727151984518, "J_1KI": 7375.577878112791, "W_1KI": 25.373727151984518, "W_D": 4.492727151984518, "J_D": 1305.9358129017337, "W_D_1KI": 4.492727151984518, "J_D_1KI": 4.492727151984518} diff --git a/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.output new file mode 100644 index 0000000..c57d8f0 --- /dev/null +++ b/pytorch/output_389000+_1core_old/altra_1_csr_10_10_10_test1.output @@ -0,0 +1,51 @@ +['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/test1.mtx -c 1'] +{"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": 283.81978273391724} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.4069, 0.7437, 0.0499, ..., 0.6858, 0.0232, 0.7224]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 283.81978273391724 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.4069, 0.7437, 0.0499, ..., 0.6858, 0.0232, 0.7224]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 283.81978273391724 seconds + +[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36] +[23.36, 23.0, 23.52, 26.52, 28.0, 29.08, 29.6, 28.4, 27.72, 27.56, 27.4, 26.84, 27.08, 27.28, 27.24, 27.04, 26.76, 26.76, 26.68, 26.48, 26.44, 26.48, 26.36, 26.2, 26.48, 26.48, 26.64, 26.64, 26.68, 26.52, 26.52, 26.68, 26.64, 26.68, 26.68, 26.68, 26.64, 26.64, 26.64, 26.92, 26.84, 26.6, 26.72, 26.68, 26.44, 26.76, 26.96, 26.8, 26.8, 26.96, 27.0, 26.92, 26.96, 26.96, 26.64, 26.52, 26.36, 26.56, 26.6, 26.72, 26.72, 26.72, 26.64, 26.6, 26.52, 26.64, 26.52, 26.48, 26.44, 26.44, 26.48, 26.56, 26.76, 26.64, 26.76, 26.72, 26.76, 26.8, 26.76, 26.64, 26.44, 26.44, 26.44, 26.44, 26.64, 26.56, 26.76, 26.76, 26.68, 26.68, 26.72, 26.72, 26.8, 26.8, 26.64, 26.4, 26.52, 26.56, 26.68, 26.68, 26.96, 26.72, 26.64, 26.76, 26.76, 26.84, 26.76, 26.96, 26.8, 26.76, 26.68, 26.72, 26.8, 27.12, 27.08, 26.92, 26.68, 26.28, 26.2, 26.44, 26.4, 26.68, 26.88, 26.92, 27.08, 26.96, 26.96, 26.88, 26.84, 26.68, 26.64, 26.68, 26.52, 26.48, 26.28, 26.28, 26.24, 26.44, 26.76, 27.04, 26.92, 27.0, 27.0, 27.0, 26.84, 26.92, 26.88, 26.92, 26.92, 26.8, 26.8, 26.84, 26.92, 26.92, 27.08, 26.92, 27.0, 26.92, 26.8, 26.8, 26.96, 26.84, 26.92, 26.84, 26.68, 26.6, 26.68, 26.72, 26.96, 26.76, 26.76, 26.6, 26.4, 26.28, 26.2, 26.44, 26.8, 26.76, 26.92, 27.04, 26.8, 26.6, 26.6, 26.32, 26.24, 26.4, 26.28, 26.36, 26.64, 26.64, 26.6, 26.68, 26.68, 26.44, 26.24, 26.12, 26.36, 26.44, 26.68, 26.8, 26.92, 26.96, 26.92, 26.88, 26.88, 26.76, 26.76, 26.8, 26.68, 26.72, 26.64, 26.52, 26.64, 26.92, 26.92, 26.76, 26.6, 26.6, 26.56, 26.36, 26.36, 26.64, 26.6, 26.72, 26.76, 26.84, 26.72, 27.04, 26.72, 26.6, 26.88, 26.76, 26.72, 26.64, 26.8, 27.04, 27.04, 26.96, 27.04, 27.04, 26.8, 26.68, 26.88, 26.84, 27.0, 26.92, 26.96, 27.0, 26.76, 26.8, 26.88, 26.88, 26.96, 27.0, 27.12, 27.32, 27.32, 27.32, 27.32, 27.24, 26.88, 26.6, 26.68, 26.64, 26.92, 27.08, 27.28, 27.04, 26.92, 27.08, 27.4, 27.56, 27.6, 27.48, 27.2, 26.88] +290.67774844169617 +{'CPU': 'Altra', 'CORES': 1, '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': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518} +[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36, 23.48, 23.28, 23.2, 23.24, 23.24, 23.32, 23.6, 23.8, 23.52, 23.52] +417.62 +20.881 +{'CPU': 'Altra', 'CORES': 1, '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': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518, 'J_1KI': 7375.577878112791, 'W_1KI': 25.373727151984518, 'W_D': 4.492727151984518, 'J_D': 1305.9358129017337, 'W_D_1KI': 4.492727151984518, 'J_D_1KI': 4.492727151984518} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..4f2ba5e --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1665, "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": 10.677687168121338, "TIME_S_1KI": 6.413025326199002, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 984.0334419322014, "W": 72.51, "J_1KI": 591.0110762355564, "W_1KI": 43.54954954954955, "W_D": 37.19775000000001, "J_D": 504.81078423160335, "W_D_1KI": 22.340990990991, "J_D_1KI": 13.418012607201801} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..78545de --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_amazon0312.output @@ -0,0 +1,71 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 6.305053472518921} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6406, 0.1468, 0.7280, ..., 0.0181, 0.2043, 0.6040]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 6.305053472518921 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1665', '-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": 10.677687168121338} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7736, 0.6337, 0.2989, ..., 0.0625, 0.4089, 0.3233]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.677687168121338 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7736, 0.6337, 0.2989, ..., 0.0625, 0.4089, 0.3233]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.677687168121338 seconds + +[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39] +[72.51] +13.571003198623657 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, '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': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51} +[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39, 39.83, 38.29, 40.69, 40.97, 41.34, 39.2, 38.48, 38.73, 38.45, 38.46] +706.2449999999999 +35.31224999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, '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': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51, 'J_1KI': 591.0110762355564, 'W_1KI': 43.54954954954955, 'W_D': 37.19775000000001, 'J_D': 504.81078423160335, 'W_D_1KI': 22.340990990991, 'J_D_1KI': 13.418012607201801} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..6c16e41 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2881, "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": 10.489487886428833, "TIME_S_1KI": 3.640919085882969, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 982.9177148509026, "W": 72.89, "J_1KI": 341.1724105695601, "W_1KI": 25.30024297119056, "W_D": 38.247499999999995, "J_D": 515.765472612977, "W_D_1KI": 13.275772301284276, "J_D_1KI": 4.6080431451871835} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..3aa1656 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_helm2d03.output @@ -0,0 +1,74 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.64394474029541} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2874, 0.3706, 0.3465, ..., 0.0468, 0.1058, 0.2863]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 3.64394474029541 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2881', '-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": 10.489487886428833} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4950, 0.0177, 0.5787, ..., 0.9424, 0.3532, 0.5521]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.489487886428833 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4950, 0.0177, 0.5787, ..., 0.9424, 0.3532, 0.5521]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.489487886428833 seconds + +[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77] +[72.89] +13.484946012496948 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, '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': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89} +[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77, 39.12, 38.21, 38.45, 38.24, 39.01, 38.22, 38.93, 38.26, 38.73, 38.6] +692.8500000000001 +34.642500000000005 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, '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': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89, 'J_1KI': 341.1724105695601, 'W_1KI': 25.30024297119056, 'W_D': 38.247499999999995, 'J_D': 515.765472612977, 'W_D_1KI': 13.275772301284276, 'J_D_1KI': 4.6080431451871835} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.json new file mode 100644 index 0000000..d92fabc --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3433, "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": 10.349842309951782, "TIME_S_1KI": 3.01480987764398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.6259407162667, "W": 65.83, "J_1KI": 252.43983126020004, "W_1KI": 19.175648121176813, "W_D": 31.058999999999997, "J_D": 408.8794636595249, "W_D_1KI": 9.047189047480337, "J_D_1KI": 2.635359466204584} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.output new file mode 100644 index 0000000..994626a --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_language.output @@ -0,0 +1,71 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.058311939239502} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8774, 0.7244, 0.5547, ..., 0.2046, 0.1297, 0.0114]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 3.058311939239502 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '3433', '-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": 10.349842309951782} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8074, 0.3012, 0.9549, ..., 0.2881, 0.3396, 0.4512]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.349842309951782 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8074, 0.3012, 0.9549, ..., 0.2881, 0.3396, 0.4512]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.349842309951782 seconds + +[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47] +[65.83] +13.164604902267456 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, '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': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83} +[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47, 39.36, 38.28, 38.75, 39.51, 38.42, 38.7, 38.49, 38.22, 39.26, 38.62] +695.4200000000001 +34.771 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, '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': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83, 'J_1KI': 252.43983126020004, 'W_1KI': 19.175648121176813, 'W_D': 31.058999999999997, 'J_D': 408.8794636595249, 'W_D_1KI': 9.047189047480337, 'J_D_1KI': 2.635359466204584} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.json new file mode 100644 index 0000000..7b79af7 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1403, "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": 10.47270679473877, "TIME_S_1KI": 7.4645094759364, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1076.08722530365, "W": 76.53000000000002, "J_1KI": 766.9901819698147, "W_1KI": 54.54739843193158, "W_D": 40.18550000000002, "J_D": 565.0477354297641, "W_D_1KI": 28.642551674982194, "J_D_1KI": 20.415218585161934} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.output new file mode 100644 index 0000000..f00e48c --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_marine1.output @@ -0,0 +1,74 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 7.480671405792236} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5771, 0.6006, 0.1014, ..., 0.3420, 0.9665, 0.9706]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 7.480671405792236 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1403', '-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": 10.47270679473877} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9737, 0.1599, 0.8628, ..., 0.5469, 0.5754, 0.2289]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.47270679473877 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9737, 0.1599, 0.8628, ..., 0.5469, 0.5754, 0.2289]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.47270679473877 seconds + +[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92] +[76.53] +14.060985565185547 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, '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': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002} +[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92, 39.54, 38.42, 38.88, 38.56, 45.29, 38.95, 39.28, 38.53, 39.92, 38.52] +726.89 +36.3445 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, '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': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002, 'J_1KI': 766.9901819698147, 'W_1KI': 54.54739843193158, 'W_D': 40.18550000000002, 'J_D': 565.0477354297641, 'W_D_1KI': 28.642551674982194, 'J_D_1KI': 20.415218585161934} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.json new file mode 100644 index 0000000..8c1f364 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2778, "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": 10.518349885940552, "TIME_S_1KI": 3.786303054694223, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 942.0412521362306, "W": 70.4, "J_1KI": 339.1077221512709, "W_1KI": 25.341972642188626, "W_D": 35.57825000000001, "J_D": 476.0820906081797, "W_D_1KI": 12.807145428365734, "J_D_1KI": 4.61020353792863} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.output new file mode 100644 index 0000000..c39c36c --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_mario002.output @@ -0,0 +1,71 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', '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": 3.778977870941162} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3385, 0.4156, 0.4762, ..., 0.6246, 0.7256, 0.2909]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 3.778977870941162 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2778', '-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": 10.518349885940552} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1706, 0.4199, 0.8169, ..., 0.9237, 0.2859, 0.4340]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.518349885940552 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1706, 0.4199, 0.8169, ..., 0.9237, 0.2859, 0.4340]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.518349885940552 seconds + +[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56] +[70.4] +13.381267786026001 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, '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': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4} +[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56, 40.19, 38.52, 38.44, 38.38, 38.77, 38.32, 38.58, 39.02, 38.41, 38.83] +696.435 +34.821749999999994 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, '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': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4, 'J_1KI': 339.1077221512709, 'W_1KI': 25.341972642188626, 'W_D': 35.57825000000001, 'J_D': 476.0820906081797, 'W_D_1KI': 12.807145428365734, 'J_D_1KI': 4.61020353792863} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..ea20071 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362, "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": 10.810595989227295, "TIME_S_1KI": 29.863524832119598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1183.753153848648, "W": 76.66, "J_1KI": 3270.036336598475, "W_1KI": 211.76795580110496, "W_D": 41.3065, "J_D": 637.8385031235218, "W_D_1KI": 114.10635359116023, "J_D_1KI": 315.21092152254204} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..291751e --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_msdoor.output @@ -0,0 +1,74 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 2.8945302963256836} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0122, 0.2882, 0.1159, ..., 0.4750, 0.0340, 0.6603]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 2.8945302963256836 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '362', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 10.810595989227295} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2571, 0.3344, 0.1194, ..., 0.8187, 0.7037, 0.4726]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.810595989227295 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2571, 0.3344, 0.1194, ..., 0.8187, 0.7037, 0.4726]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.810595989227295 seconds + +[41.31, 42.41, 38.93, 38.76, 38.75, 38.61, 39.07, 40.32, 39.22, 39.05] +[76.66] +15.441601276397705 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362, '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': 10.810595989227295, 'TIME_S_1KI': 29.863524832119598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.753153848648, 'W': 76.66} +[41.31, 42.41, 38.93, 38.76, 38.75, 38.61, 39.07, 40.32, 39.22, 39.05, 39.97, 39.08, 38.84, 38.82, 38.78, 38.68, 38.9, 38.82, 39.3, 39.23] +707.0699999999999 +35.3535 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362, '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': 10.810595989227295, 'TIME_S_1KI': 29.863524832119598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.753153848648, 'W': 76.66, 'J_1KI': 3270.036336598475, 'W_1KI': 211.76795580110496, 'W_D': 41.3065, 'J_D': 637.8385031235218, 'W_D_1KI': 114.10635359116023, 'J_D_1KI': 315.21092152254204} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.json new file mode 100644 index 0000000..48065b0 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "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": 20.74174404144287, "TIME_S_1KI": 20.74174404144287, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1964.3499762821195, "W": 78.38, "J_1KI": 1964.3499762821195, "W_1KI": 78.38, "W_D": 43.477, "J_D": 1089.6152579588888, "W_D_1KI": 43.477, "J_D_1KI": 43.477} diff --git a/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.output new file mode 100644 index 0000000..7a3f429 --- /dev/null +++ b/pytorch/output_389000+_1core_old/epyc_7313p_1_csr_10_10_10_test1.output @@ -0,0 +1,51 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 20.74174404144287} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3044, 0.7914, 0.5459, ..., 0.2990, 0.3126, 0.6970]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 20.74174404144287 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3044, 0.7914, 0.5459, ..., 0.2990, 0.3126, 0.6970]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 20.74174404144287 seconds + +[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49] +[78.38] +25.061877727508545 +{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38} +[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49, 39.91, 38.51, 38.34, 38.55, 38.51, 38.42, 38.42, 38.4, 38.39, 38.48] +698.06 +34.903 +{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38, 'J_1KI': 1964.3499762821195, 'W_1KI': 78.38, 'W_D': 43.477, 'J_D': 1089.6152579588888, 'W_D_1KI': 43.477, 'J_D_1KI': 43.477} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..d26e6d4 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "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": 12.424208641052246, "TIME_S_1KI": 12.424208641052246, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 879.7217792987824, "W": 52.38, "J_1KI": 879.7217792987824, "W_1KI": 52.38, "W_D": 36.156000000000006, "J_D": 607.2397986316682, "W_D_1KI": 36.156000000000006, "J_D_1KI": 36.156000000000006} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..3f99e16 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_amazon0312.output @@ -0,0 +1,49 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 12.424208641052246} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6069, 0.8095, 0.9925, ..., 0.5957, 0.3239, 0.4137]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 12.424208641052246 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6069, 0.8095, 0.9925, ..., 0.5957, 0.3239, 0.4137]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 12.424208641052246 seconds + +[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01] +[52.38] +16.79499387741089 +{'CPU': 'Xeon 4216', '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': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38} +[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01, 18.12, 18.25, 18.06, 18.22, 18.08, 17.89, 17.8, 17.75, 19.11, 17.94] +324.48 +16.224 +{'CPU': 'Xeon 4216', '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': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38, 'J_1KI': 879.7217792987824, 'W_1KI': 52.38, 'W_D': 36.156000000000006, 'J_D': 607.2397986316682, 'W_D_1KI': 36.156000000000006, 'J_D_1KI': 36.156000000000006} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..2466ba0 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1567, "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": 10.482280731201172, "TIME_S_1KI": 6.6893942126363575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 809.832340335846, "W": 53.88, "J_1KI": 516.8043014268321, "W_1KI": 34.38417358008934, "W_D": 37.52475, "J_D": 564.0080941540002, "W_D_1KI": 23.94687300574346, "J_D_1KI": 15.2819866022613} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..a8b8852 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_helm2d03.output @@ -0,0 +1,74 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.698031663894653} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6880, 0.8256, 0.6674, ..., 0.8572, 0.2017, 0.9423]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 6.698031663894653 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1567', '-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": 10.482280731201172} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9695, 0.5429, 0.1111, ..., 0.2474, 0.2323, 0.6789]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.482280731201172 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9695, 0.5429, 0.1111, ..., 0.2474, 0.2323, 0.6789]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.482280731201172 seconds + +[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9] +[53.88] +15.030295848846436 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, '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': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88} +[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9, 18.29, 18.08, 17.89, 18.0, 17.96, 17.67, 18.29, 17.73, 17.87, 17.69] +327.105 +16.35525 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, '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': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88, 'J_1KI': 516.8043014268321, 'W_1KI': 34.38417358008934, 'W_D': 37.52475, 'J_D': 564.0080941540002, 'W_D_1KI': 23.94687300574346, 'J_D_1KI': 15.2819866022613} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.json new file mode 100644 index 0000000..9d30cd5 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1711, "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": 10.39021348953247, "TIME_S_1KI": 6.072597013169182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.5677815818786, "W": 52.04, "J_1KI": 443.34762219864325, "W_1KI": 30.414962010520163, "W_D": 35.8595, "J_D": 522.710633428812, "W_D_1KI": 20.95821157218001, "J_D_1KI": 12.249100860420812} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.output new file mode 100644 index 0000000..7a78a51 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_language.output @@ -0,0 +1,71 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.135177135467529} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0547, 0.0947, 0.9321, ..., 0.9094, 0.0107, 0.8738]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 6.135177135467529 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1711', '-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": 10.39021348953247} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6001, 0.7097, 0.4908, ..., 0.7271, 0.7976, 0.2970]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.39021348953247 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6001, 0.7097, 0.4908, ..., 0.7271, 0.7976, 0.2970]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.39021348953247 seconds + +[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07] +[52.04] +14.576629161834717 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, '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': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04} +[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07, 18.72, 17.93, 17.72, 18.0, 18.39, 17.97, 18.72, 17.86, 18.44, 17.78] +323.60999999999996 +16.1805 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, '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': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04, 'J_1KI': 443.34762219864325, 'W_1KI': 30.414962010520163, 'W_D': 35.8595, 'J_D': 522.710633428812, 'W_D_1KI': 20.95821157218001, 'J_D_1KI': 12.249100860420812} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.json new file mode 100644 index 0000000..bc13ead --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "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": 14.3206205368042, "TIME_S_1KI": 14.3206205368042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1077.2933460760116, "W": 53.62, "J_1KI": 1077.2933460760116, "W_1KI": 53.62, "W_D": 37.37875, "J_D": 750.9861741819977, "W_D_1KI": 37.37875, "J_D_1KI": 37.37875} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.output new file mode 100644 index 0000000..ba23280 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_marine1.output @@ -0,0 +1,51 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 14.3206205368042} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4405, 0.4136, 0.9296, ..., 0.1477, 0.1453, 0.8762]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 14.3206205368042 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4405, 0.4136, 0.9296, ..., 0.1477, 0.1453, 0.8762]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 14.3206205368042 seconds + +[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76] +[53.62] +20.091259717941284 +{'CPU': 'Xeon 4216', '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': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62} +[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76, 18.27, 17.65, 17.53, 17.72, 17.84, 17.65, 17.87, 17.77, 17.96, 17.61] +324.825 +16.24125 +{'CPU': 'Xeon 4216', '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': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62, 'J_1KI': 1077.2933460760116, 'W_1KI': 53.62, 'W_D': 37.37875, 'J_D': 750.9861741819977, 'W_D_1KI': 37.37875, 'J_D_1KI': 37.37875} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.json new file mode 100644 index 0000000..f7e4602 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1598, "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": 10.48720407485962, "TIME_S_1KI": 6.562705929198761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 768.8178126811981, "W": 53.0, "J_1KI": 481.1125235802241, "W_1KI": 33.16645807259074, "W_D": 36.777, "J_D": 533.4870320184231, "W_D_1KI": 23.01439299123905, "J_D_1KI": 14.40199811717087} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.output new file mode 100644 index 0000000..323fb21 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_mario002.output @@ -0,0 +1,71 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '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": 6.568377256393433} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1410, 0.8504, 0.4141, ..., 0.6370, 0.5152, 0.1646]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 6.568377256393433 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1598', '-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": 10.48720407485962} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9888, 0.3844, 0.2800, ..., 0.8268, 0.5179, 0.1169]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.48720407485962 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9888, 0.3844, 0.2800, ..., 0.8268, 0.5179, 0.1169]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.48720407485962 seconds + +[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72] +[53.0] +14.505996465682983 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, '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': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0} +[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72, 18.42, 18.18, 17.97, 17.99, 17.82, 17.72, 17.94, 18.12, 18.4, 18.76] +324.4599999999999 +16.222999999999995 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, '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': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0, 'J_1KI': 481.1125235802241, 'W_1KI': 33.16645807259074, 'W_D': 36.777, 'J_D': 533.4870320184231, 'W_D_1KI': 23.01439299123905, 'J_D_1KI': 14.40199811717087} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..cf5b335 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 181, "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": 10.025615215301514, "TIME_S_1KI": 55.390139311058086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1684.3236584544181, "W": 50.05, "J_1KI": 9305.655571571371, "W_1KI": 276.5193370165745, "W_D": 32.91325, "J_D": 1107.6236893431544, "W_D_1KI": 181.84116022099448, "J_D_1KI": 1004.6472940386435} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..a2042c2 --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_msdoor.output @@ -0,0 +1,74 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 5.796452283859253} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5016, 0.6412, 0.4729, ..., 0.2262, 0.2092, 0.6154]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 5.796452283859253 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '181', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"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": 10.025615215301514} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6635, 0.3125, 0.6322, ..., 0.2814, 0.2894, 0.9983]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.025615215301514 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6635, 0.3125, 0.6322, ..., 0.2814, 0.2894, 0.9983]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.025615215301514 seconds + +[20.79, 18.75, 18.84, 18.78, 18.62, 18.79, 22.54, 18.49, 18.38, 20.09] +[50.05] +33.652820348739624 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 181, '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': 10.025615215301514, 'TIME_S_1KI': 55.390139311058086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1684.3236584544181, 'W': 50.05} +[20.79, 18.75, 18.84, 18.78, 18.62, 18.79, 22.54, 18.49, 18.38, 20.09, 18.86, 18.34, 18.49, 18.63, 18.54, 19.75, 19.38, 18.65, 18.68, 18.43] +342.735 +17.13675 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 181, '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': 10.025615215301514, 'TIME_S_1KI': 55.390139311058086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1684.3236584544181, 'W': 50.05, 'J_1KI': 9305.655571571371, 'W_1KI': 276.5193370165745, 'W_D': 32.91325, 'J_D': 1107.6236893431544, 'W_D_1KI': 181.84116022099448, 'J_D_1KI': 1004.6472940386435} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.json new file mode 100644 index 0000000..54da32a --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "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": 37.58896088600159, "TIME_S_1KI": 37.58896088600159, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2465.35528427124, "W": 53.12, "J_1KI": 2465.35528427124, "W_1KI": 53.12, "W_D": 36.98599999999999, "J_D": 1716.5593099408145, "W_D_1KI": 36.98599999999999, "J_D_1KI": 36.98599999999999} diff --git a/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.output new file mode 100644 index 0000000..7c7841e --- /dev/null +++ b/pytorch/output_389000+_1core_old/xeon_4216_1_csr_10_10_10_test1.output @@ -0,0 +1,51 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"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": 37.58896088600159} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9581, 0.4048, 0.0262, ..., 0.9819, 0.7450, 0.5527]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 37.58896088600159 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9581, 0.4048, 0.0262, ..., 0.9819, 0.7450, 0.5527]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 37.58896088600159 seconds + +[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58] +[53.12] +46.41105580329895 +{'CPU': 'Xeon 4216', 'CORES': 1, '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': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12} +[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58, 18.32, 17.63, 17.7, 18.87, 17.96, 17.88, 17.96, 17.6, 17.83, 17.79] +322.68000000000006 +16.134000000000004 +{'CPU': 'Xeon 4216', 'CORES': 1, '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': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12, 'J_1KI': 2465.35528427124, 'W_1KI': 53.12, 'W_D': 36.98599999999999, 'J_D': 1716.5593099408145, 'W_D_1KI': 36.98599999999999, 'J_D_1KI': 36.98599999999999} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json index 5fe67db..1a7c044 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 30.323144912719727, "TIME_S_1KI": 30.323144912719727, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2708.072883968354, "W": 77.40203729318671, "J_1KI": 2708.072883968354, "W_1KI": 77.40203729318671, "W_D": 53.61603729318671, "J_D": 1875.8697033972746, "W_D_1KI": 53.61603729318671, "J_D_1KI": 53.61603729318671} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 313, "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": 10.007118940353394, "TIME_S_1KI": 31.97162600751883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 689.5519872760772, "W": 51.509547141423724, "J_1KI": 2203.0414928948153, "W_1KI": 164.56724326333457, "W_D": 32.444547141423726, "J_D": 434.3311715829372, "W_D_1KI": 103.65670013234417, "J_D_1KI": 331.17156591803246} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output index c77cfff..513f3cc 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx'] -{"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": 30.323144912719727} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/amazon0312.mtx'] +{"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": 3.347659111022949} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9519, 0.7886, 0.4122, ..., 0.0191, 0.4041, 0.8787]) +tensor([0.4734, 0.1924, 0.8808, ..., 0.7808, 0.1983, 0.7064]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -18,7 +18,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 30.323144912719727 seconds +Time: 3.347659111022949 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 313 -m matrices/389000+_cols/amazon0312.mtx'] +{"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": 10.007118940353394} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9519, 0.7886, 0.4122, ..., 0.0191, 0.4041, 0.8787]) +tensor([0.2622, 0.5228, 0.4307, ..., 0.5472, 0.3686, 0.1004]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -37,13 +40,32 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 30.323144912719727 seconds +Time: 10.007118940353394 seconds -[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56] -[26.56, 26.64, 26.44, 29.44, 30.6, 36.2, 50.64, 63.2, 76.56, 90.36, 96.08, 94.84, 94.28, 93.0, 94.08, 94.08, 94.76, 95.56, 97.52, 96.08, 95.76, 91.48, 89.72, 90.12, 89.92, 89.6, 90.64, 91.72, 90.48, 91.12, 89.6, 89.6, 90.12, 90.48] -34.98710083961487 -{'CPU': 'Altra', 'CORES': 80, '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': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671} -[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56, 26.8, 26.84, 26.76, 26.72, 26.52, 26.44, 26.32, 26.16, 26.0, 26.0] -475.72 -23.786 -{'CPU': 'Altra', 'CORES': 80, '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': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671, 'J_1KI': 2708.072883968354, 'W_1KI': 77.40203729318671, 'W_D': 53.61603729318671, 'J_D': 1875.8697033972746, 'W_D_1KI': 53.61603729318671, 'J_D_1KI': 53.61603729318671} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.2622, 0.5228, 0.4307, ..., 0.5472, 0.3686, 0.1004]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.007118940353394 seconds + +[21.32, 21.36, 21.36, 21.4, 21.44, 21.44, 21.6, 21.16, 21.4, 21.16] +[20.88, 20.96, 21.2, 22.32, 24.04, 36.28, 50.32, 66.68, 79.88, 91.76, 91.76, 92.0, 92.36] +13.386877298355103 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 313, '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': 10.007118940353394, 'TIME_S_1KI': 31.97162600751883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.5519872760772, 'W': 51.509547141423724} +[21.32, 21.36, 21.36, 21.4, 21.44, 21.44, 21.6, 21.16, 21.4, 21.16, 20.6, 20.72, 20.8, 20.92, 21.0, 20.88, 20.92, 21.16, 21.52, 21.36] +381.3 +19.065 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 313, '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': 10.007118940353394, 'TIME_S_1KI': 31.97162600751883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.5519872760772, 'W': 51.509547141423724, 'J_1KI': 2203.0414928948153, 'W_1KI': 164.56724326333457, 'W_D': 32.444547141423726, 'J_D': 434.3311715829372, 'W_D_1KI': 103.65670013234417, 'J_D_1KI': 331.17156591803246} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json index 1f7ee40..2e38d31 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 21.815975189208984, "TIME_S_1KI": 21.815975189208984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.1918925571438, "W": 74.64919082171829, "J_1KI": 1916.1918925571438, "W_1KI": 74.64919082171829, "W_D": 51.65619082171829, "J_D": 1325.9778567373746, "W_D_1KI": 51.65619082171829, "J_D_1KI": 51.65619082171829} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 482, "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": 10.509353399276733, "TIME_S_1KI": 21.803637757835546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.3407905197143, "W": 56.77107336102275, "J_1KI": 1809.8356649786604, "W_1KI": 117.78230987764056, "W_D": 37.87007336102275, "J_D": 581.9091973602772, "W_D_1KI": 78.56861693158245, "J_D_1KI": 163.0054293186358} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output index 256e384..997e8ec 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx'] -{"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": 21.815975189208984} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/helm2d03.mtx'] +{"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": 2.177938222885132} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9058, 0.4925, 0.9859, ..., 0.1438, 0.2004, 0.4986]) +tensor([0.7657, 0.4255, 0.4226, ..., 0.3669, 0.8087, 0.9314]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 21.815975189208984 seconds +Time: 2.177938222885132 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 482 -m matrices/389000+_cols/helm2d03.mtx'] +{"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": 10.509353399276733} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9058, 0.4925, 0.9859, ..., 0.1438, 0.2004, 0.4986]) +tensor([0.7329, 0.1169, 0.4575, ..., 0.0344, 0.4424, 0.5702]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -39,13 +42,33 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 21.815975189208984 seconds +Time: 10.509353399276733 seconds -[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16] -[25.0, 24.96, 25.32, 27.72, 29.24, 40.8, 57.48, 70.72, 86.32, 97.72, 97.72, 96.24, 97.0, 95.72, 95.32, 93.32, 95.8, 96.36, 95.36, 95.72, 94.88, 94.16, 93.92, 92.68, 93.4] -25.669292211532593 -{'CPU': 'Altra', 'CORES': 80, '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': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829} -[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16, 25.44, 25.44, 25.28, 25.52, 25.88, 25.84, 25.68, 25.88, 25.8, 25.52] -459.86 -22.993000000000002 -{'CPU': 'Altra', 'CORES': 80, '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': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829, 'J_1KI': 1916.1918925571438, 'W_1KI': 74.64919082171829, 'W_D': 51.65619082171829, 'J_D': 1325.9778567373746, 'W_D_1KI': 51.65619082171829, 'J_D_1KI': 51.65619082171829} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.7329, 0.1169, 0.4575, ..., 0.0344, 0.4424, 0.5702]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.509353399276733 seconds + +[21.04, 20.84, 20.96, 20.96, 21.08, 21.2, 21.36, 21.52, 21.56, 21.4] +[21.56, 21.24, 21.44, 22.56, 26.08, 42.0, 42.0, 56.96, 74.52, 89.68, 98.52, 98.0, 96.24, 94.04, 90.4] +15.365937948226929 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 482, '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': 10.509353399276733, 'TIME_S_1KI': 21.803637757835546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.3407905197143, 'W': 56.77107336102275} +[21.04, 20.84, 20.96, 20.96, 21.08, 21.2, 21.36, 21.52, 21.56, 21.4, 21.16, 20.8, 21.0, 20.84, 20.72, 20.76, 20.76, 20.76, 20.64, 20.92] +378.02 +18.901 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 482, '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': 10.509353399276733, 'TIME_S_1KI': 21.803637757835546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.3407905197143, 'W': 56.77107336102275, 'J_1KI': 1809.8356649786604, 'W_1KI': 117.78230987764056, 'W_D': 37.87007336102275, 'J_D': 581.9091973602772, 'W_D_1KI': 78.56861693158245, 'J_D_1KI': 163.0054293186358} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json index 6674d3c..972d962 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 11.040250062942505, "TIME_S_1KI": 11.040250062942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1365.697071151733, "W": 66.06671490426629, "J_1KI": 1365.697071151733, "W_1KI": 66.06671490426629, "W_D": 44.15271490426629, "J_D": 912.7021604681013, "W_D_1KI": 44.15271490426629, "J_D_1KI": 44.15271490426629} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 884, "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": 11.851567268371582, "TIME_S_1KI": 13.406750303587764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1190.6868698120118, "W": 63.72440078348012, "J_1KI": 1346.9308482036333, "W_1KI": 72.08642622565624, "W_D": 44.698400783480125, "J_D": 835.1871223602296, "W_D_1KI": 50.56380179126711, "J_D_1KI": 57.1988708046008} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output index 8666779..a301654 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx'] -{"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": 11.040250062942505} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/language.mtx'] +{"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": 1.6173334121704102} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.4838, 0.0445, 0.9105, ..., 0.8272, 0.1700, 0.2253]) +tensor([0.5886, 0.7747, 0.2504, ..., 0.5146, 0.0032, 0.1335]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -18,7 +18,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 11.040250062942505 seconds +Time: 1.6173334121704102 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 649 -m matrices/389000+_cols/language.mtx'] +{"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": 7.708375930786133} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.4838, 0.0445, 0.9105, ..., 0.8272, 0.1700, 0.2253]) +tensor([0.0192, 0.1254, 0.7964, ..., 0.4582, 0.6654, 0.4900]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -37,13 +40,54 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 11.040250062942505 seconds +Time: 7.708375930786133 seconds -[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12] -[24.28, 24.56, 24.56, 28.8, 30.88, 44.64, 59.0, 72.44, 83.72, 90.96, 88.6, 88.6, 85.88, 86.24, 86.16, 87.16, 86.96, 88.8, 89.32, 90.72] -20.67148447036743 -{'CPU': 'Altra', 'CORES': 80, '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': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629} -[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12, 24.64, 24.56, 24.68, 24.52, 24.56, 24.64, 24.8, 24.72, 24.72, 24.6] -438.28 -21.913999999999998 -{'CPU': 'Altra', 'CORES': 80, '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': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629, 'J_1KI': 1365.697071151733, 'W_1KI': 66.06671490426629, 'W_D': 44.15271490426629, 'J_D': 912.7021604681013, 'W_D_1KI': 44.15271490426629, 'J_D_1KI': 44.15271490426629} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 884 -m matrices/389000+_cols/language.mtx'] +{"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": 11.851567268371582} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.8214, 0.4147, 0.6576, ..., 0.5168, 0.0794, 0.3445]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 11.851567268371582 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.8214, 0.4147, 0.6576, ..., 0.5168, 0.0794, 0.3445]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 11.851567268371582 seconds + +[20.72, 20.68, 20.84, 21.08, 20.88, 21.12, 21.32, 21.52, 21.52, 21.56] +[21.52, 21.24, 20.64, 24.16, 25.12, 40.2, 55.2, 69.68, 84.28, 93.4, 92.72, 93.0, 92.68, 90.64, 91.0, 90.68, 90.68, 88.28] +18.68494415283203 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 884, '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': 11.851567268371582, 'TIME_S_1KI': 13.406750303587764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.6868698120118, 'W': 63.72440078348012} +[20.72, 20.68, 20.84, 21.08, 20.88, 21.12, 21.32, 21.52, 21.52, 21.56, 21.32, 21.64, 21.52, 21.32, 21.12, 20.68, 20.72, 20.84, 21.12, 21.6] +380.52 +19.026 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 884, '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': 11.851567268371582, 'TIME_S_1KI': 13.406750303587764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.6868698120118, 'W': 63.72440078348012, 'J_1KI': 1346.9308482036333, 'W_1KI': 72.08642622565624, 'W_D': 44.698400783480125, 'J_D': 835.1871223602296, 'W_D_1KI': 50.56380179126711, 'J_D_1KI': 57.1988708046008} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json index 44a4ce9..123317a 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 47.31629490852356, "TIME_S_1KI": 47.31629490852356, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3588.3839783859257, "W": 77.04772778103, "J_1KI": 3588.3839783859257, "W_1KI": 77.04772778103, "W_D": 53.199727781030006, "J_D": 2477.698646305085, "W_D_1KI": 53.199727781030006, "J_D_1KI": 53.199727781030006} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 250, "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": 10.670701742172241, "TIME_S_1KI": 42.682806968688965, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.5241182804108, "W": 52.6363325812433, "J_1KI": 3034.096473121643, "W_1KI": 210.5453303249732, "W_D": 33.653332581243305, "J_D": 484.96662232279783, "W_D_1KI": 134.61333032497322, "J_D_1KI": 538.4533212998929} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output index 1934d42..b007654 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx'] -{"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": 47.31629490852356} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/marine1.mtx'] +{"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": 4.5594401359558105} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9557, 0.7622, 0.1453, ..., 0.2002, 0.7167, 0.8732]) +tensor([0.7164, 0.1317, 0.7878, ..., 0.1123, 0.4775, 0.4250]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 47.31629490852356 seconds +Time: 4.5594401359558105 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 230 -m matrices/389000+_cols/marine1.mtx'] +{"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": 9.633327722549438} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.9557, 0.7622, 0.1453, ..., 0.2002, 0.7167, 0.8732]) +tensor([0.3874, 0.3074, 0.2414, ..., 0.1445, 0.3638, 0.7985]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -39,13 +42,56 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 47.31629490852356 seconds +Time: 9.633327722549438 seconds -[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04] -[26.4, 26.6, 26.72, 27.8, 29.68, 36.68, 47.12, 59.56, 71.44, 82.04, 90.0, 90.2, 89.8, 91.64, 91.36, 89.28, 89.28, 88.72, 90.36, 90.56, 88.08, 89.32, 91.24, 89.92, 91.16, 94.4, 94.0, 92.04, 91.28, 91.0, 89.92, 89.76, 89.76, 89.8, 90.52, 90.56, 92.08, 91.72, 90.16, 89.0, 89.52, 88.96, 88.76, 89.0, 87.92] -46.57352113723755 -{'CPU': 'Altra', 'CORES': 80, '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': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103} -[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04, 26.76, 26.48, 26.44, 26.48, 26.72, 26.72, 26.92, 26.92, 27.0, 27.0] -476.9599999999999 -23.847999999999995 -{'CPU': 'Altra', 'CORES': 80, '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': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103, 'J_1KI': 3588.3839783859257, 'W_1KI': 77.04772778103, 'W_D': 53.199727781030006, 'J_D': 2477.698646305085, 'W_D_1KI': 53.199727781030006, 'J_D_1KI': 53.199727781030006} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 250 -m matrices/389000+_cols/marine1.mtx'] +{"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": 10.670701742172241} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.8821, 0.9353, 0.4205, ..., 0.7121, 0.3216, 0.5038]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.670701742172241 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.8821, 0.9353, 0.4205, ..., 0.7121, 0.3216, 0.5038]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.670701742172241 seconds + +[20.92, 20.96, 20.8, 20.92, 21.08, 21.16, 21.16, 21.28, 21.16, 21.24] +[21.24, 21.24, 21.48, 24.52, 26.52, 35.0, 41.8, 59.24, 70.16, 87.04, 92.28, 93.64, 93.28, 89.72] +14.410656690597534 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 250, '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': 10.670701742172241, 'TIME_S_1KI': 42.682806968688965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5241182804108, 'W': 52.6363325812433} +[20.92, 20.96, 20.8, 20.92, 21.08, 21.16, 21.16, 21.28, 21.16, 21.24, 20.64, 21.0, 21.16, 21.2, 21.2, 21.52, 21.2, 21.0, 20.96, 21.0] +379.65999999999997 +18.982999999999997 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 250, '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': 10.670701742172241, 'TIME_S_1KI': 42.682806968688965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5241182804108, 'W': 52.6363325812433, 'J_1KI': 3034.096473121643, 'W_1KI': 210.5453303249732, 'W_D': 33.653332581243305, 'J_D': 484.96662232279783, 'W_D_1KI': 134.61333032497322, 'J_D_1KI': 538.4533212998929} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json index 2a1d024..a59f65a 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 26.970608472824097, "TIME_S_1KI": 26.970608472824097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1533.6805934524534, "W": 70.7905728448904, "J_1KI": 1533.6805934524534, "W_1KI": 70.7905728448904, "W_D": 46.71357284489041, "J_D": 1012.0514249830246, "W_D_1KI": 46.71357284489041, "J_D_1KI": 46.71357284489041} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 724, "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": 16.38759136199951, "TIME_S_1KI": 22.634794698894353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1398.1154557418827, "W": 67.22076163927095, "J_1KI": 1931.098695776081, "W_1KI": 92.84635585534662, "W_D": 46.91176163927094, "J_D": 975.711333886862, "W_D_1KI": 64.79525088297092, "J_D_1KI": 89.49620287703165} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output index 34cf597..c12de20 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx'] -{"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": 26.970608472824097} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/mario002.mtx'] +{"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": 1.4485745429992676} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.0717, 0.4634, 0.0880, ..., 0.8346, 0.7497, 0.2295]) +tensor([0.4950, 0.6288, 0.7310, ..., 0.6351, 0.3616, 0.4602]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -18,7 +18,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 26.970608472824097 seconds +Time: 1.4485745429992676 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 724 -m matrices/389000+_cols/mario002.mtx'] +{"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": 16.38759136199951} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.0717, 0.4634, 0.0880, ..., 0.8346, 0.7497, 0.2295]) +tensor([0.2643, 0.8183, 0.0244, ..., 0.1942, 0.9771, 0.8862]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -37,13 +40,32 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 26.970608472824097 seconds +Time: 16.38759136199951 seconds -[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44] -[26.12, 26.4, 26.44, 27.4, 29.4, 37.08, 54.84, 69.0, 83.6, 95.56, 98.68, 97.52, 97.96, 97.76, 97.76, 97.8, 98.4, 95.2, 93.56, 91.64, 89.8] -21.665040016174316 -{'CPU': 'Altra', 'CORES': 80, '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': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904} -[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44, 26.88, 26.64, 26.8, 27.08, 27.0, 26.92, 26.68, 26.44, 26.32, 26.32] -481.53999999999996 -24.076999999999998 -{'CPU': 'Altra', 'CORES': 80, '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': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904, 'J_1KI': 1533.6805934524534, 'W_1KI': 70.7905728448904, 'W_D': 46.71357284489041, 'J_D': 1012.0514249830246, 'W_D_1KI': 46.71357284489041, 'J_D_1KI': 46.71357284489041} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.2643, 0.8183, 0.0244, ..., 0.1942, 0.9771, 0.8862]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 16.38759136199951 seconds + +[21.16, 21.08, 20.96, 20.96, 21.04, 21.0, 20.92, 20.88, 20.88, 20.68] +[20.92, 21.04, 22.04, 23.88, 30.04, 41.96, 61.04, 61.04, 74.72, 90.32, 97.56, 95.32, 94.88, 95.04, 93.4, 92.08, 91.96, 90.48, 91.36, 92.0] +20.798863649368286 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 724, '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': 16.38759136199951, 'TIME_S_1KI': 22.634794698894353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.1154557418827, 'W': 67.22076163927095} +[21.16, 21.08, 20.96, 20.96, 21.04, 21.0, 20.92, 20.88, 20.88, 20.68, 23.8, 23.76, 23.76, 24.52, 24.4, 24.4, 24.44, 24.36, 23.96, 24.08] +406.18000000000006 +20.309000000000005 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 724, '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': 16.38759136199951, 'TIME_S_1KI': 22.634794698894353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.1154557418827, 'W': 67.22076163927095, 'J_1KI': 1931.098695776081, 'W_1KI': 92.84635585534662, 'W_D': 46.91176163927094, 'J_D': 975.711333886862, 'W_D_1KI': 64.79525088297092, 'J_D_1KI': 89.49620287703165} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.json index e644ee7..4a5419c 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "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": 11.470221519470215, "TIME_S_1KI": 114.70221519470215, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 957.8876025772095, "W": 54.677060171971085, "J_1KI": 9578.876025772095, "W_1KI": 546.7706017197108, "W_D": 36.076060171971086, "J_D": 632.016620496273, "W_D_1KI": 360.76060171971085, "J_D_1KI": 3607.6060171971085} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "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": 10.841147184371948, "TIME_S_1KI": 108.41147184371948, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 985.8542587471007, "W": 56.246277973051825, "J_1KI": 9858.542587471007, "W_1KI": 562.4627797305183, "W_D": 37.621277973051825, "J_D": 659.4053588223456, "W_D_1KI": 376.21277973051826, "J_D_1KI": 3762.127797305183} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.output index 9a31fd5..9d72c91 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_msdoor.output @@ -1,5 +1,5 @@ ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/msdoor.mtx'] -{"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": 11.470221519470215} +{"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": 10.841147184371948} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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, 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([0.0348, 0.4239, 0.1578, ..., 0.9250, 0.1408, 0.2700]) +tensor([0.9807, 0.8009, 0.3733, ..., 0.5616, 0.1564, 0.8713]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,7 +19,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 11.470221519470215 seconds +Time: 10.841147184371948 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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, 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([0.0348, 0.4239, 0.1578, ..., 0.9250, 0.1408, 0.2700]) +tensor([0.9807, 0.8009, 0.3733, ..., 0.5616, 0.1564, 0.8713]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -39,13 +39,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 11.470221519470215 seconds +Time: 10.841147184371948 seconds -[20.56, 20.56, 20.52, 20.48, 20.68, 20.68, 20.56, 20.6, 20.64, 20.56] -[20.56, 20.64, 21.64, 22.44, 26.08, 26.08, 35.0, 38.96, 51.72, 68.64, 76.08, 87.88, 94.36, 93.12, 94.56, 93.48, 95.12] -17.51900339126587 -{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 11.470221519470215, 'TIME_S_1KI': 114.70221519470215, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 957.8876025772095, 'W': 54.677060171971085} -[20.56, 20.56, 20.52, 20.48, 20.68, 20.68, 20.56, 20.6, 20.64, 20.56, 20.84, 20.56, 20.68, 20.64, 20.6, 20.6, 20.88, 20.96, 20.96, 20.88] -372.02 -18.601 -{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 11.470221519470215, 'TIME_S_1KI': 114.70221519470215, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 957.8876025772095, 'W': 54.677060171971085, 'J_1KI': 9578.876025772095, 'W_1KI': 546.7706017197108, 'W_D': 36.076060171971086, 'J_D': 632.016620496273, 'W_D_1KI': 360.76060171971085, 'J_D_1KI': 3607.6060171971085} +[20.88, 20.76, 20.88, 20.72, 20.52, 20.52, 20.6, 20.68, 20.68, 20.68] +[20.64, 20.6, 20.52, 22.6, 23.44, 35.76, 36.92, 45.72, 59.68, 71.44, 78.12, 93.88, 93.48, 93.48, 94.16, 92.4, 90.84] +17.52745771408081 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 10.841147184371948, 'TIME_S_1KI': 108.41147184371948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 985.8542587471007, 'W': 56.246277973051825} +[20.88, 20.76, 20.88, 20.72, 20.52, 20.52, 20.6, 20.68, 20.68, 20.68, 20.6, 20.48, 20.56, 20.68, 20.64, 20.92, 20.8, 20.8, 20.76, 20.84] +372.5 +18.625 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 10.841147184371948, 'TIME_S_1KI': 108.41147184371948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 985.8542587471007, 'W': 56.246277973051825, 'J_1KI': 9858.542587471007, 'W_1KI': 562.4627797305183, 'W_D': 37.621277973051825, 'J_D': 659.4053588223456, 'W_D_1KI': 376.21277973051826, 'J_D_1KI': 3762.127797305183} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json index a64dfeb..2e9ee6f 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 80, "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": 92.92496466636658, "TIME_S_1KI": 92.92496466636658, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8599.791397418978, "W": 85.10134779265783, "J_1KI": 8599.791397418978, "W_1KI": 85.10134779265783, "W_D": 60.408347792657835, "J_D": 6104.4766405498995, "W_D_1KI": 60.408347792657835, "J_D_1KI": 60.408347792657835} +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 134, "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": 10.734622240066528, "TIME_S_1KI": 80.10912119452632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1165.961840686798, "W": 59.88416348969712, "J_1KI": 8701.207766319389, "W_1KI": 446.89674246042625, "W_D": 40.74416348969713, "J_D": 793.3005504512787, "W_D_1KI": 304.06092156490394, "J_D_1KI": 2269.1113549619695} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output index 6caaca3..a1e38c5 100644 --- a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx'] -{"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": 92.92496466636658} +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/test1.mtx'] +{"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": 7.78681206703186} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.1407, 0.4697, 0.2203, ..., 0.3353, 0.2584, 0.9591]) +tensor([0.8142, 0.6149, 0.3771, ..., 0.4524, 0.2082, 0.4883]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,7 +19,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 92.92496466636658 seconds +Time: 7.78681206703186 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 134 -m matrices/389000+_cols/test1.mtx'] +{"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": 10.734622240066528} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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([0.1407, 0.4697, 0.2203, ..., 0.3353, 0.2584, 0.9591]) +tensor([0.0291, 0.5333, 0.0083, ..., 0.7423, 0.9201, 0.1436]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -39,13 +42,33 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 92.92496466636658 seconds +Time: 10.734622240066528 seconds -[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64] -[26.8, 26.88, 30.16, 31.04, 31.04, 35.8, 41.52, 47.96, 57.12, 73.0, 77.8, 92.16, 95.08, 96.56, 94.84, 94.48, 94.88, 92.8, 91.28, 91.28, 91.72, 91.6, 89.08, 89.24, 90.12, 89.76, 89.48, 89.84, 90.24, 88.68, 88.6, 90.24, 88.36, 90.44, 90.44, 93.28, 93.6, 95.92, 95.52, 91.44, 91.84, 89.96, 91.0, 91.04, 90.84, 90.68, 88.92, 93.0, 95.6, 97.32, 97.32, 98.32, 96.56, 92.48, 91.04, 92.0, 91.72, 94.24, 92.8, 92.52, 91.64, 93.04, 93.56, 98.0, 99.0, 97.0, 97.0, 94.36, 93.2, 93.0, 91.36, 92.44, 95.16, 96.28, 96.76, 96.4, 97.92, 92.76, 93.72, 94.64, 95.76, 96.08, 96.08, 99.8, 99.2, 97.52, 97.16, 96.92, 97.2, 99.08, 99.0, 97.56, 97.8, 97.16, 97.4, 98.52, 99.92, 98.48] -101.05352759361267 -{'CPU': 'Altra', 'CORES': 80, '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': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783} -[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64, 27.96, 27.76, 27.8, 27.8, 27.96, 28.08, 28.04, 27.96, 27.76, 27.68] -493.86 -24.693 -{'CPU': 'Altra', 'CORES': 80, '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': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783, 'J_1KI': 8599.791397418978, 'W_1KI': 85.10134779265783, 'W_D': 60.408347792657835, 'J_D': 6104.4766405498995, 'W_D_1KI': 60.408347792657835, 'J_D_1KI': 60.408347792657835} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0291, 0.5333, 0.0083, ..., 0.7423, 0.9201, 0.1436]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.734622240066528 seconds + +[21.28, 21.2, 21.2, 21.52, 21.6, 21.64, 21.6, 21.6, 21.4, 21.12] +[20.96, 20.92, 21.2, 25.0, 26.0, 33.88, 35.04, 50.52, 62.8, 75.28, 84.96, 84.96, 92.52, 89.64, 89.32, 91.76, 91.92, 91.0, 91.48] +19.47028684616089 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 134, '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': 10.734622240066528, 'TIME_S_1KI': 80.10912119452632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1165.961840686798, 'W': 59.88416348969712} +[21.28, 21.2, 21.2, 21.52, 21.6, 21.64, 21.6, 21.6, 21.4, 21.12, 21.2, 21.16, 21.12, 21.24, 21.0, 21.0, 21.08, 21.08, 21.08, 20.96] +382.79999999999995 +19.139999999999997 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 134, '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': 10.734622240066528, 'TIME_S_1KI': 80.10912119452632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1165.961840686798, 'W': 59.88416348969712, 'J_1KI': 8701.207766319389, 'W_1KI': 446.89674246042625, 'W_D': 40.74416348969713, 'J_D': 793.3005504512787, 'W_D_1KI': 304.06092156490394, 'J_D_1KI': 2269.1113549619695} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json index 9573140..039a99b 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19947, "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": 10.311090230941772, "TIME_S_1KI": 0.5169243611040143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1933.8912408947945, "W": 148.79, "J_1KI": 96.951483475951, "W_1KI": 7.459267057702912, "W_D": 113.0615, "J_D": 1469.5116911917924, "W_D_1KI": 5.668095452950318, "J_D_1KI": 0.28415779079311765} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20444, "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": 10.743699073791504, "TIME_S_1KI": 0.5255184442277198, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1952.5723613500595, "W": 147.85, "J_1KI": 95.50833307327625, "W_1KI": 7.231950694580317, "W_D": 111.42925, "J_D": 1471.583860642314, "W_D_1KI": 5.4504622383095285, "J_D_1KI": 0.2666044921888832} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output index 2ce455e..2d1577b 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx'] -{"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": 0.5623607635498047} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 0.07464981079101562} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4201, 0.2453, 0.6690, ..., 0.5318, 0.2145, 0.3171]) +tensor([0.5382, 0.0057, 0.9081, ..., 0.3448, 0.4948, 0.2998]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 0.5623607635498047 seconds +Time: 0.07464981079101562 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18671', '-m', 'matrices/389000+_cols/amazon0312.mtx'] -{"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": 9.828076839447021} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '14065', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 7.223658800125122} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8027, 0.1127, 0.8893, ..., 0.1900, 0.1655, 0.6380]) +tensor([0.1299, 0.9977, 0.9750, ..., 0.5958, 0.2133, 0.2941]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -40,10 +40,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 9.828076839447021 seconds +Time: 7.223658800125122 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19947', '-m', 'matrices/389000+_cols/amazon0312.mtx'] -{"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": 10.311090230941772} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20444', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 10.743699073791504} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8053, 0.3778, 0.3769, ..., 0.3826, 0.6227, 0.7489]) +tensor([0.1957, 0.9680, 0.1698, ..., 0.9334, 0.4823, 0.2801]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -62,7 +62,7 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.311090230941772 seconds +Time: 10.743699073791504 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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, 5, 10, ..., 3200428, 400707]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(400727, 400727), nnz=3200440, layout=torch.sparse_csr) -tensor([0.8053, 0.3778, 0.3769, ..., 0.3826, 0.6227, 0.7489]) +tensor([0.1957, 0.9680, 0.1698, ..., 0.9334, 0.4823, 0.2801]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -81,13 +81,13 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.311090230941772 seconds +Time: 10.743699073791504 seconds -[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21] -[148.79] -12.997454404830933 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, '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': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79} -[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21, 39.98, 39.27, 40.58, 39.91, 39.77, 39.38, 39.3, 39.5, 39.26, 39.23] -714.5699999999999 -35.7285 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, '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': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79, 'J_1KI': 96.951483475951, 'W_1KI': 7.459267057702912, 'W_D': 113.0615, 'J_D': 1469.5116911917924, 'W_D_1KI': 5.668095452950318, 'J_D_1KI': 0.28415779079311765} +[41.13, 39.71, 39.86, 39.79, 40.29, 40.11, 39.78, 40.05, 40.27, 45.95] +[147.85] +13.206441402435303 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20444, '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': 10.743699073791504, 'TIME_S_1KI': 0.5255184442277198, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1952.5723613500595, 'W': 147.85} +[41.13, 39.71, 39.86, 39.79, 40.29, 40.11, 39.78, 40.05, 40.27, 45.95, 40.44, 39.9, 39.89, 39.77, 39.77, 40.33, 45.71, 39.7, 39.86, 39.73] +728.415 +36.42075 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20444, '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': 10.743699073791504, 'TIME_S_1KI': 0.5255184442277198, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1952.5723613500595, 'W': 147.85, 'J_1KI': 95.50833307327625, 'W_1KI': 7.231950694580317, 'W_D': 111.42925, 'J_D': 1471.583860642314, 'W_D_1KI': 5.4504622383095285, 'J_D_1KI': 0.2666044921888832} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json index f538d2a..9df49c0 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30516, "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": 10.102073907852173, "TIME_S_1KI": 0.3310418766500253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1905.9418558597565, "W": 149.24, "J_1KI": 62.45713251604917, "W_1KI": 4.89054922008127, "W_D": 113.06250000000001, "J_D": 1443.9195328205826, "W_D_1KI": 3.7050235941801026, "J_D_1KI": 0.12141249161686009} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31485, "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": 10.251946926116943, "TIME_S_1KI": 0.3256136867116704, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1974.5086739730834, "W": 148.38, "J_1KI": 62.71267822687258, "W_1KI": 4.712720343020486, "W_D": 112.41049999999998, "J_D": 1495.858655449867, "W_D_1KI": 3.57028743846276, "J_D_1KI": 0.11339645667660028} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output index e5c9a6c..8154ff4 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx'] -{"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": 0.3914318084716797} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 0.07498788833618164} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2679, 0.5598, 0.6944, ..., 0.1144, 0.4933, 0.2716]) +tensor([0.3070, 0.9264, 0.3239, ..., 0.1710, 0.1593, 0.5164]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 0.3914318084716797 seconds +Time: 0.07498788833618164 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26824', '-m', 'matrices/389000+_cols/helm2d03.mtx'] -{"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": 9.229604244232178} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '14002', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 4.669507026672363} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3693, 0.3607, 0.3383, ..., 0.8476, 0.1262, 0.7740]) +tensor([0.9355, 0.2021, 0.7636, ..., 0.2628, 0.6881, 0.5346]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -42,10 +42,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 9.229604244232178 seconds +Time: 4.669507026672363 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30516', '-m', 'matrices/389000+_cols/helm2d03.mtx'] -{"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": 10.102073907852173} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31485', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 10.251946926116943} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9962, 0.9854, 0.1629, ..., 0.5690, 0.4270, 0.4262]) +tensor([0.2013, 0.9305, 0.4942, ..., 0.1847, 0.5234, 0.5260]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -65,7 +65,7 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.102073907852173 seconds +Time: 10.251946926116943 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9962, 0.9854, 0.1629, ..., 0.5690, 0.4270, 0.4262]) +tensor([0.2013, 0.9305, 0.4942, ..., 0.1847, 0.5234, 0.5260]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -85,13 +85,13 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.102073907852173 seconds +Time: 10.251946926116943 seconds -[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31] -[149.24] -12.77098536491394 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, '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': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24} -[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31, 39.88, 39.67, 44.75, 39.75, 39.19, 39.36, 39.18, 39.31, 39.57, 39.06] -723.55 -36.177499999999995 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, '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': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24, 'J_1KI': 62.45713251604917, 'W_1KI': 4.89054922008127, 'W_D': 113.06250000000001, 'J_D': 1443.9195328205826, 'W_D_1KI': 3.7050235941801026, 'J_D_1KI': 0.12141249161686009} +[40.35, 39.88, 39.67, 39.9, 39.63, 39.59, 40.1, 39.6, 39.74, 39.57] +[148.38] +13.307107925415039 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31485, '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': 10.251946926116943, 'TIME_S_1KI': 0.3256136867116704, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1974.5086739730834, 'W': 148.38} +[40.35, 39.88, 39.67, 39.9, 39.63, 39.59, 40.1, 39.6, 39.74, 39.57, 40.5, 39.64, 41.53, 39.95, 40.12, 39.48, 39.63, 41.18, 39.57, 39.94] +719.3900000000001 +35.969500000000004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31485, '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': 10.251946926116943, 'TIME_S_1KI': 0.3256136867116704, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1974.5086739730834, 'W': 148.38, 'J_1KI': 62.71267822687258, 'W_1KI': 4.712720343020486, 'W_D': 112.41049999999998, 'J_D': 1495.858655449867, 'W_D_1KI': 3.57028743846276, 'J_D_1KI': 0.11339645667660028} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json index 0278cd1..35701f2 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31490, "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": 10.491079330444336, "TIME_S_1KI": 0.33315590125259875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1840.929758644104, "W": 139.7, "J_1KI": 58.46077353585595, "W_1KI": 4.436328993331216, "W_D": 104.24499999999999, "J_D": 1373.713118753433, "W_D_1KI": 3.310416005080978, "J_D_1KI": 0.10512594490571539} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31030, "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": 10.644544839859009, "TIME_S_1KI": 0.34304043957006153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1841.5261273384094, "W": 137.88, "J_1KI": 59.34663639505026, "W_1KI": 4.443441830486626, "W_D": 101.87724999999999, "J_D": 1360.6731770843267, "W_D_1KI": 3.2831856268127617, "J_D_1KI": 0.1058068200713104} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output index 47af1b0..5fd0cce 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx'] -{"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": 0.366832971572876} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 0.0656125545501709} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4489, 0.3341, 0.6321, ..., 0.9617, 0.3616, 0.1355]) +tensor([0.8738, 0.8223, 0.1046, ..., 0.8913, 0.8470, 0.6996]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 0.366832971572876 seconds +Time: 0.0656125545501709 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28623', '-m', 'matrices/389000+_cols/language.mtx'] -{"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": 9.543859958648682} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '16003', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 5.415090322494507} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6001, 0.9333, 0.1063, ..., 0.8124, 0.4889, 0.2196]) +tensor([0.8933, 0.4751, 0.7405, ..., 0.3665, 0.2174, 0.2241]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -40,10 +40,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 9.543859958648682 seconds +Time: 5.415090322494507 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31490', '-m', 'matrices/389000+_cols/language.mtx'] -{"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": 10.491079330444336} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31030', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 10.644544839859009} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8719, 0.5932, 0.6007, ..., 0.1184, 0.6398, 0.5112]) +tensor([0.0411, 0.6108, 0.8373, ..., 0.6771, 0.3976, 0.3132]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -62,7 +62,7 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.491079330444336 seconds +Time: 10.644544839859009 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8719, 0.5932, 0.6007, ..., 0.1184, 0.6398, 0.5112]) +tensor([0.0411, 0.6108, 0.8373, ..., 0.6771, 0.3976, 0.3132]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -81,13 +81,13 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.491079330444336 seconds +Time: 10.644544839859009 seconds -[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92] -[139.7] -13.177736282348633 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, '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': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7} -[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92, 40.13, 40.06, 39.2, 39.24, 39.63, 39.8, 39.04, 39.77, 39.24, 39.0] -709.1 -35.455 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, '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': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7, 'J_1KI': 58.46077353585595, 'W_1KI': 4.436328993331216, 'W_D': 104.24499999999999, 'J_D': 1373.713118753433, 'W_D_1KI': 3.310416005080978, 'J_D_1KI': 0.10512594490571539} +[40.9, 39.77, 39.78, 39.58, 40.8, 39.62, 39.7, 39.78, 39.6, 39.66] +[137.88] +13.356006145477295 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31030, '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': 10.644544839859009, 'TIME_S_1KI': 0.34304043957006153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1841.5261273384094, 'W': 137.88} +[40.9, 39.77, 39.78, 39.58, 40.8, 39.62, 39.7, 39.78, 39.6, 39.66, 41.27, 40.24, 40.42, 39.63, 40.3, 40.0, 40.47, 39.96, 39.69, 39.6] +720.0550000000001 +36.002750000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31030, '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': 10.644544839859009, 'TIME_S_1KI': 0.34304043957006153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1841.5261273384094, 'W': 137.88, 'J_1KI': 59.34663639505026, 'W_1KI': 4.443441830486626, 'W_D': 101.87724999999999, 'J_D': 1360.6731770843267, 'W_D_1KI': 3.2831856268127617, 'J_D_1KI': 0.1058068200713104} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json index d48619d..24a073e 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19504, "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": 10.365571975708008, "TIME_S_1KI": 0.5314587764411407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2136.8143803691864, "W": 158.23, "J_1KI": 109.55775124944557, "W_1KI": 8.112694831829367, "W_D": 121.8915, "J_D": 1646.0817167716025, "W_D_1KI": 6.249564191960623, "J_D_1KI": 0.3204247432301386} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19526, "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": 10.585829496383667, "TIME_S_1KI": 0.5421401974999318, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2131.511208808422, "W": 156.70999999999998, "J_1KI": 109.16271682927491, "W_1KI": 8.025709310662705, "W_D": 120.54374999999997, "J_D": 1639.5913105532522, "W_D_1KI": 6.173499436648569, "J_D_1KI": 0.3161681571570506} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output index 5ccf432..893cef7 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx'] -{"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": 0.6027348041534424} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 0.09912705421447754} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.5488, 0.1373, 0.1334, ..., 0.6361, 0.1287, 0.1871]) +tensor([0.1039, 0.0419, 0.4538, ..., 0.1385, 0.4900, 0.1194]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 0.6027348041534424 seconds +Time: 0.09912705421447754 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17420', '-m', 'matrices/389000+_cols/marine1.mtx'] -{"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": 9.3778395652771} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '10592', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 5.695624351501465} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.5314, 0.3776, 0.6404, ..., 0.2799, 0.5486, 0.5791]) +tensor([0.5908, 0.2614, 0.4183, ..., 0.3795, 0.9697, 0.2884]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -42,10 +42,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 9.3778395652771 seconds +Time: 5.695624351501465 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19504', '-m', 'matrices/389000+_cols/marine1.mtx'] -{"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": 10.365571975708008} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19526', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 10.585829496383667} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7619, 0.3106, 0.0542, ..., 0.9939, 0.8451, 0.6161]) +tensor([0.2662, 0.1912, 0.2943, ..., 0.6909, 0.8280, 0.5797]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -65,7 +65,7 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.365571975708008 seconds +Time: 10.585829496383667 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7619, 0.3106, 0.0542, ..., 0.9939, 0.8451, 0.6161]) +tensor([0.2662, 0.1912, 0.2943, ..., 0.6909, 0.8280, 0.5797]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -85,13 +85,13 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.365571975708008 seconds +Time: 10.585829496383667 seconds -[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37] -[158.23] -13.504483222961426 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, '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': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23} -[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37, 40.1, 41.46, 44.99, 39.34, 39.95, 39.5, 40.61, 39.29, 39.33, 39.27] -726.77 -36.338499999999996 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, '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': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23, 'J_1KI': 109.55775124944557, 'W_1KI': 8.112694831829367, 'W_D': 121.8915, 'J_D': 1646.0817167716025, 'W_D_1KI': 6.249564191960623, 'J_D_1KI': 0.3204247432301386} +[41.18, 40.21, 39.95, 39.84, 39.76, 39.75, 40.62, 39.63, 40.37, 40.25] +[156.71] +13.601628541946411 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19526, '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': 10.585829496383667, 'TIME_S_1KI': 0.5421401974999318, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2131.511208808422, 'W': 156.70999999999998} +[41.18, 40.21, 39.95, 39.84, 39.76, 39.75, 40.62, 39.63, 40.37, 40.25, 40.63, 40.04, 40.26, 40.99, 40.14, 39.59, 39.59, 41.68, 40.09, 39.57] +723.325 +36.166250000000005 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19526, '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': 10.585829496383667, 'TIME_S_1KI': 0.5421401974999318, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2131.511208808422, 'W': 156.70999999999998, 'J_1KI': 109.16271682927491, 'W_1KI': 8.025709310662705, 'W_D': 120.54374999999997, 'J_D': 1639.5913105532522, 'W_D_1KI': 6.173499436648569, 'J_D_1KI': 0.3161681571570506} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json index 69c4b0e..8a04e1e 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 24972, "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": 10.226792573928833, "TIME_S_1KI": 0.4095303769793702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.3517652535438, "W": 140.07, "J_1KI": 66.68876202360819, "W_1KI": 5.609082172032676, "W_D": 104.13924999999999, "J_D": 1238.155806523025, "W_D_1KI": 4.170240669549895, "J_D_1KI": 0.16699666304460578} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27603, "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": 10.749645233154297, "TIME_S_1KI": 0.3894375695813606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1902.9152708339693, "W": 138.46, "J_1KI": 68.93871212672424, "W_1KI": 5.01612143607579, "W_D": 102.32625, "J_D": 1406.3136193281412, "W_D_1KI": 3.7070698837082925, "J_D_1KI": 0.1342995284464838} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output index 0369c8f..8aa168d 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx'] -{"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": 0.4204576015472412} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 0.07314276695251465} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4700, 0.6519, 0.6666, ..., 0.7659, 0.1482, 0.5452]) +tensor([0.7100, 0.1750, 0.2816, ..., 0.1443, 0.3555, 0.1765]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 0.4204576015472412 seconds +Time: 0.07314276695251465 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24972', '-m', 'matrices/389000+_cols/mario002.mtx'] -{"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": 10.226792573928833} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '14355', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 5.46039080619812} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0699, 0.8434, 0.7786, ..., 0.4343, 0.2465, 0.4017]) +tensor([0.5853, 0.7185, 0.5959, ..., 0.9034, 0.4321, 0.0194]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -40,7 +40,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.226792573928833 seconds +Time: 5.46039080619812 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '27603', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 10.749645233154297} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0699, 0.8434, 0.7786, ..., 0.4343, 0.2465, 0.4017]) +tensor([0.4515, 0.9454, 0.8708, ..., 0.6311, 0.0544, 0.8027]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -59,13 +62,32 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.226792573928833 seconds +Time: 10.749645233154297 seconds -[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87] -[140.07] -11.889425039291382 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, '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': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07} -[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87, 39.88, 39.14, 39.13, 39.09, 39.22, 39.35, 39.38, 39.37, 40.66, 59.78] -718.615 -35.93075 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, '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': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07, 'J_1KI': 66.68876202360819, 'W_1KI': 5.609082172032676, 'W_D': 104.13924999999999, 'J_D': 1238.155806523025, 'W_D_1KI': 4.170240669549895, 'J_D_1KI': 0.16699666304460578} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4515, 0.9454, 0.8708, ..., 0.6311, 0.0544, 0.8027]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.749645233154297 seconds + +[40.75, 39.81, 40.34, 39.74, 40.56, 40.24, 41.33, 39.87, 39.64, 39.7] +[138.46] +13.74342966079712 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27603, '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': 10.749645233154297, 'TIME_S_1KI': 0.3894375695813606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1902.9152708339693, 'W': 138.46} +[40.75, 39.81, 40.34, 39.74, 40.56, 40.24, 41.33, 39.87, 39.64, 39.7, 40.43, 39.98, 40.42, 40.2, 40.4, 40.22, 40.21, 39.67, 39.79, 39.63] +722.6750000000001 +36.133750000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27603, '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': 10.749645233154297, 'TIME_S_1KI': 0.3894375695813606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1902.9152708339693, 'W': 138.46, 'J_1KI': 68.93871212672424, 'W_1KI': 5.01612143607579, 'W_D': 102.32625, 'J_D': 1406.3136193281412, 'W_D_1KI': 3.7070698837082925, 'J_D_1KI': 0.1342995284464838} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.json index 76268cb..5bc5bf4 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2238, "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": 11.481182098388672, "TIME_S_1KI": 5.130108176223714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1893.635374045372, "W": 130.54, "J_1KI": 846.1284066333208, "W_1KI": 58.32886505808758, "W_D": 94.69824999999999, "J_D": 1373.7088713052867, "W_D_1KI": 42.313784629133146, "J_D_1KI": 18.90696364125699} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1942, "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": 10.117614030838013, "TIME_S_1KI": 5.20989393966942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1678.0417341613768, "W": 128.64, "J_1KI": 864.0791628019448, "W_1KI": 66.24098867147269, "W_D": 92.52024999999999, "J_D": 1206.8784262674449, "W_D_1KI": 47.641735324407826, "J_D_1KI": 24.53230449248601} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.output index aa659f8..b85e195 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_msdoor.output @@ -1,5 +1,5 @@ ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 0.544938325881958} +{"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": 0.5405440330505371} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0497, 0.3572, 0.9272, ..., 0.8625, 0.2792, 0.5285]) +tensor([0.2095, 0.1237, 0.5072, ..., 0.3381, 0.2229, 0.3516]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 0.544938325881958 seconds +Time: 0.5405440330505371 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1926', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 9.506705045700073} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1942', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 10.117614030838013} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9110, 0.2003, 0.2096, ..., 0.2036, 0.8917, 0.5683]) +tensor([0.0159, 0.3909, 0.5799, ..., 0.8292, 0.1223, 0.5318]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -42,10 +42,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 9.506705045700073 seconds - -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2127', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 9.97889494895935} +Time: 10.117614030838013 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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 +53,7 @@ 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([0.5968, 0.7420, 0.6758, ..., 0.0056, 0.9599, 0.3333]) +tensor([0.0159, 0.3909, 0.5799, ..., 0.8292, 0.1223, 0.5318]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -65,56 +62,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 9.97889494895935 seconds +Time: 10.117614030838013 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2238', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 11.481182098388672} - -/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1118, 0.5594, 0.9085, ..., 0.4616, 0.1638, 0.5506]) -Matrix Type: SuiteSparse -Matrix: msdoor -Matrix Format: csr -Shape: torch.Size([415863, 415863]) -Rows: 415863 -Size: 172942034769 -NNZ: 20240935 -Density: 0.00011703883921012015 -Time: 11.481182098388672 seconds - -/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1118, 0.5594, 0.9085, ..., 0.4616, 0.1638, 0.5506]) -Matrix Type: SuiteSparse -Matrix: msdoor -Matrix Format: csr -Shape: torch.Size([415863, 415863]) -Rows: 415863 -Size: 172942034769 -NNZ: 20240935 -Density: 0.00011703883921012015 -Time: 11.481182098388672 seconds - -[40.88, 39.48, 39.55, 40.68, 39.35, 39.35, 39.89, 39.72, 39.69, 39.48] -[130.54] -14.506169557571411 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2238, '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': 11.481182098388672, 'TIME_S_1KI': 5.130108176223714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1893.635374045372, 'W': 130.54} -[40.88, 39.48, 39.55, 40.68, 39.35, 39.35, 39.89, 39.72, 39.69, 39.48, 40.3, 39.34, 40.16, 39.39, 39.51, 39.33, 39.34, 39.93, 39.57, 44.45] -716.835 -35.841750000000005 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2238, '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': 11.481182098388672, 'TIME_S_1KI': 5.130108176223714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1893.635374045372, 'W': 130.54, 'J_1KI': 846.1284066333208, 'W_1KI': 58.32886505808758, 'W_D': 94.69824999999999, 'J_D': 1373.7088713052867, 'W_D_1KI': 42.313784629133146, 'J_D_1KI': 18.90696364125699} +[40.88, 39.6, 44.74, 39.67, 40.01, 39.37, 39.44, 39.58, 39.92, 41.63] +[128.64] +13.04447865486145 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1942, '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': 10.117614030838013, 'TIME_S_1KI': 5.20989393966942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1678.0417341613768, 'W': 128.64} +[40.88, 39.6, 44.74, 39.67, 40.01, 39.37, 39.44, 39.58, 39.92, 41.63, 40.9, 40.26, 39.79, 39.47, 39.91, 39.45, 40.18, 39.67, 39.87, 39.52] +722.395 +36.119749999999996 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1942, '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': 10.117614030838013, 'TIME_S_1KI': 5.20989393966942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1678.0417341613768, 'W': 128.64, 'J_1KI': 864.0791628019448, 'W_1KI': 66.24098867147269, 'W_D': 92.52024999999999, 'J_D': 1206.8784262674449, 'W_D_1KI': 47.641735324407826, 'J_D_1KI': 24.53230449248601} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json index f7d6df9..a483f7b 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2723, "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": 11.063719511032104, "TIME_S_1KI": 4.063062618814581, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1680.215021328926, "W": 125.38, "J_1KI": 617.045545842426, "W_1KI": 46.04480352552332, "W_D": 89.84949999999999, "J_D": 1204.071459235072, "W_D_1KI": 32.99651120088138, "J_D_1KI": 12.117705178436056} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2545, "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": 10.131094455718994, "TIME_S_1KI": 3.980783676117483, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1672.2965030264854, "W": 122.27, "J_1KI": 657.0909638610945, "W_1KI": 48.04322200392927, "W_D": 86.17024999999998, "J_D": 1178.5573545425532, "W_D_1KI": 33.85864440078585, "J_D_1KI": 13.3039860120966} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output index a60431b..dfbacb5 100644 --- a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx'] -{"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": 3.8556222915649414} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 0.41251182556152344} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7510, 0.1721, 0.0746, ..., 0.5838, 0.0016, 0.2497]) +tensor([0.5870, 0.1854, 0.9273, ..., 0.6429, 0.9789, 0.8173]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 3.8556222915649414 seconds +Time: 0.41251182556152344 seconds -['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2723', '-m', 'matrices/389000+_cols/test1.mtx'] -{"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": 11.063719511032104} +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2545', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 10.131094455718994} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3646, 0.9778, 0.6678, ..., 0.8764, 0.3618, 0.1561]) +tensor([0.6282, 0.5992, 0.7140, ..., 0.0584, 0.7225, 0.7014]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -42,7 +42,7 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 11.063719511032104 seconds +Time: 10.131094455718994 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3646, 0.9778, 0.6678, ..., 0.8764, 0.3618, 0.1561]) +tensor([0.6282, 0.5992, 0.7140, ..., 0.0584, 0.7225, 0.7014]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -62,13 +62,13 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 11.063719511032104 seconds +Time: 10.131094455718994 seconds -[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17] -[125.38] -13.400981187820435 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, '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': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38} -[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17, 40.93, 39.54, 39.73, 39.16, 39.13, 39.04, 39.13, 39.33, 39.0, 39.11] -710.61 -35.5305 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, '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': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38, 'J_1KI': 617.045545842426, 'W_1KI': 46.04480352552332, 'W_D': 89.84949999999999, 'J_D': 1204.071459235072, 'W_D_1KI': 32.99651120088138, 'J_D_1KI': 12.117705178436056} +[40.48, 39.49, 39.56, 41.16, 39.55, 39.52, 39.52, 39.92, 42.03, 40.2] +[122.27] +13.677079439163208 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2545, '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': 10.131094455718994, 'TIME_S_1KI': 3.980783676117483, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1672.2965030264854, 'W': 122.27} +[40.48, 39.49, 39.56, 41.16, 39.55, 39.52, 39.52, 39.92, 42.03, 40.2, 40.52, 39.72, 41.17, 40.74, 40.19, 39.86, 39.67, 39.62, 39.7, 39.95] +721.9950000000001 +36.09975000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2545, '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': 10.131094455718994, 'TIME_S_1KI': 3.980783676117483, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1672.2965030264854, 'W': 122.27, 'J_1KI': 657.0909638610945, 'W_1KI': 48.04322200392927, 'W_D': 86.17024999999998, 'J_D': 1178.5573545425532, 'W_D_1KI': 33.85864440078585, 'J_D_1KI': 13.3039860120966} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json index 17b55ec..bad19c8 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8225, "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": 10.766162395477295, "TIME_S_1KI": 1.3089559143437441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.989917767048, "W": 89.05, "J_1KI": 158.17506598991466, "W_1KI": 10.826747720364741, "W_D": 72.6925, "J_D": 1062.012460384965, "W_D_1KI": 8.837993920972645, "J_D_1KI": 1.0745281362884673} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7969, "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": 10.460268020629883, "TIME_S_1KI": 1.3126199047094844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1273.458453361988, "W": 89.60999999999999, "J_1KI": 159.80153762855917, "W_1KI": 11.244823691805745, "W_D": 72.86774999999999, "J_D": 1035.532331380069, "W_D_1KI": 9.143901367800224, "J_D_1KI": 1.1474339776383766} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output index 81fbe8a..294fa8c 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx'] -{"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": 1.2765758037567139} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 0.14413952827453613} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.8946, 0.9409, 0.1159, ..., 0.4084, 0.7142, 0.8849]) +tensor([0.1815, 0.4837, 0.2778, ..., 0.6260, 0.2954, 0.0974]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 1.2765758037567139 seconds +Time: 0.14413952827453613 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '8225', '-m', 'matrices/389000+_cols/amazon0312.mtx'] -{"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": 10.766162395477295} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '7284', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 9.59673285484314} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2056, 0.8237, 0.5206, ..., 0.1956, 0.5378, 0.6984]) +tensor([0.7104, 0.5699, 0.1142, ..., 0.3938, 0.4522, 0.8852]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -40,7 +40,10 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.766162395477295 seconds +Time: 9.59673285484314 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '7969', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 10.460268020629883} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2056, 0.8237, 0.5206, ..., 0.1956, 0.5378, 0.6984]) +tensor([0.1192, 0.7104, 0.6793, ..., 0.8908, 0.2097, 0.0637]) Matrix Type: SuiteSparse Matrix: amazon0312 Matrix Format: csr @@ -59,13 +62,32 @@ Rows: 400727 Size: 160582128529 NNZ: 3200440 Density: 1.9930237750099465e-05 -Time: 10.766162395477295 seconds +Time: 10.460268020629883 seconds -[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57] -[89.05] -14.609656572341919 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, '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': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05} -[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57, 18.25, 17.56, 21.38, 17.98, 17.81, 17.52, 18.04, 17.55, 17.82, 17.58] -327.15 -16.357499999999998 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, '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': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05, 'J_1KI': 158.17506598991466, 'W_1KI': 10.826747720364741, 'W_D': 72.6925, 'J_D': 1062.012460384965, 'W_D_1KI': 8.837993920972645, 'J_D_1KI': 1.0745281362884673} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1192, 0.7104, 0.6793, ..., 0.8908, 0.2097, 0.0637]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.460268020629883 seconds + +[19.33, 18.71, 18.84, 18.65, 18.46, 18.36, 18.45, 18.79, 18.56, 19.13] +[89.61] +14.211119890213013 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7969, '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': 10.460268020629883, 'TIME_S_1KI': 1.3126199047094844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1273.458453361988, 'W': 89.60999999999999} +[19.33, 18.71, 18.84, 18.65, 18.46, 18.36, 18.45, 18.79, 18.56, 19.13, 18.86, 18.45, 18.67, 18.53, 18.54, 18.59, 18.65, 18.36, 18.37, 18.41] +334.845 +16.742250000000002 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7969, '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': 10.460268020629883, 'TIME_S_1KI': 1.3126199047094844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1273.458453361988, 'W': 89.60999999999999, 'J_1KI': 159.80153762855917, 'W_1KI': 11.244823691805745, 'W_D': 72.86774999999999, 'J_D': 1035.532331380069, 'W_D_1KI': 9.143901367800224, 'J_D_1KI': 1.1474339776383766} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json index d957b86..50d74f7 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12273, "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": 10.708429336547852, "TIME_S_1KI": 0.8725192973639575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.9024416732789, "W": 89.76, "J_1KI": 104.53046864444545, "W_1KI": 7.313615252994378, "W_D": 73.62625, "J_D": 1052.309446259439, "W_D_1KI": 5.999042613867839, "J_D_1KI": 0.48880001742588114} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12083, "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": 10.562071084976196, "TIME_S_1KI": 0.8741265484545391, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1286.1158259391784, "W": 90.07, "J_1KI": 106.44010808070664, "W_1KI": 7.454274600678639, "W_D": 72.984, "J_D": 1042.1436376190186, "W_D_1KI": 6.0402217992220475, "J_D_1KI": 0.4998942149484439} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output index 61cc75a..3dcd9da 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx'] -{"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": 0.855471134185791} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 0.10086560249328613} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.2921, 0.7848, 0.3759, ..., 0.1614, 0.6217, 0.8908]) +tensor([0.5085, 0.3637, 0.5513, ..., 0.0902, 0.3482, 0.8704]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 0.855471134185791 seconds +Time: 0.10086560249328613 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12273', '-m', 'matrices/389000+_cols/helm2d03.mtx'] -{"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": 10.708429336547852} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '10409', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 9.044915676116943} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0043, 0.4903, 0.3538, ..., 0.3528, 0.3455, 0.3342]) +tensor([0.1039, 0.7188, 0.0051, ..., 0.3636, 0.6710, 0.7462]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -42,7 +42,10 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.708429336547852 seconds +Time: 9.044915676116943 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12083', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 10.562071084976196} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.0043, 0.4903, 0.3538, ..., 0.3528, 0.3455, 0.3342]) +tensor([0.0286, 0.7769, 0.6625, ..., 0.1945, 0.5440, 0.1678]) Matrix Type: SuiteSparse Matrix: helm2d03 Matrix Format: csr @@ -62,13 +65,33 @@ Rows: 392257 Size: 153865554049 NNZ: 2741935 Density: 1.7820330332848923e-05 -Time: 10.708429336547852 seconds +Time: 10.562071084976196 seconds -[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75] -[89.76] -14.292585134506226 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, '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': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76} -[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75, 18.01, 17.48, 17.78, 17.28, 17.51, 17.82, 17.71, 17.91, 19.53, 17.77] -322.67499999999995 -16.13375 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, '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': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76, 'J_1KI': 104.53046864444545, 'W_1KI': 7.313615252994378, 'W_D': 73.62625, 'J_D': 1052.309446259439, 'W_D_1KI': 5.999042613867839, 'J_D_1KI': 0.48880001742588114} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0286, 0.7769, 0.6625, ..., 0.1945, 0.5440, 0.1678]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.562071084976196 seconds + +[19.3, 18.47, 19.21, 18.51, 18.85, 18.84, 19.09, 18.58, 18.92, 18.66] +[90.07] +14.279069900512695 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12083, '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': 10.562071084976196, 'TIME_S_1KI': 0.8741265484545391, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.1158259391784, 'W': 90.07} +[19.3, 18.47, 19.21, 18.51, 18.85, 18.84, 19.09, 18.58, 18.92, 18.66, 19.24, 19.31, 19.02, 18.49, 19.68, 18.44, 19.28, 18.51, 18.77, 22.3] +341.72 +17.086000000000002 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12083, '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': 10.562071084976196, 'TIME_S_1KI': 0.8741265484545391, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.1158259391784, 'W': 90.07, 'J_1KI': 106.44010808070664, 'W_1KI': 7.454274600678639, 'W_D': 72.984, 'J_D': 1042.1436376190186, 'W_D_1KI': 6.0402217992220475, 'J_D_1KI': 0.4998942149484439} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json index 51eb669..dd22985 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13367, "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": 10.647616863250732, "TIME_S_1KI": 0.7965599508678636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.2656374621392, "W": 89.59, "J_1KI": 95.32921653790224, "W_1KI": 6.702326625271191, "W_D": 73.54725, "J_D": 1046.0847572813632, "W_D_1KI": 5.502150819181567, "J_D_1KI": 0.4116219659745318} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13070, "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": 10.441888809204102, "TIME_S_1KI": 0.79892033735303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1270.8188339996339, "W": 89.92, "J_1KI": 97.23173940318546, "W_1KI": 6.879877582249426, "W_D": 72.68875, "J_D": 1027.2935111197828, "W_D_1KI": 5.561495791889824, "J_D_1KI": 0.42551612791811966} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output index 467022c..18f4bf5 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx'] -{"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": 0.785470724105835} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 0.09303474426269531} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6209, 0.8638, 0.8531, ..., 0.3261, 0.5283, 0.1956]) +tensor([0.8645, 0.7904, 0.0541, ..., 0.6524, 0.8012, 0.1352]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 0.785470724105835 seconds +Time: 0.09303474426269531 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13367', '-m', 'matrices/389000+_cols/language.mtx'] -{"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": 10.647616863250732} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '11286', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 9.066349983215332} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6025, 0.9576, 0.6646, ..., 0.8829, 0.1742, 0.2421]) +tensor([0.9324, 0.3964, 0.9102, ..., 0.8453, 0.2811, 0.9791]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -40,7 +40,10 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.647616863250732 seconds +Time: 9.066349983215332 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13070', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 10.441888809204102} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6025, 0.9576, 0.6646, ..., 0.8829, 0.1742, 0.2421]) +tensor([0.2796, 0.5979, 0.5303, ..., 0.2232, 0.5637, 0.4654]) Matrix Type: SuiteSparse Matrix: language Matrix Format: csr @@ -59,13 +62,32 @@ Rows: 399130 Size: 159304756900 NNZ: 1216334 Density: 7.635264782228233e-06 -Time: 10.647616863250732 seconds +Time: 10.441888809204102 seconds -[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87] -[89.59] -14.223302125930786 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, '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': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59} -[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87, 18.16, 17.58, 17.67, 17.88, 17.53, 17.55, 17.46, 18.42, 17.89, 18.4] -320.855 -16.04275 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, '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': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59, 'J_1KI': 95.32921653790224, 'W_1KI': 6.702326625271191, 'W_D': 73.54725, 'J_D': 1046.0847572813632, 'W_D_1KI': 5.502150819181567, 'J_D_1KI': 0.4116219659745318} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2796, 0.5979, 0.5303, ..., 0.2232, 0.5637, 0.4654]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.441888809204102 seconds + +[19.05, 19.23, 18.52, 19.83, 18.72, 18.33, 18.49, 18.65, 22.46, 18.97] +[89.92] +14.132771730422974 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13070, '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': 10.441888809204102, 'TIME_S_1KI': 0.79892033735303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1270.8188339996339, 'W': 89.92} +[19.05, 19.23, 18.52, 19.83, 18.72, 18.33, 18.49, 18.65, 22.46, 18.97, 18.79, 18.42, 18.53, 18.39, 22.69, 18.8, 18.55, 19.02, 18.31, 18.56] +344.625 +17.23125 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13070, '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': 10.441888809204102, 'TIME_S_1KI': 0.79892033735303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1270.8188339996339, 'W': 89.92, 'J_1KI': 97.23173940318546, 'W_1KI': 6.879877582249426, 'W_D': 72.68875, 'J_D': 1027.2935111197828, 'W_D_1KI': 5.561495791889824, 'J_D_1KI': 0.42551612791811966} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json index 735b097..be4cd73 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5913, "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": 10.544729232788086, "TIME_S_1KI": 1.783312909316436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.8479092025757, "W": 89.36, "J_1KI": 219.49059854601313, "W_1KI": 15.112464062235752, "W_D": 73.2365, "J_D": 1063.6732139918804, "W_D_1KI": 12.385675629967869, "J_D_1KI": 2.094651721624872} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5844, "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": 10.486568212509155, "TIME_S_1KI": 1.7944161896832915, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1290.2744817972184, "W": 89.54, "J_1KI": 220.78618785031114, "W_1KI": 15.321697467488022, "W_D": 72.66325, "J_D": 1047.0799334314465, "W_D_1KI": 12.433821013004794, "J_D_1KI": 2.127621665469677} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output index 41b8f99..439b928 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx'] -{"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": 1.7754507064819336} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 0.1927320957183838} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.1497, 0.9722, 0.3177, ..., 0.4783, 0.5290, 0.8575]) +tensor([0.0087, 0.0382, 0.0045, ..., 0.7481, 0.3346, 0.8339]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 1.7754507064819336 seconds +Time: 0.1927320957183838 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5913', '-m', 'matrices/389000+_cols/marine1.mtx'] -{"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": 10.544729232788086} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5447', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 9.786617279052734} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3921, 0.4534, 0.1657, ..., 0.0431, 0.7782, 0.5404]) +tensor([0.6583, 0.4574, 0.0542, ..., 0.0890, 0.7186, 0.4180]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -42,7 +42,10 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.544729232788086 seconds +Time: 9.786617279052734 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5844', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 10.486568212509155} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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, 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([0.3921, 0.4534, 0.1657, ..., 0.0431, 0.7782, 0.5404]) +tensor([0.3839, 0.0049, 0.8086, ..., 0.8018, 0.6206, 0.4463]) Matrix Type: SuiteSparse Matrix: marine1 Matrix Format: csr @@ -62,13 +65,33 @@ Rows: 400320 Size: 160256102400 NNZ: 6226538 Density: 3.885367175883594e-05 -Time: 10.544729232788086 seconds +Time: 10.486568212509155 seconds -[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7] -[89.36] -14.523812770843506 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, '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': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36} -[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7, 18.27, 17.88, 17.72, 17.88, 17.99, 17.97, 17.75, 18.36, 18.01, 17.9] -322.46999999999997 -16.1235 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, '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': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36, 'J_1KI': 219.49059854601313, 'W_1KI': 15.112464062235752, 'W_D': 73.2365, 'J_D': 1063.6732139918804, 'W_D_1KI': 12.385675629967869, 'J_D_1KI': 2.094651721624872} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3839, 0.0049, 0.8086, ..., 0.8018, 0.6206, 0.4463]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.486568212509155 seconds + +[18.93, 18.84, 18.48, 18.73, 18.57, 18.75, 18.76, 18.54, 18.53, 18.82] +[89.54] +14.410034418106079 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5844, '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': 10.486568212509155, 'TIME_S_1KI': 1.7944161896832915, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.2744817972184, 'W': 89.54} +[18.93, 18.84, 18.48, 18.73, 18.57, 18.75, 18.76, 18.54, 18.53, 18.82, 19.02, 18.48, 18.77, 18.74, 18.54, 18.53, 18.63, 19.36, 19.52, 18.76] +337.53499999999997 +16.876749999999998 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5844, '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': 10.486568212509155, 'TIME_S_1KI': 1.7944161896832915, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.2744817972184, 'W': 89.54, 'J_1KI': 220.78618785031114, 'W_1KI': 15.321697467488022, 'W_D': 72.66325, 'J_D': 1047.0799334314465, 'W_D_1KI': 12.433821013004794, 'J_D_1KI': 2.127621665469677} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json index 6bd73ad..385de8a 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13717, "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": 10.75633430480957, "TIME_S_1KI": 0.7841608445585456, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.2889261174203, "W": 89.67, "J_1KI": 94.72107065082892, "W_1KI": 6.537143690311293, "W_D": 73.277, "J_D": 1061.7597260968685, "W_D_1KI": 5.342057301159145, "J_D_1KI": 0.3894479333060542} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13300, "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": 10.529933452606201, "TIME_S_1KI": 0.7917243197448272, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1286.9559070491791, "W": 90.26, "J_1KI": 96.76360203377287, "W_1KI": 6.786466165413534, "W_D": 73.33850000000001, "J_D": 1045.6837556960584, "W_D_1KI": 5.514172932330827, "J_D_1KI": 0.4145994685963028} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output index c3613ad..b0ca139 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx'] -{"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": 0.7654633522033691} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 0.09129667282104492} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4671, 0.2005, 0.4838, ..., 0.7591, 0.5753, 0.8077]) +tensor([0.4738, 0.5556, 0.7324, ..., 0.8329, 0.5882, 0.0054]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -18,10 +18,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 0.7654633522033691 seconds +Time: 0.09129667282104492 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13717', '-m', 'matrices/389000+_cols/mario002.mtx'] -{"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": 10.75633430480957} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '11500', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 9.07878065109253} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.1039, 0.6560, 0.4015, ..., 0.5631, 0.0997, 0.1433]) +tensor([0.3050, 0.4022, 0.2951, ..., 0.0929, 0.0757, 0.4211]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -40,7 +40,10 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.75633430480957 seconds +Time: 9.07878065109253 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13300', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 10.529933452606201} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.1039, 0.6560, 0.4015, ..., 0.5631, 0.0997, 0.1433]) +tensor([0.0848, 0.1263, 0.2212, ..., 0.3920, 0.2718, 0.7713]) Matrix Type: SuiteSparse Matrix: mario002 Matrix Format: csr @@ -59,13 +62,32 @@ Rows: 389874 Size: 152001735876 NNZ: 2101242 Density: 1.3823802655215408e-05 -Time: 10.75633430480957 seconds +Time: 10.529933452606201 seconds -[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27] -[89.67] -14.489672422409058 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, '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': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67} -[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27, 22.42, 17.61, 17.63, 17.67, 17.98, 17.5, 17.66, 18.19, 17.57, 17.62] -327.86 -16.393 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, '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': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67, 'J_1KI': 94.72107065082892, 'W_1KI': 6.537143690311293, 'W_D': 73.277, 'J_D': 1061.7597260968685, 'W_D_1KI': 5.342057301159145, 'J_D_1KI': 0.3894479333060542} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0848, 0.1263, 0.2212, ..., 0.3920, 0.2718, 0.7713]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.529933452606201 seconds + +[18.79, 18.64, 19.07, 18.52, 18.59, 18.51, 18.8, 18.52, 18.66, 18.42] +[90.26] +14.25831937789917 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13300, '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': 10.529933452606201, 'TIME_S_1KI': 0.7917243197448272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.9559070491791, 'W': 90.26} +[18.79, 18.64, 19.07, 18.52, 18.59, 18.51, 18.8, 18.52, 18.66, 18.42, 19.47, 19.56, 18.62, 19.35, 18.8, 18.37, 18.44, 18.4, 19.39, 19.7] +338.42999999999995 +16.921499999999998 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13300, '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': 10.529933452606201, 'TIME_S_1KI': 0.7917243197448272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.9559070491791, 'W': 90.26, 'J_1KI': 96.76360203377287, 'W_1KI': 6.786466165413534, 'W_D': 73.33850000000001, 'J_D': 1045.6837556960584, 'W_D_1KI': 5.514172932330827, 'J_D_1KI': 0.4145994685963028} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.json index f2c89e5..67eebdd 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1379, "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": 10.283698081970215, "TIME_S_1KI": 7.457359015206827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2003.8670825815202, "W": 66.59, "J_1KI": 1453.1305892541843, "W_1KI": 48.288614938361135, "W_D": 49.53425, "J_D": 1490.615002783656, "W_D_1KI": 35.920413343002174, "J_D_1KI": 26.04816050979128} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1361, "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": 10.109994173049927, "TIME_S_1KI": 7.428357217523826, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.268822283745, "W": 66.51, "J_1KI": 1456.479663691216, "W_1KI": 48.86847905951507, "W_D": 49.65675, "J_D": 1479.9733474806549, "W_D_1KI": 36.48548861131521, "J_D_1KI": 26.807853498394717} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.output index 1e66417..373bef3 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_msdoor.output @@ -1,5 +1,5 @@ ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 1.2347450256347656} +{"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": 1.2034754753112793} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.4403, 0.7085, 0.5973, ..., 0.5516, 0.0584, 0.9044]) +tensor([0.1176, 0.8686, 0.2824, ..., 0.3331, 0.7981, 0.1656]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 1.2347450256347656 seconds +Time: 1.2034754753112793 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '850', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 6.469892501831055} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '872', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 6.726288318634033} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.6729, 0.8216, 0.8655, ..., 0.6622, 0.9214, 0.0755]) +tensor([0.3845, 0.9586, 0.5179, ..., 0.3427, 0.9150, 0.7393]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -42,10 +42,10 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 6.469892501831055 seconds +Time: 6.726288318634033 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1379', '-m', 'matrices/389000+_cols/msdoor.mtx'] -{"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": 10.283698081970215} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1361', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 10.109994173049927} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9393, 0.6181, 0.5648, ..., 0.9318, 0.7600, 0.4697]) +tensor([0.6981, 0.2673, 0.9239, ..., 0.2995, 0.1197, 0.8968]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -65,7 +65,7 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.283698081970215 seconds +Time: 10.109994173049927 seconds /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9393, 0.6181, 0.5648, ..., 0.9318, 0.7600, 0.4697]) +tensor([0.6981, 0.2673, 0.9239, ..., 0.2995, 0.1197, 0.8968]) Matrix Type: SuiteSparse Matrix: msdoor Matrix Format: csr @@ -85,13 +85,13 @@ Rows: 415863 Size: 172942034769 NNZ: 20240935 Density: 0.00011703883921012015 -Time: 10.283698081970215 seconds +Time: 10.109994173049927 seconds -[19.74, 19.62, 18.69, 18.65, 18.95, 18.9, 18.69, 18.4, 18.92, 18.85] -[66.59] -30.092612743377686 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1379, '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': 10.283698081970215, 'TIME_S_1KI': 7.457359015206827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2003.8670825815202, 'W': 66.59} -[19.74, 19.62, 18.69, 18.65, 18.95, 18.9, 18.69, 18.4, 18.92, 18.85, 19.77, 18.65, 19.56, 18.53, 18.44, 18.48, 18.72, 20.39, 19.13, 18.43] -341.115 -17.05575 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1379, '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': 10.283698081970215, 'TIME_S_1KI': 7.457359015206827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2003.8670825815202, 'W': 66.59, 'J_1KI': 1453.1305892541843, 'W_1KI': 48.288614938361135, 'W_D': 49.53425, 'J_D': 1490.615002783656, 'W_D_1KI': 35.920413343002174, 'J_D_1KI': 26.04816050979128} +[19.93, 18.53, 18.56, 18.41, 19.05, 18.63, 18.7, 18.57, 19.18, 18.5] +[66.51] +29.80407190322876 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1361, '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': 10.109994173049927, 'TIME_S_1KI': 7.428357217523826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.268822283745, 'W': 66.51} +[19.93, 18.53, 18.56, 18.41, 19.05, 18.63, 18.7, 18.57, 19.18, 18.5, 19.95, 18.55, 19.03, 18.6, 18.4, 18.49, 18.92, 18.56, 18.35, 18.69] +337.065 +16.85325 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1361, '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': 10.109994173049927, 'TIME_S_1KI': 7.428357217523826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.268822283745, 'W': 66.51, 'J_1KI': 1456.479663691216, 'W_1KI': 48.86847905951507, 'W_D': 49.65675, 'J_D': 1479.9733474806549, 'W_D_1KI': 36.48548861131521, 'J_D_1KI': 26.807853498394717} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json index 54a5eaf..2d0c600 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1798, "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": 10.02889084815979, "TIME_S_1KI": 5.577803586295767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.981861114502, "W": 84.07, "J_1KI": 764.1723365486662, "W_1KI": 46.75750834260289, "W_D": 67.9785, "J_D": 1110.9935285568238, "W_D_1KI": 37.80784204671858, "J_D_1KI": 21.027720826873512} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1805, "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": 10.747247457504272, "TIME_S_1KI": 5.954153716068849, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1410.3635589265823, "W": 86.03, "J_1KI": 781.3648525909043, "W_1KI": 47.662049861495845, "W_D": 68.94125, "J_D": 1130.2130269306897, "W_D_1KI": 38.19459833795014, "J_D_1KI": 21.16044229249315} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output index 1a5d3f5..9ab5d2f 100644 --- a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output @@ -1,5 +1,5 @@ -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx'] -{"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": 5.838165521621704} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 0.6256942749023438} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.7305, 0.5169, 0.5807, ..., 0.4360, 0.1397, 0.7206]) +tensor([0.6695, 0.2218, 0.1299, ..., 0.1192, 0.3969, 0.5315]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -19,10 +19,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 5.838165521621704 seconds +Time: 0.6256942749023438 seconds -['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1798', '-m', 'matrices/389000+_cols/test1.mtx'] -{"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": 10.02889084815979} +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1678', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 9.75907301902771} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9760, 0.7722, 0.6451, ..., 0.4999, 0.6092, 0.1757]) +tensor([0.9208, 0.2681, 0.3717, ..., 0.5353, 0.8632, 0.3544]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -42,7 +42,10 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 10.02889084815979 seconds +Time: 9.75907301902771 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1805', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 10.747247457504272} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.9760, 0.7722, 0.6451, ..., 0.4999, 0.6092, 0.1757]) +tensor([0.8674, 0.3420, 0.2989, ..., 0.5091, 0.5046, 0.7986]) Matrix Type: SuiteSparse Matrix: test1 Matrix Format: csr @@ -62,13 +65,33 @@ Rows: 392908 Size: 154376696464 NNZ: 12968200 Density: 8.400361127706946e-05 -Time: 10.02889084815979 seconds +Time: 10.747247457504272 seconds -[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74] -[84.07] -16.343307495117188 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, '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': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07} -[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74, 18.31, 17.88, 17.67, 17.64, 18.57, 17.92, 17.84, 17.7, 17.61, 17.81] -321.83000000000004 -16.091500000000003 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, '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': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07, 'J_1KI': 764.1723365486662, 'W_1KI': 46.75750834260289, 'W_D': 67.9785, 'J_D': 1110.9935285568238, 'W_D_1KI': 37.80784204671858, 'J_D_1KI': 21.027720826873512} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8674, 0.3420, 0.2989, ..., 0.5091, 0.5046, 0.7986]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.747247457504272 seconds + +[18.97, 18.86, 18.58, 19.56, 18.45, 19.76, 18.48, 18.48, 18.99, 18.51] +[86.03] +16.39385747909546 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1805, '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': 10.747247457504272, 'TIME_S_1KI': 5.954153716068849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.3635589265823, 'W': 86.03} +[18.97, 18.86, 18.58, 19.56, 18.45, 19.76, 18.48, 18.48, 18.99, 18.51, 18.86, 18.56, 18.58, 18.55, 22.91, 18.68, 18.58, 18.79, 18.56, 18.47] +341.775 +17.088749999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1805, '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': 10.747247457504272, 'TIME_S_1KI': 5.954153716068849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.3635589265823, 'W': 86.03, 'J_1KI': 781.3648525909043, 'W_1KI': 47.662049861495845, 'W_D': 68.94125, 'J_D': 1130.2130269306897, 'W_D_1KI': 38.19459833795014, 'J_D_1KI': 21.16044229249315} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..5fe67db --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 30.323144912719727, "TIME_S_1KI": 30.323144912719727, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2708.072883968354, "W": 77.40203729318671, "J_1KI": 2708.072883968354, "W_1KI": 77.40203729318671, "W_D": 53.61603729318671, "J_D": 1875.8697033972746, "W_D_1KI": 53.61603729318671, "J_D_1KI": 53.61603729318671} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..c77cfff --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_amazon0312.output @@ -0,0 +1,49 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx'] +{"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": 30.323144912719727} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9519, 0.7886, 0.4122, ..., 0.0191, 0.4041, 0.8787]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 30.323144912719727 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9519, 0.7886, 0.4122, ..., 0.0191, 0.4041, 0.8787]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 30.323144912719727 seconds + +[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56] +[26.56, 26.64, 26.44, 29.44, 30.6, 36.2, 50.64, 63.2, 76.56, 90.36, 96.08, 94.84, 94.28, 93.0, 94.08, 94.08, 94.76, 95.56, 97.52, 96.08, 95.76, 91.48, 89.72, 90.12, 89.92, 89.6, 90.64, 91.72, 90.48, 91.12, 89.6, 89.6, 90.12, 90.48] +34.98710083961487 +{'CPU': 'Altra', 'CORES': 80, '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': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671} +[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56, 26.8, 26.84, 26.76, 26.72, 26.52, 26.44, 26.32, 26.16, 26.0, 26.0] +475.72 +23.786 +{'CPU': 'Altra', 'CORES': 80, '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': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671, 'J_1KI': 2708.072883968354, 'W_1KI': 77.40203729318671, 'W_D': 53.61603729318671, 'J_D': 1875.8697033972746, 'W_D_1KI': 53.61603729318671, 'J_D_1KI': 53.61603729318671} diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..1f7ee40 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 21.815975189208984, "TIME_S_1KI": 21.815975189208984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.1918925571438, "W": 74.64919082171829, "J_1KI": 1916.1918925571438, "W_1KI": 74.64919082171829, "W_D": 51.65619082171829, "J_D": 1325.9778567373746, "W_D_1KI": 51.65619082171829, "J_D_1KI": 51.65619082171829} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..256e384 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_helm2d03.output @@ -0,0 +1,51 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx'] +{"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": 21.815975189208984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9058, 0.4925, 0.9859, ..., 0.1438, 0.2004, 0.4986]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 21.815975189208984 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9058, 0.4925, 0.9859, ..., 0.1438, 0.2004, 0.4986]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 21.815975189208984 seconds + +[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16] +[25.0, 24.96, 25.32, 27.72, 29.24, 40.8, 57.48, 70.72, 86.32, 97.72, 97.72, 96.24, 97.0, 95.72, 95.32, 93.32, 95.8, 96.36, 95.36, 95.72, 94.88, 94.16, 93.92, 92.68, 93.4] +25.669292211532593 +{'CPU': 'Altra', 'CORES': 80, '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': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829} +[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16, 25.44, 25.44, 25.28, 25.52, 25.88, 25.84, 25.68, 25.88, 25.8, 25.52] +459.86 +22.993000000000002 +{'CPU': 'Altra', 'CORES': 80, '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': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829, 'J_1KI': 1916.1918925571438, 'W_1KI': 74.64919082171829, 'W_D': 51.65619082171829, 'J_D': 1325.9778567373746, 'W_D_1KI': 51.65619082171829, 'J_D_1KI': 51.65619082171829} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.json new file mode 100644 index 0000000..6674d3c --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 11.040250062942505, "TIME_S_1KI": 11.040250062942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1365.697071151733, "W": 66.06671490426629, "J_1KI": 1365.697071151733, "W_1KI": 66.06671490426629, "W_D": 44.15271490426629, "J_D": 912.7021604681013, "W_D_1KI": 44.15271490426629, "J_D_1KI": 44.15271490426629} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.output new file mode 100644 index 0000000..8666779 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_language.output @@ -0,0 +1,49 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx'] +{"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": 11.040250062942505} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.4838, 0.0445, 0.9105, ..., 0.8272, 0.1700, 0.2253]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 11.040250062942505 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.4838, 0.0445, 0.9105, ..., 0.8272, 0.1700, 0.2253]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 11.040250062942505 seconds + +[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12] +[24.28, 24.56, 24.56, 28.8, 30.88, 44.64, 59.0, 72.44, 83.72, 90.96, 88.6, 88.6, 85.88, 86.24, 86.16, 87.16, 86.96, 88.8, 89.32, 90.72] +20.67148447036743 +{'CPU': 'Altra', 'CORES': 80, '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': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629} +[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12, 24.64, 24.56, 24.68, 24.52, 24.56, 24.64, 24.8, 24.72, 24.72, 24.6] +438.28 +21.913999999999998 +{'CPU': 'Altra', 'CORES': 80, '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': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629, 'J_1KI': 1365.697071151733, 'W_1KI': 66.06671490426629, 'W_D': 44.15271490426629, 'J_D': 912.7021604681013, 'W_D_1KI': 44.15271490426629, 'J_D_1KI': 44.15271490426629} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.json new file mode 100644 index 0000000..44a4ce9 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 47.31629490852356, "TIME_S_1KI": 47.31629490852356, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3588.3839783859257, "W": 77.04772778103, "J_1KI": 3588.3839783859257, "W_1KI": 77.04772778103, "W_D": 53.199727781030006, "J_D": 2477.698646305085, "W_D_1KI": 53.199727781030006, "J_D_1KI": 53.199727781030006} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.output new file mode 100644 index 0000000..1934d42 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_marine1.output @@ -0,0 +1,51 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx'] +{"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": 47.31629490852356} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9557, 0.7622, 0.1453, ..., 0.2002, 0.7167, 0.8732]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 47.31629490852356 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.9557, 0.7622, 0.1453, ..., 0.2002, 0.7167, 0.8732]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 47.31629490852356 seconds + +[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04] +[26.4, 26.6, 26.72, 27.8, 29.68, 36.68, 47.12, 59.56, 71.44, 82.04, 90.0, 90.2, 89.8, 91.64, 91.36, 89.28, 89.28, 88.72, 90.36, 90.56, 88.08, 89.32, 91.24, 89.92, 91.16, 94.4, 94.0, 92.04, 91.28, 91.0, 89.92, 89.76, 89.76, 89.8, 90.52, 90.56, 92.08, 91.72, 90.16, 89.0, 89.52, 88.96, 88.76, 89.0, 87.92] +46.57352113723755 +{'CPU': 'Altra', 'CORES': 80, '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': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103} +[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04, 26.76, 26.48, 26.44, 26.48, 26.72, 26.72, 26.92, 26.92, 27.0, 27.0] +476.9599999999999 +23.847999999999995 +{'CPU': 'Altra', 'CORES': 80, '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': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103, 'J_1KI': 3588.3839783859257, 'W_1KI': 77.04772778103, 'W_D': 53.199727781030006, 'J_D': 2477.698646305085, 'W_D_1KI': 53.199727781030006, 'J_D_1KI': 53.199727781030006} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.json new file mode 100644 index 0000000..2a1d024 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 26.970608472824097, "TIME_S_1KI": 26.970608472824097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1533.6805934524534, "W": 70.7905728448904, "J_1KI": 1533.6805934524534, "W_1KI": 70.7905728448904, "W_D": 46.71357284489041, "J_D": 1012.0514249830246, "W_D_1KI": 46.71357284489041, "J_D_1KI": 46.71357284489041} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.output new file mode 100644 index 0000000..34cf597 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_mario002.output @@ -0,0 +1,49 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx'] +{"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": 26.970608472824097} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0717, 0.4634, 0.0880, ..., 0.8346, 0.7497, 0.2295]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 26.970608472824097 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0717, 0.4634, 0.0880, ..., 0.8346, 0.7497, 0.2295]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 26.970608472824097 seconds + +[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44] +[26.12, 26.4, 26.44, 27.4, 29.4, 37.08, 54.84, 69.0, 83.6, 95.56, 98.68, 97.52, 97.96, 97.76, 97.76, 97.8, 98.4, 95.2, 93.56, 91.64, 89.8] +21.665040016174316 +{'CPU': 'Altra', 'CORES': 80, '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': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904} +[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44, 26.88, 26.64, 26.8, 27.08, 27.0, 26.92, 26.68, 26.44, 26.32, 26.32] +481.53999999999996 +24.076999999999998 +{'CPU': 'Altra', 'CORES': 80, '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': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904, 'J_1KI': 1533.6805934524534, 'W_1KI': 70.7905728448904, 'W_D': 46.71357284489041, 'J_D': 1012.0514249830246, 'W_D_1KI': 46.71357284489041, 'J_D_1KI': 46.71357284489041} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..e644ee7 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "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": 11.470221519470215, "TIME_S_1KI": 114.70221519470215, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 957.8876025772095, "W": 54.677060171971085, "J_1KI": 9578.876025772095, "W_1KI": 546.7706017197108, "W_D": 36.076060171971086, "J_D": 632.016620496273, "W_D_1KI": 360.76060171971085, "J_D_1KI": 3607.6060171971085} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..9a31fd5 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_msdoor.output @@ -0,0 +1,51 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/389000+_cols/msdoor.mtx'] +{"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": 11.470221519470215} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0348, 0.4239, 0.1578, ..., 0.9250, 0.1408, 0.2700]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.470221519470215 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.0348, 0.4239, 0.1578, ..., 0.9250, 0.1408, 0.2700]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.470221519470215 seconds + +[20.56, 20.56, 20.52, 20.48, 20.68, 20.68, 20.56, 20.6, 20.64, 20.56] +[20.56, 20.64, 21.64, 22.44, 26.08, 26.08, 35.0, 38.96, 51.72, 68.64, 76.08, 87.88, 94.36, 93.12, 94.56, 93.48, 95.12] +17.51900339126587 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 11.470221519470215, 'TIME_S_1KI': 114.70221519470215, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 957.8876025772095, 'W': 54.677060171971085} +[20.56, 20.56, 20.52, 20.48, 20.68, 20.68, 20.56, 20.6, 20.64, 20.56, 20.84, 20.56, 20.68, 20.64, 20.6, 20.6, 20.88, 20.96, 20.96, 20.88] +372.02 +18.601 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, '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': 11.470221519470215, 'TIME_S_1KI': 114.70221519470215, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 957.8876025772095, 'W': 54.677060171971085, 'J_1KI': 9578.876025772095, 'W_1KI': 546.7706017197108, 'W_D': 36.076060171971086, 'J_D': 632.016620496273, 'W_D_1KI': 360.76060171971085, 'J_D_1KI': 3607.6060171971085} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.json new file mode 100644 index 0000000..a64dfeb --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "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": 92.92496466636658, "TIME_S_1KI": 92.92496466636658, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8599.791397418978, "W": 85.10134779265783, "J_1KI": 8599.791397418978, "W_1KI": 85.10134779265783, "W_D": 60.408347792657835, "J_D": 6104.4766405498995, "W_D_1KI": 60.408347792657835, "J_D_1KI": 60.408347792657835} diff --git a/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.output new file mode 100644 index 0000000..6caaca3 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/altra_max_csr_10_10_10_test1.output @@ -0,0 +1,51 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx'] +{"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": 92.92496466636658} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1407, 0.4697, 0.2203, ..., 0.3353, 0.2584, 0.9591]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 92.92496466636658 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([0.1407, 0.4697, 0.2203, ..., 0.3353, 0.2584, 0.9591]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 92.92496466636658 seconds + +[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64] +[26.8, 26.88, 30.16, 31.04, 31.04, 35.8, 41.52, 47.96, 57.12, 73.0, 77.8, 92.16, 95.08, 96.56, 94.84, 94.48, 94.88, 92.8, 91.28, 91.28, 91.72, 91.6, 89.08, 89.24, 90.12, 89.76, 89.48, 89.84, 90.24, 88.68, 88.6, 90.24, 88.36, 90.44, 90.44, 93.28, 93.6, 95.92, 95.52, 91.44, 91.84, 89.96, 91.0, 91.04, 90.84, 90.68, 88.92, 93.0, 95.6, 97.32, 97.32, 98.32, 96.56, 92.48, 91.04, 92.0, 91.72, 94.24, 92.8, 92.52, 91.64, 93.04, 93.56, 98.0, 99.0, 97.0, 97.0, 94.36, 93.2, 93.0, 91.36, 92.44, 95.16, 96.28, 96.76, 96.4, 97.92, 92.76, 93.72, 94.64, 95.76, 96.08, 96.08, 99.8, 99.2, 97.52, 97.16, 96.92, 97.2, 99.08, 99.0, 97.56, 97.8, 97.16, 97.4, 98.52, 99.92, 98.48] +101.05352759361267 +{'CPU': 'Altra', 'CORES': 80, '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': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783} +[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64, 27.96, 27.76, 27.8, 27.8, 27.96, 28.08, 28.04, 27.96, 27.76, 27.68] +493.86 +24.693 +{'CPU': 'Altra', 'CORES': 80, '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': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783, 'J_1KI': 8599.791397418978, 'W_1KI': 85.10134779265783, 'W_D': 60.408347792657835, 'J_D': 6104.4766405498995, 'W_D_1KI': 60.408347792657835, 'J_D_1KI': 60.408347792657835} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..9573140 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19947, "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": 10.311090230941772, "TIME_S_1KI": 0.5169243611040143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1933.8912408947945, "W": 148.79, "J_1KI": 96.951483475951, "W_1KI": 7.459267057702912, "W_D": 113.0615, "J_D": 1469.5116911917924, "W_D_1KI": 5.668095452950318, "J_D_1KI": 0.28415779079311765} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..2ce455e --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_amazon0312.output @@ -0,0 +1,93 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 0.5623607635498047} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4201, 0.2453, 0.6690, ..., 0.5318, 0.2145, 0.3171]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.5623607635498047 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18671', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 9.828076839447021} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8027, 0.1127, 0.8893, ..., 0.1900, 0.1655, 0.6380]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 9.828076839447021 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19947', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 10.311090230941772} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8053, 0.3778, 0.3769, ..., 0.3826, 0.6227, 0.7489]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.311090230941772 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8053, 0.3778, 0.3769, ..., 0.3826, 0.6227, 0.7489]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.311090230941772 seconds + +[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21] +[148.79] +12.997454404830933 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, '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': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79} +[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21, 39.98, 39.27, 40.58, 39.91, 39.77, 39.38, 39.3, 39.5, 39.26, 39.23] +714.5699999999999 +35.7285 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, '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': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79, 'J_1KI': 96.951483475951, 'W_1KI': 7.459267057702912, 'W_D': 113.0615, 'J_D': 1469.5116911917924, 'W_D_1KI': 5.668095452950318, 'J_D_1KI': 0.28415779079311765} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..f538d2a --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30516, "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": 10.102073907852173, "TIME_S_1KI": 0.3310418766500253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1905.9418558597565, "W": 149.24, "J_1KI": 62.45713251604917, "W_1KI": 4.89054922008127, "W_D": 113.06250000000001, "J_D": 1443.9195328205826, "W_D_1KI": 3.7050235941801026, "J_D_1KI": 0.12141249161686009} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..e5c9a6c --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_helm2d03.output @@ -0,0 +1,97 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 0.3914318084716797} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2679, 0.5598, 0.6944, ..., 0.1144, 0.4933, 0.2716]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.3914318084716797 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26824', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 9.229604244232178} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3693, 0.3607, 0.3383, ..., 0.8476, 0.1262, 0.7740]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 9.229604244232178 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30516', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 10.102073907852173} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9962, 0.9854, 0.1629, ..., 0.5690, 0.4270, 0.4262]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.102073907852173 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9962, 0.9854, 0.1629, ..., 0.5690, 0.4270, 0.4262]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.102073907852173 seconds + +[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31] +[149.24] +12.77098536491394 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, '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': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24} +[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31, 39.88, 39.67, 44.75, 39.75, 39.19, 39.36, 39.18, 39.31, 39.57, 39.06] +723.55 +36.177499999999995 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, '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': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24, 'J_1KI': 62.45713251604917, 'W_1KI': 4.89054922008127, 'W_D': 113.06250000000001, 'J_D': 1443.9195328205826, 'W_D_1KI': 3.7050235941801026, 'J_D_1KI': 0.12141249161686009} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.json new file mode 100644 index 0000000..0278cd1 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31490, "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": 10.491079330444336, "TIME_S_1KI": 0.33315590125259875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1840.929758644104, "W": 139.7, "J_1KI": 58.46077353585595, "W_1KI": 4.436328993331216, "W_D": 104.24499999999999, "J_D": 1373.713118753433, "W_D_1KI": 3.310416005080978, "J_D_1KI": 0.10512594490571539} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.output new file mode 100644 index 0000000..47af1b0 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_language.output @@ -0,0 +1,93 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 0.366832971572876} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4489, 0.3341, 0.6321, ..., 0.9617, 0.3616, 0.1355]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.366832971572876 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28623', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 9.543859958648682} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6001, 0.9333, 0.1063, ..., 0.8124, 0.4889, 0.2196]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 9.543859958648682 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31490', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 10.491079330444336} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8719, 0.5932, 0.6007, ..., 0.1184, 0.6398, 0.5112]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.491079330444336 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8719, 0.5932, 0.6007, ..., 0.1184, 0.6398, 0.5112]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.491079330444336 seconds + +[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92] +[139.7] +13.177736282348633 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, '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': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7} +[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92, 40.13, 40.06, 39.2, 39.24, 39.63, 39.8, 39.04, 39.77, 39.24, 39.0] +709.1 +35.455 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, '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': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7, 'J_1KI': 58.46077353585595, 'W_1KI': 4.436328993331216, 'W_D': 104.24499999999999, 'J_D': 1373.713118753433, 'W_D_1KI': 3.310416005080978, 'J_D_1KI': 0.10512594490571539} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.json new file mode 100644 index 0000000..d48619d --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19504, "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": 10.365571975708008, "TIME_S_1KI": 0.5314587764411407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2136.8143803691864, "W": 158.23, "J_1KI": 109.55775124944557, "W_1KI": 8.112694831829367, "W_D": 121.8915, "J_D": 1646.0817167716025, "W_D_1KI": 6.249564191960623, "J_D_1KI": 0.3204247432301386} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.output new file mode 100644 index 0000000..5ccf432 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_marine1.output @@ -0,0 +1,97 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 0.6027348041534424} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5488, 0.1373, 0.1334, ..., 0.6361, 0.1287, 0.1871]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 0.6027348041534424 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17420', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 9.3778395652771} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5314, 0.3776, 0.6404, ..., 0.2799, 0.5486, 0.5791]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 9.3778395652771 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19504', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 10.365571975708008} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7619, 0.3106, 0.0542, ..., 0.9939, 0.8451, 0.6161]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.365571975708008 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7619, 0.3106, 0.0542, ..., 0.9939, 0.8451, 0.6161]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.365571975708008 seconds + +[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37] +[158.23] +13.504483222961426 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, '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': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23} +[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37, 40.1, 41.46, 44.99, 39.34, 39.95, 39.5, 40.61, 39.29, 39.33, 39.27] +726.77 +36.338499999999996 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, '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': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23, 'J_1KI': 109.55775124944557, 'W_1KI': 8.112694831829367, 'W_D': 121.8915, 'J_D': 1646.0817167716025, 'W_D_1KI': 6.249564191960623, 'J_D_1KI': 0.3204247432301386} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.json new file mode 100644 index 0000000..69c4b0e --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 24972, "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": 10.226792573928833, "TIME_S_1KI": 0.4095303769793702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.3517652535438, "W": 140.07, "J_1KI": 66.68876202360819, "W_1KI": 5.609082172032676, "W_D": 104.13924999999999, "J_D": 1238.155806523025, "W_D_1KI": 4.170240669549895, "J_D_1KI": 0.16699666304460578} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.output new file mode 100644 index 0000000..0369c8f --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_mario002.output @@ -0,0 +1,71 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 0.4204576015472412} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4700, 0.6519, 0.6666, ..., 0.7659, 0.1482, 0.5452]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.4204576015472412 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24972', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 10.226792573928833} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0699, 0.8434, 0.7786, ..., 0.4343, 0.2465, 0.4017]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.226792573928833 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0699, 0.8434, 0.7786, ..., 0.4343, 0.2465, 0.4017]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.226792573928833 seconds + +[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87] +[140.07] +11.889425039291382 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, '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': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07} +[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87, 39.88, 39.14, 39.13, 39.09, 39.22, 39.35, 39.38, 39.37, 40.66, 59.78] +718.615 +35.93075 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, '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': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07, 'J_1KI': 66.68876202360819, 'W_1KI': 5.609082172032676, 'W_D': 104.13924999999999, 'J_D': 1238.155806523025, 'W_D_1KI': 4.170240669549895, 'J_D_1KI': 0.16699666304460578} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..76268cb --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2238, "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": 11.481182098388672, "TIME_S_1KI": 5.130108176223714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1893.635374045372, "W": 130.54, "J_1KI": 846.1284066333208, "W_1KI": 58.32886505808758, "W_D": 94.69824999999999, "J_D": 1373.7088713052867, "W_D_1KI": 42.313784629133146, "J_D_1KI": 18.90696364125699} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..aa659f8 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_msdoor.output @@ -0,0 +1,120 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 0.544938325881958} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0497, 0.3572, 0.9272, ..., 0.8625, 0.2792, 0.5285]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 0.544938325881958 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1926', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 9.506705045700073} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9110, 0.2003, 0.2096, ..., 0.2036, 0.8917, 0.5683]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 9.506705045700073 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2127', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 9.97889494895935} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5968, 0.7420, 0.6758, ..., 0.0056, 0.9599, 0.3333]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 9.97889494895935 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2238', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 11.481182098388672} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1118, 0.5594, 0.9085, ..., 0.4616, 0.1638, 0.5506]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.481182098388672 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1118, 0.5594, 0.9085, ..., 0.4616, 0.1638, 0.5506]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.481182098388672 seconds + +[40.88, 39.48, 39.55, 40.68, 39.35, 39.35, 39.89, 39.72, 39.69, 39.48] +[130.54] +14.506169557571411 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2238, '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': 11.481182098388672, 'TIME_S_1KI': 5.130108176223714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1893.635374045372, 'W': 130.54} +[40.88, 39.48, 39.55, 40.68, 39.35, 39.35, 39.89, 39.72, 39.69, 39.48, 40.3, 39.34, 40.16, 39.39, 39.51, 39.33, 39.34, 39.93, 39.57, 44.45] +716.835 +35.841750000000005 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2238, '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': 11.481182098388672, 'TIME_S_1KI': 5.130108176223714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1893.635374045372, 'W': 130.54, 'J_1KI': 846.1284066333208, 'W_1KI': 58.32886505808758, 'W_D': 94.69824999999999, 'J_D': 1373.7088713052867, 'W_D_1KI': 42.313784629133146, 'J_D_1KI': 18.90696364125699} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.json new file mode 100644 index 0000000..f7d6df9 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2723, "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": 11.063719511032104, "TIME_S_1KI": 4.063062618814581, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1680.215021328926, "W": 125.38, "J_1KI": 617.045545842426, "W_1KI": 46.04480352552332, "W_D": 89.84949999999999, "J_D": 1204.071459235072, "W_D_1KI": 32.99651120088138, "J_D_1KI": 12.117705178436056} diff --git a/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.output new file mode 100644 index 0000000..a60431b --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/epyc_7313p_max_csr_10_10_10_test1.output @@ -0,0 +1,74 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 3.8556222915649414} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7510, 0.1721, 0.0746, ..., 0.5838, 0.0016, 0.2497]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 3.8556222915649414 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2723', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 11.063719511032104} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3646, 0.9778, 0.6678, ..., 0.8764, 0.3618, 0.1561]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 11.063719511032104 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3646, 0.9778, 0.6678, ..., 0.8764, 0.3618, 0.1561]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 11.063719511032104 seconds + +[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17] +[125.38] +13.400981187820435 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, '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': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38} +[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17, 40.93, 39.54, 39.73, 39.16, 39.13, 39.04, 39.13, 39.33, 39.0, 39.11] +710.61 +35.5305 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, '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': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38, 'J_1KI': 617.045545842426, 'W_1KI': 46.04480352552332, 'W_D': 89.84949999999999, 'J_D': 1204.071459235072, 'W_D_1KI': 32.99651120088138, 'J_D_1KI': 12.117705178436056} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.json new file mode 100644 index 0000000..17b55ec --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8225, "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": 10.766162395477295, "TIME_S_1KI": 1.3089559143437441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.989917767048, "W": 89.05, "J_1KI": 158.17506598991466, "W_1KI": 10.826747720364741, "W_D": 72.6925, "J_D": 1062.012460384965, "W_D_1KI": 8.837993920972645, "J_D_1KI": 1.0745281362884673} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.output new file mode 100644 index 0000000..81fbe8a --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_amazon0312.output @@ -0,0 +1,71 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 1.2765758037567139} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8946, 0.9409, 0.1159, ..., 0.4084, 0.7142, 0.8849]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 1.2765758037567139 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '8225', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"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": 10.766162395477295} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2056, 0.8237, 0.5206, ..., 0.1956, 0.5378, 0.6984]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.766162395477295 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2056, 0.8237, 0.5206, ..., 0.1956, 0.5378, 0.6984]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: csr +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.766162395477295 seconds + +[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57] +[89.05] +14.609656572341919 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, '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': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05} +[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57, 18.25, 17.56, 21.38, 17.98, 17.81, 17.52, 18.04, 17.55, 17.82, 17.58] +327.15 +16.357499999999998 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, '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': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05, 'J_1KI': 158.17506598991466, 'W_1KI': 10.826747720364741, 'W_D': 72.6925, 'J_D': 1062.012460384965, 'W_D_1KI': 8.837993920972645, 'J_D_1KI': 1.0745281362884673} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_darcy003.json similarity index 100% rename from pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.json rename to pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_darcy003.json diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_darcy003.output similarity index 100% rename from pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.output rename to pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_darcy003.output diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.json new file mode 100644 index 0000000..d957b86 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12273, "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": 10.708429336547852, "TIME_S_1KI": 0.8725192973639575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.9024416732789, "W": 89.76, "J_1KI": 104.53046864444545, "W_1KI": 7.313615252994378, "W_D": 73.62625, "J_D": 1052.309446259439, "W_D_1KI": 5.999042613867839, "J_D_1KI": 0.48880001742588114} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.output new file mode 100644 index 0000000..61cc75a --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_helm2d03.output @@ -0,0 +1,74 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 0.855471134185791} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2921, 0.7848, 0.3759, ..., 0.1614, 0.6217, 0.8908]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.855471134185791 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12273', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"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": 10.708429336547852} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0043, 0.4903, 0.3538, ..., 0.3528, 0.3455, 0.3342]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.708429336547852 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0043, 0.4903, 0.3538, ..., 0.3528, 0.3455, 0.3342]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: csr +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.708429336547852 seconds + +[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75] +[89.76] +14.292585134506226 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, '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': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76} +[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75, 18.01, 17.48, 17.78, 17.28, 17.51, 17.82, 17.71, 17.91, 19.53, 17.77] +322.67499999999995 +16.13375 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, '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': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76, 'J_1KI': 104.53046864444545, 'W_1KI': 7.313615252994378, 'W_D': 73.62625, 'J_D': 1052.309446259439, 'W_D_1KI': 5.999042613867839, 'J_D_1KI': 0.48880001742588114} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.json new file mode 100644 index 0000000..51eb669 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13367, "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": 10.647616863250732, "TIME_S_1KI": 0.7965599508678636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.2656374621392, "W": 89.59, "J_1KI": 95.32921653790224, "W_1KI": 6.702326625271191, "W_D": 73.54725, "J_D": 1046.0847572813632, "W_D_1KI": 5.502150819181567, "J_D_1KI": 0.4116219659745318} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.output new file mode 100644 index 0000000..467022c --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_language.output @@ -0,0 +1,71 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 0.785470724105835} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6209, 0.8638, 0.8531, ..., 0.3261, 0.5283, 0.1956]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.785470724105835 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13367', '-m', 'matrices/389000+_cols/language.mtx'] +{"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": 10.647616863250732} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6025, 0.9576, 0.6646, ..., 0.8829, 0.1742, 0.2421]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.647616863250732 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6025, 0.9576, 0.6646, ..., 0.8829, 0.1742, 0.2421]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: csr +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.647616863250732 seconds + +[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87] +[89.59] +14.223302125930786 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, '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': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59} +[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87, 18.16, 17.58, 17.67, 17.88, 17.53, 17.55, 17.46, 18.42, 17.89, 18.4] +320.855 +16.04275 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, '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': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59, 'J_1KI': 95.32921653790224, 'W_1KI': 6.702326625271191, 'W_D': 73.54725, 'J_D': 1046.0847572813632, 'W_D_1KI': 5.502150819181567, 'J_D_1KI': 0.4116219659745318} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.json new file mode 100644 index 0000000..735b097 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5913, "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": 10.544729232788086, "TIME_S_1KI": 1.783312909316436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.8479092025757, "W": 89.36, "J_1KI": 219.49059854601313, "W_1KI": 15.112464062235752, "W_D": 73.2365, "J_D": 1063.6732139918804, "W_D_1KI": 12.385675629967869, "J_D_1KI": 2.094651721624872} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.output new file mode 100644 index 0000000..41b8f99 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_marine1.output @@ -0,0 +1,74 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 1.7754507064819336} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1497, 0.9722, 0.3177, ..., 0.4783, 0.5290, 0.8575]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.7754507064819336 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5913', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"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": 10.544729232788086} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3921, 0.4534, 0.1657, ..., 0.0431, 0.7782, 0.5404]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.544729232788086 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3921, 0.4534, 0.1657, ..., 0.0431, 0.7782, 0.5404]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: csr +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.544729232788086 seconds + +[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7] +[89.36] +14.523812770843506 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, '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': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36} +[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7, 18.27, 17.88, 17.72, 17.88, 17.99, 17.97, 17.75, 18.36, 18.01, 17.9] +322.46999999999997 +16.1235 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, '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': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36, 'J_1KI': 219.49059854601313, 'W_1KI': 15.112464062235752, 'W_D': 73.2365, 'J_D': 1063.6732139918804, 'W_D_1KI': 12.385675629967869, 'J_D_1KI': 2.094651721624872} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.json new file mode 100644 index 0000000..6bd73ad --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13717, "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": 10.75633430480957, "TIME_S_1KI": 0.7841608445585456, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.2889261174203, "W": 89.67, "J_1KI": 94.72107065082892, "W_1KI": 6.537143690311293, "W_D": 73.277, "J_D": 1061.7597260968685, "W_D_1KI": 5.342057301159145, "J_D_1KI": 0.3894479333060542} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.output new file mode 100644 index 0000000..c3613ad --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_mario002.output @@ -0,0 +1,71 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 0.7654633522033691} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4671, 0.2005, 0.4838, ..., 0.7591, 0.5753, 0.8077]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.7654633522033691 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13717', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"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": 10.75633430480957} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1039, 0.6560, 0.4015, ..., 0.5631, 0.0997, 0.1433]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.75633430480957 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1039, 0.6560, 0.4015, ..., 0.5631, 0.0997, 0.1433]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: csr +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.75633430480957 seconds + +[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27] +[89.67] +14.489672422409058 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, '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': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67} +[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27, 22.42, 17.61, 17.63, 17.67, 17.98, 17.5, 17.66, 18.19, 17.57, 17.62] +327.86 +16.393 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, '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': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67, 'J_1KI': 94.72107065082892, 'W_1KI': 6.537143690311293, 'W_D': 73.277, 'J_D': 1061.7597260968685, 'W_D_1KI': 5.342057301159145, 'J_D_1KI': 0.3894479333060542} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.json new file mode 100644 index 0000000..f2c89e5 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1379, "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": 10.283698081970215, "TIME_S_1KI": 7.457359015206827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2003.8670825815202, "W": 66.59, "J_1KI": 1453.1305892541843, "W_1KI": 48.288614938361135, "W_D": 49.53425, "J_D": 1490.615002783656, "W_D_1KI": 35.920413343002174, "J_D_1KI": 26.04816050979128} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.output new file mode 100644 index 0000000..1e66417 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_msdoor.output @@ -0,0 +1,97 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 1.2347450256347656} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4403, 0.7085, 0.5973, ..., 0.5516, 0.0584, 0.9044]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 1.2347450256347656 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '850', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 6.469892501831055} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6729, 0.8216, 0.8655, ..., 0.6622, 0.9214, 0.0755]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 6.469892501831055 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1379', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"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": 10.283698081970215} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9393, 0.6181, 0.5648, ..., 0.9318, 0.7600, 0.4697]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.283698081970215 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9393, 0.6181, 0.5648, ..., 0.9318, 0.7600, 0.4697]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: csr +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.283698081970215 seconds + +[19.74, 19.62, 18.69, 18.65, 18.95, 18.9, 18.69, 18.4, 18.92, 18.85] +[66.59] +30.092612743377686 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1379, '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': 10.283698081970215, 'TIME_S_1KI': 7.457359015206827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2003.8670825815202, 'W': 66.59} +[19.74, 19.62, 18.69, 18.65, 18.95, 18.9, 18.69, 18.4, 18.92, 18.85, 19.77, 18.65, 19.56, 18.53, 18.44, 18.48, 18.72, 20.39, 19.13, 18.43] +341.115 +17.05575 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1379, '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': 10.283698081970215, 'TIME_S_1KI': 7.457359015206827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2003.8670825815202, 'W': 66.59, 'J_1KI': 1453.1305892541843, 'W_1KI': 48.288614938361135, 'W_D': 49.53425, 'J_D': 1490.615002783656, 'W_D_1KI': 35.920413343002174, 'J_D_1KI': 26.04816050979128} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.json new file mode 100644 index 0000000..54a5eaf --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1798, "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": 10.02889084815979, "TIME_S_1KI": 5.577803586295767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.981861114502, "W": 84.07, "J_1KI": 764.1723365486662, "W_1KI": 46.75750834260289, "W_D": 67.9785, "J_D": 1110.9935285568238, "W_D_1KI": 37.80784204671858, "J_D_1KI": 21.027720826873512} diff --git a/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.output new file mode 100644 index 0000000..1a5d3f5 --- /dev/null +++ b/pytorch/output_389000+_maxcore_old/xeon_4216_max_csr_10_10_10_test1.output @@ -0,0 +1,74 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 5.838165521621704} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7305, 0.5169, 0.5807, ..., 0.4360, 0.1397, 0.7206]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 5.838165521621704 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1798', '-m', 'matrices/389000+_cols/test1.mtx'] +{"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": 10.02889084815979} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9760, 0.7722, 0.6451, ..., 0.4999, 0.6092, 0.1757]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.02889084815979 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9760, 0.7722, 0.6451, ..., 0.4999, 0.6092, 0.1757]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: csr +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.02889084815979 seconds + +[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74] +[84.07] +16.343307495117188 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, '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': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07} +[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74, 18.31, 17.88, 17.67, 17.64, 18.57, 17.92, 17.84, 17.7, 17.61, 17.81] +321.83000000000004 +16.091500000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, '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': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07, 'J_1KI': 764.1723365486662, 'W_1KI': 46.75750834260289, 'W_D': 67.9785, 'J_D': 1110.9935285568238, 'W_D_1KI': 37.80784204671858, 'J_D_1KI': 21.027720826873512}