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389000+ coo

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cephi 2024-12-18 22:22:56 -05:00
parent 7569920be0
commit b5ccf071e9
126 changed files with 4548 additions and 0 deletions
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1
pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.json Normal file
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{"CPU": "Altra", "CORES": 16, "ITERATIONS": 113, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.044164180755615, "TIME_S_1KI": 88.88640867925324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 399.0583483409882, "W": 27.463802555817157, "J_1KI": 3531.4898083273292, "W_1KI": 243.04250049395714, "W_D": 11.900802555817158, "J_D": 172.92268986439706, "W_D_1KI": 105.31683677714298, "J_D_1KI": 932.00740510746}

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pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.output Normal file
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.9289438724517822}
tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707],
[ 0, 0, 1, ..., 400724, 400725, 400726]]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]),
size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo)
tensor([0.3618, 0.1403, 0.4609, ..., 0.6433, 0.7116, 0.4571])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: coo
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 0.9289438724517822 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 coo 113 -m matrices/389000+_cols/amazon0312.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.044164180755615}
tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707],
[ 0, 0, 1, ..., 400724, 400725, 400726]]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]),
size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo)
tensor([0.8763, 0.0482, 0.2785, ..., 0.8044, 0.9404, 0.7022])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: coo
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.044164180755615 seconds
tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707],
[ 0, 0, 1, ..., 400724, 400725, 400726]]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]),
size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo)
tensor([0.8763, 0.0482, 0.2785, ..., 0.8044, 0.9404, 0.7022])
Matrix Type: SuiteSparse
Matrix: amazon0312
Matrix Format: coo
Shape: torch.Size([400727, 400727])
Rows: 400727
Size: 160582128529
NNZ: 3200440
Density: 1.9930237750099465e-05
Time: 10.044164180755615 seconds
[16.52, 16.76, 16.8, 16.72, 17.04, 16.92, 16.72, 16.88, 16.88, 16.76]
[16.56, 16.68, 16.76, 18.68, 19.36, 24.52, 29.16, 33.8, 36.48, 38.88, 38.88, 38.68, 38.88, 38.84]
14.530338525772095
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 113, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.044164180755615, 'TIME_S_1KI': 88.88640867925324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 399.0583483409882, 'W': 27.463802555817157}
[16.52, 16.76, 16.8, 16.72, 17.04, 16.92, 16.72, 16.88, 16.88, 16.76, 16.92, 16.76, 16.76, 16.88, 17.24, 17.72, 18.96, 18.92, 18.88, 18.64]
311.26
15.562999999999999
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 113, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.044164180755615, 'TIME_S_1KI': 88.88640867925324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 399.0583483409882, 'W': 27.463802555817157, 'J_1KI': 3531.4898083273292, 'W_1KI': 243.04250049395714, 'W_D': 11.900802555817158, 'J_D': 172.92268986439706, 'W_D_1KI': 105.31683677714298, 'J_D_1KI': 932.00740510746}

1
pytorch/output_389000+_16core/altra_16_coo_10_10_10_helm2d03.json Normal file
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{"CPU": "Altra", "CORES": 16, "ITERATIONS": 182, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.577990770339966, "TIME_S_1KI": 58.12082840846135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 405.2002028274536, "W": 27.830491908156272, "J_1KI": 2226.3747408101844, "W_1KI": 152.91479070415534, "W_D": 12.904491908156274, "J_D": 187.8839495840073, "W_D_1KI": 70.90380169316634, "J_D_1KI": 389.58132798443046}

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pytorch/output_389000+_16core/altra_16_coo_10_10_10_helm2d03.output Normal file
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.7667891979217529}
tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254],
[ 0, 0, 0, ..., 392256, 392255, 392256]]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
-0.7602]),
size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo)
tensor([0.8101, 0.0565, 0.3715, ..., 0.3044, 0.3984, 0.4752])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: coo
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 0.7667891979217529 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 coo 136 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 7.835829734802246}
tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254],
[ 0, 0, 0, ..., 392256, 392255, 392256]]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
-0.7602]),
size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo)
tensor([0.6143, 0.6941, 0.3080, ..., 0.2687, 0.1392, 0.4472])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: coo
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 7.835829734802246 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 coo 182 -m matrices/389000+_cols/helm2d03.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.577990770339966}
tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254],
[ 0, 0, 0, ..., 392256, 392255, 392256]]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
-0.7602]),
size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo)
tensor([0.4534, 0.9614, 0.4347, ..., 0.7492, 0.4190, 0.3129])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: coo
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.577990770339966 seconds
tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254],
[ 0, 0, 0, ..., 392256, 392255, 392256]]),
values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602,
-0.7602]),
size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo)
tensor([0.4534, 0.9614, 0.4347, ..., 0.7492, 0.4190, 0.3129])
Matrix Type: SuiteSparse
Matrix: helm2d03
Matrix Format: coo
Shape: torch.Size([392257, 392257])
Rows: 392257
Size: 153865554049
NNZ: 2741935
Density: 1.7820330332848923e-05
Time: 10.577990770339966 seconds
[16.76, 16.52, 16.88, 16.96, 16.68, 16.68, 16.6, 16.12, 15.96, 15.96]
[16.2, 16.36, 16.56, 20.4, 21.44, 25.84, 30.32, 32.76, 35.8, 38.96, 39.04, 39.0, 39.0, 39.28]
14.559577465057373
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.577990770339966, 'TIME_S_1KI': 58.12082840846135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 405.2002028274536, 'W': 27.830491908156272}
[16.76, 16.52, 16.88, 16.96, 16.68, 16.68, 16.6, 16.12, 15.96, 15.96, 16.36, 16.32, 16.48, 16.48, 16.6, 16.8, 16.92, 16.88, 16.76, 16.68]
298.52
14.925999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.577990770339966, 'TIME_S_1KI': 58.12082840846135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 405.2002028274536, 'W': 27.830491908156272, 'J_1KI': 2226.3747408101844, 'W_1KI': 152.91479070415534, 'W_D': 12.904491908156274, 'J_D': 187.8839495840073, 'W_D_1KI': 70.90380169316634, 'J_D_1KI': 389.58132798443046}

1
pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.json Normal file
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{"CPU": "Altra", "CORES": 16, "ITERATIONS": 373, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.37615156173706, "TIME_S_1KI": 27.818100701707937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 403.1560953426361, "W": 27.703453693461576, "J_1KI": 1080.8474405968796, "W_1KI": 74.27199381625087, "W_D": 12.822453693461576, "J_D": 186.59949120306968, "W_D_1KI": 34.37655145700155, "J_D_1KI": 92.16233634584866}

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pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.output Normal file
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3676939010620117}
tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129],
[ 0, 0, 1, ..., 399128, 399129, 399129]]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo)
tensor([0.2195, 0.2966, 0.0612, ..., 0.9691, 0.9520, 0.8459])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: coo
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 0.3676939010620117 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 coo 285 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 8.002167224884033}
tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129],
[ 0, 0, 1, ..., 399128, 399129, 399129]]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo)
tensor([0.5703, 0.5551, 0.8897, ..., 0.2083, 0.9973, 0.5202])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: coo
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 8.002167224884033 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 coo 373 -m matrices/389000+_cols/language.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.37615156173706}
tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129],
[ 0, 0, 1, ..., 399128, 399129, 399129]]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo)
tensor([0.5564, 0.2657, 0.4233, ..., 0.6118, 0.8985, 0.4407])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: coo
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.37615156173706 seconds
tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129],
[ 0, 0, 1, ..., 399128, 399129, 399129]]),
values=tensor([ 1., -1., 1., ..., 1., -1., 1.]),
size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo)
tensor([0.5564, 0.2657, 0.4233, ..., 0.6118, 0.8985, 0.4407])
Matrix Type: SuiteSparse
Matrix: language
Matrix Format: coo
Shape: torch.Size([399130, 399130])
Rows: 399130
Size: 159304756900
NNZ: 1216334
Density: 7.635264782228233e-06
Time: 10.37615156173706 seconds
[16.36, 16.48, 16.52, 16.52, 16.64, 16.64, 16.68, 16.6, 16.76, 17.04]
[17.08, 16.96, 17.6, 18.68, 20.08, 25.16, 29.72, 34.04, 37.16, 38.8, 38.8, 38.68, 38.56, 38.36]
14.552557229995728
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 373, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.37615156173706, 'TIME_S_1KI': 27.818100701707937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 403.1560953426361, 'W': 27.703453693461576}
[16.36, 16.48, 16.52, 16.52, 16.64, 16.64, 16.68, 16.6, 16.76, 17.04, 16.64, 16.56, 16.44, 16.36, 16.2, 16.36, 16.52, 16.52, 16.52, 16.56]
297.62
14.881
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 373, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.37615156173706, 'TIME_S_1KI': 27.818100701707937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 403.1560953426361, 'W': 27.703453693461576, 'J_1KI': 1080.8474405968796, 'W_1KI': 74.27199381625087, 'W_D': 12.822453693461576, 'J_D': 186.59949120306968, 'W_D_1KI': 34.37655145700155, 'J_D_1KI': 92.16233634584866}

1
pytorch/output_389000+_16core/altra_16_coo_10_10_10_marine1.json Normal file
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tensor([0.9880, 0.2978, 0.6008, ..., 0.3725, 0.1138, 0.2491])
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[40.14, 39.31, 38.5, 38.49, 38.57, 39.63, 38.53, 39.33, 38.44, 39.47]
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13.786789894104004
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702.0
35.1
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pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_amazon0312.output Normal file
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324.03999999999996
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pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.output Normal file
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pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.json Normal file
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1
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370.9
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858.645
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