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as-caida coo

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cephi 2024-12-18 14:28:06 -05:00
parent 65e55bd251
commit a8eff4c683
432 changed files with 6907 additions and 9338 deletions
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1
pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.json Normal file
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.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 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.17511463165283203}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
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size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.3471, 0.9472, 0.8809, ..., 0.4614, 0.6320, 0.9437])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 0.17511463165283203 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 5996 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 9.475815296173096}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
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values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.0786, 0.3286, 0.8287, ..., 0.2886, 0.0657, 0.3483])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
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Size: 984641641
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Density: 7.616375021864427e-05
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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 6644 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.559938669204712}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.3081, 0.2338, 0.7835, ..., 0.8001, 0.8735, 0.7461])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
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Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.559938669204712 seconds
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.3081, 0.2338, 0.7835, ..., 0.8001, 0.8735, 0.7461])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.559938669204712 seconds
[16.52, 16.64, 16.64, 16.72, 16.68, 16.76, 16.6, 16.36, 16.12, 16.16]
[16.28, 16.4, 19.96, 21.0, 23.68, 23.68, 24.24, 25.16, 22.36, 21.32, 20.8, 20.76, 20.56, 20.6]
14.222814798355103
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[16.52, 16.64, 16.64, 16.72, 16.68, 16.76, 16.6, 16.36, 16.12, 16.16, 16.64, 16.84, 16.76, 16.68, 16.72, 16.72, 16.52, 16.6, 16.8, 17.0]
299.32000000000005
14.966000000000003
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.json Normal file
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.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 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.2135164737701416}
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tensor([0.7628, 0.8034, 0.1705, ..., 0.9286, 0.7986, 0.6691])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
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Rows: 31379
Size: 984641641
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{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 8.384274959564209}
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
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tensor([0.6786, 0.1047, 0.5064, ..., 0.9449, 0.2409, 0.4195])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
Shape: torch.Size([31379, 31379])
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Size: 984641641
NNZ: 80948
Density: 8.221062021893506e-05
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{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.447131633758545}
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
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size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
tensor([0.2661, 0.3239, 0.0589, ..., 0.7524, 0.1646, 0.2356])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
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Rows: 31379
Size: 984641641
NNZ: 80948
Density: 8.221062021893506e-05
Time: 10.447131633758545 seconds
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
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values=tensor([3., 3., 3., ..., 3., 3., 1.]),
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tensor([0.2661, 0.3239, 0.0589, ..., 0.7524, 0.1646, 0.2356])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
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Size: 984641641
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Density: 8.221062021893506e-05
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[16.72, 17.0, 17.0, 17.08, 17.2, 17.12, 16.96, 16.8, 16.76, 16.48]
[16.68, 17.08, 18.04, 18.84, 21.68, 21.68, 22.8, 23.32, 23.24, 21.92, 21.08, 20.96, 20.6, 20.6]
14.180746078491211
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301.49999999999994
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1
pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.json Normal file
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.output Normal file
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{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.2259833812713623}
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tensor([0.2384, 0.4578, 0.3485, ..., 0.8138, 0.6026, 0.3518])
Matrix Type: SuiteSparse
Matrix: as-caida_G_030
Matrix Format: coo
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tensor([0.9229, 0.9287, 0.9701, ..., 0.7114, 0.2046, 0.2710])
Matrix Type: SuiteSparse
Matrix: as-caida_G_030
Matrix Format: coo
Shape: torch.Size([31379, 31379])
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Size: 984641641
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{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.373903036117554}
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size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
tensor([0.4715, 0.9234, 0.3797, ..., 0.0569, 0.4086, 0.4497])
Matrix Type: SuiteSparse
Matrix: as-caida_G_030
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 86850
Density: 8.820467912752026e-05
Time: 10.373903036117554 seconds
tensor(indices=tensor([[ 1809, 21783, 106, ..., 7018, 160, 882],
[ 0, 0, 2, ..., 31357, 31360, 31373]]),
values=tensor([3., 3., 3., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
tensor([0.4715, 0.9234, 0.3797, ..., 0.0569, 0.4086, 0.4497])
Matrix Type: SuiteSparse
Matrix: as-caida_G_030
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 86850
Density: 8.820467912752026e-05
Time: 10.373903036117554 seconds
[16.4, 16.4, 16.56, 16.4, 16.44, 16.64, 16.6, 16.6, 16.68, 16.92]
[16.68, 16.48, 16.68, 20.4, 22.8, 24.32, 25.28, 23.08, 22.48, 21.04, 21.04, 20.92, 20.88, 20.64]
14.26013445854187
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[16.4, 16.4, 16.56, 16.4, 16.44, 16.64, 16.6, 16.6, 16.68, 16.92, 16.12, 16.04, 15.8, 15.96, 16.04, 16.0, 16.28, 16.52, 16.68, 16.84]
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.json Normal file
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pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.json Normal file
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@ -1,65 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.1808183193206787}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.7415, 0.8054, 0.6431, ..., 0.7043, 0.2095, 0.2852])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 0.1808183193206787 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5806 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.171998977661133}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.0310, 0.3080, 0.7594, ..., 0.0941, 0.5225, 0.9795])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 10.171998977661133 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.0310, 0.3080, 0.7594, ..., 0.0941, 0.5225, 0.9795])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 10.171998977661133 seconds
[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76]
[20.8, 20.88, 21.04, 25.72, 26.8, 29.92, 30.76, 28.4, 26.88, 24.6, 24.56, 24.56, 24.72]
13.240945100784302
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572}
[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76, 20.2, 20.36, 20.24, 20.2, 20.24, 20.36, 20.4, 20.56, 20.6, 20.48]
368.31999999999994
18.415999999999997
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572, 'J_1KI': 53.88152995029071, 'W_1KI': 4.069311483445328, 'W_D': 5.210422472883575, 'J_D': 68.9909179153442, 'W_D_1KI': 0.8974203363561101, 'J_D_1KI': 0.15456774653050467}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5449, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288, "TIME_S_1KI": 1.9124832300335624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.5276795768738, "W": 22.970903604955094, "J_1KI": 59.924330992269006, "W_1KI": 4.21561820608462, "W_D": 4.577903604955093, "J_D": 65.0741593434811, "W_D_1KI": 0.8401364663158548, "J_D_1KI": 0.15418177029103594}

85
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.24444031715393066}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.0041, 0.0059, 0.8299, ..., 0.3077, 0.8545, 0.8513])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 0.24444031715393066 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4295 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.275424003601074}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.5048, 0.0128, 0.9259, ..., 0.5690, 0.7343, 0.9731])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 8.275424003601074 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5449 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.2882, 0.9342, 0.0969, ..., 0.6573, 0.4161, 0.3369])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 10.42112112045288 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.2882, 0.9342, 0.0969, ..., 0.6573, 0.4161, 0.3369])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 10.42112112045288 seconds
[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24]
[20.4, 20.36, 20.28, 25.0, 25.96, 28.32, 29.16, 26.92, 25.8, 24.32, 24.4, 24.24, 24.24, 24.24]
14.21483826637268
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094}
[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24, 20.48, 20.36, 20.32, 20.2, 20.16, 20.6, 20.76, 20.72, 20.8, 20.44]
367.86
18.393
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094, 'J_1KI': 59.924330992269006, 'W_1KI': 4.21561820608462, 'W_D': 4.577903604955093, 'J_D': 65.0741593434811, 'W_D_1KI': 0.8401364663158548, 'J_D_1KI': 0.15418177029103594}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5009, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179, "TIME_S_1KI": 2.0834632347291233, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.9102258396148, "W": 23.870320724339745, "J_1KI": 67.85989735268812, "W_1KI": 4.7654862695827, "W_D": 5.56032072433975, "J_D": 79.17823539018632, "W_D_1KI": 1.110066026021112, "J_D_1KI": 0.22161429946518504}

85
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.2360525131225586}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.8761, 0.0368, 0.8631, ..., 0.6340, 0.9685, 0.1396])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 0.2360525131225586 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4448 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 9.323699712753296}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.4095, 0.9128, 0.5370, ..., 0.1298, 0.9549, 0.0765])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 9.323699712753296 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5009 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.6896, 0.4674, 0.9391, ..., 0.8690, 0.1471, 0.0542])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 10.436067342758179 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.6896, 0.4674, 0.9391, ..., 0.8690, 0.1471, 0.0542])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 10.436067342758179 seconds
[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52]
[20.44, 20.72, 20.72, 23.84, 26.4, 29.12, 30.32, 31.08, 27.36, 26.24, 24.52, 25.04, 25.36, 25.64]
14.239868402481079
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745}
[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52, 20.2, 20.16, 20.16, 20.16, 20.2, 20.24, 20.16, 20.2, 19.92, 19.96]
366.19999999999993
18.309999999999995
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745, 'J_1KI': 67.85989735268812, 'W_1KI': 4.7654862695827, 'W_D': 5.56032072433975, 'J_D': 79.17823539018632, 'W_D_1KI': 1.110066026021112, 'J_D_1KI': 0.22161429946518504}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json
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@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4906, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053, "TIME_S_1KI": 2.113679259152885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 320.3935369491577, "W": 22.512475730235575, "J_1KI": 65.30646900716626, "W_1KI": 4.5887639075082705, "W_D": 4.301475730235577, "J_D": 61.217834938526146, "W_D_1KI": 0.8767785834153234, "J_D_1KI": 0.17871556938755065}

85
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.28984951972961426}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.2495, 0.4746, 0.6508, ..., 0.6030, 0.3808, 0.6963])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 0.28984951972961426 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3622 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.750367879867554}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.1862, 0.1434, 0.8787, ..., 0.7704, 0.8925, 0.8878])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 7.750367879867554 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4906 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.7972, 0.7657, 0.0835, ..., 0.8008, 0.2416, 0.9619])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 10.369710445404053 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.7972, 0.7657, 0.0835, ..., 0.8008, 0.2416, 0.9619])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 10.369710445404053 seconds
[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2]
[20.36, 20.32, 21.32, 22.76, 24.92, 24.92, 25.72, 26.36, 26.24, 25.64, 24.64, 24.68, 24.72, 24.56]
14.231821537017822
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575}
[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2, 20.36, 20.4, 20.56, 20.4, 20.6, 20.6, 20.6, 20.28, 20.12, 20.52]
364.21999999999997
18.211
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575, 'J_1KI': 65.30646900716626, 'W_1KI': 4.5887639075082705, 'W_D': 4.301475730235577, 'J_D': 61.217834938526146, 'W_D_1KI': 0.8767785834153234, 'J_D_1KI': 0.17871556938755065}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4905, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495, "TIME_S_1KI": 2.1514306860718646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.58112309455873, "W": 23.14675123767438, "J_1KI": 66.9890159214187, "W_1KI": 4.719011465377039, "W_D": 4.7337512376743796, "J_D": 67.19825526070599, "W_D_1KI": 0.9650868986084362, "J_D_1KI": 0.196755738758091}

105
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output
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@ -1,105 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.2738626003265381}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.8777, 0.5124, 0.0822, ..., 0.9706, 0.3708, 0.5874])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 0.2738626003265381 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3834 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.640145778656006}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.9169, 0.5590, 0.9513, ..., 0.6480, 0.9706, 0.8048])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 8.640145778656006 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4659 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.972679376602173}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.6180, 0.9542, 0.9412, ..., 0.9357, 0.2218, 0.1163])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 9.972679376602173 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4905 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.7047, 0.9313, 0.0358, ..., 0.9576, 0.8194, 0.2072])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 10.552767515182495 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.7047, 0.9313, 0.0358, ..., 0.9576, 0.8194, 0.2072])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 10.552767515182495 seconds
[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6]
[20.6, 20.52, 20.28, 22.6, 24.08, 26.32, 27.4, 28.32, 27.04, 26.4, 25.72, 25.72, 25.88, 25.88]
14.195561170578003
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438}
[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6, 20.8, 20.4, 20.12, 19.96, 20.16, 20.24, 20.4, 20.6, 20.48, 20.4]
368.26
18.413
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438, 'J_1KI': 66.9890159214187, 'W_1KI': 4.719011465377039, 'W_D': 4.7337512376743796, 'J_D': 67.19825526070599, 'W_D_1KI': 0.9650868986084362, 'J_D_1KI': 0.196755738758091}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json
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@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757, "TIME_S_1KI": 2.212199888688875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 340.41829671859733, "W": 23.891063458309905, "J_1KI": 71.93962314425134, "W_1KI": 5.048829978510124, "W_D": 5.390063458309907, "J_D": 76.80178092050545, "W_D_1KI": 1.1390666649006567, "J_D_1KI": 0.24071569418864255}

86
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output
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@ -1,86 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.2837095260620117}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.1141, 0.9590, 0.8822, ..., 0.4586, 0.3032, 0.3922])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 0.2837095260620117 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3700 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 8.209553480148315}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([1.3531e-01, 9.2471e-01, 3.7424e-01, ..., 8.7339e-04, 4.3447e-01,
4.7205e-01])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 8.209553480148315 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4732 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.1672, 0.2165, 0.1528, ..., 0.1782, 0.4621, 0.9393])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 10.468129873275757 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.1672, 0.2165, 0.1528, ..., 0.1782, 0.4621, 0.9393])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 10.468129873275757 seconds
[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6]
[20.6, 20.72, 20.72, 24.32, 26.2, 29.28, 30.44, 28.2, 28.0, 25.56, 25.68, 25.84, 25.84, 25.8]
14.24877119064331
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905}
[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6, 20.64, 20.36, 20.52, 20.52, 20.48, 20.36, 20.36, 20.32, 20.44, 20.64]
370.02
18.500999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905, 'J_1KI': 71.93962314425134, 'W_1KI': 5.048829978510124, 'W_D': 5.390063458309907, 'J_D': 76.80178092050545, 'W_D_1KI': 1.1390666649006567, 'J_D_1KI': 0.24071569418864255}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json
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@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4558, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836, "TIME_S_1KI": 2.2826242886284245, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.84002380371095, "W": 23.226044906428264, "J_1KI": 72.3650776225781, "W_1KI": 5.095665841691151, "W_D": 4.6110449064282655, "J_D": 65.48283049583436, "W_D_1KI": 1.0116377591988295, "J_D_1KI": 0.2219477312853948}

85
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.2867097854614258}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.2765, 0.7404, 0.5834, ..., 0.0305, 0.2184, 0.6277])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 0.2867097854614258 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3662 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 8.434634447097778}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.9579, 0.2280, 0.6543, ..., 0.1974, 0.2729, 0.8108])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 8.434634447097778 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4558 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.9497, 0.5746, 0.8058, ..., 0.6531, 0.4871, 0.2425])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 10.40420150756836 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.9497, 0.5746, 0.8058, ..., 0.6531, 0.4871, 0.2425])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 10.40420150756836 seconds
[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48]
[20.4, 20.4, 20.32, 24.04, 25.44, 28.52, 29.28, 29.6, 26.24, 25.04, 24.36, 24.48, 24.64, 24.64]
14.201299667358398
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264}
[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48, 20.76, 20.76, 20.84, 20.72, 20.92, 21.16, 21.12, 20.96, 21.04, 21.2]
372.29999999999995
18.615
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264, 'J_1KI': 72.3650776225781, 'W_1KI': 5.095665841691151, 'W_D': 4.6110449064282655, 'J_D': 65.48283049583436, 'W_D_1KI': 1.0116377591988295, 'J_D_1KI': 0.2219477312853948}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4325, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273, "TIME_S_1KI": 2.5104655006717396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.7423639297486, "W": 23.58156237869744, "J_1KI": 77.62829223809216, "W_1KI": 5.45238436501675, "W_D": 5.256562378697442, "J_D": 74.84027779102334, "W_D_1KI": 1.2153901453635703, "J_D_1KI": 0.28101506251180813}

65
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output
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@ -1,65 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.24277067184448242}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.0081, 0.1025, 0.4852, ..., 0.3080, 0.4481, 0.5761])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 0.24277067184448242 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4325 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.2760, 0.7522, 0.4563, ..., 0.1844, 0.1938, 0.2151])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 10.857763290405273 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.2760, 0.7522, 0.4563, ..., 0.1844, 0.1938, 0.2151])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 10.857763290405273 seconds
[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48]
[20.4, 20.44, 20.72, 24.96, 27.28, 28.88, 29.68, 27.12, 26.68, 26.68, 24.92, 25.16, 24.88, 24.6]
14.237494468688965
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744}
[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48, 20.2, 20.44, 20.6, 20.68, 20.64, 20.44, 20.36, 20.08, 20.16, 20.16]
366.5
18.325
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744, 'J_1KI': 77.62829223809216, 'W_1KI': 5.45238436501675, 'W_D': 5.256562378697442, 'J_D': 74.84027779102334, 'W_D_1KI': 1.2153901453635703, 'J_D_1KI': 0.28101506251180813}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404, "TIME_S_1KI": 2.423833153585579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.41223700523375, "W": 22.905972813666082, "J_1KI": 75.92748011287131, "W_1KI": 5.32820954028055, "W_D": 4.372972813666085, "J_D": 62.31526816534997, "W_D_1KI": 1.0172069815459606, "J_D_1KI": 0.23661478984553633}

85
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.2965834140777588}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.4492, 0.3348, 0.2820, ..., 0.6380, 0.4778, 0.0633])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 0.2965834140777588 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3540 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.64511513710022}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.6270, 0.7806, 0.1009, ..., 0.0616, 0.3576, 0.1481])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 8.64511513710022 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4299 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.3742, 0.9296, 0.9529, ..., 0.5552, 0.6324, 0.8504])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 10.420058727264404 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.3742, 0.9296, 0.9529, ..., 0.5552, 0.6324, 0.8504])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 10.420058727264404 seconds
[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68]
[20.72, 20.36, 20.68, 24.16, 25.6, 27.56, 28.48, 26.48, 26.4, 24.6, 24.6, 24.68, 24.36, 24.12]
14.25009274482727
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082}
[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68, 20.32, 20.56, 20.4, 20.44, 20.48, 20.4, 20.72, 20.68, 20.76, 20.76]
370.65999999999997
18.532999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082, 'J_1KI': 75.92748011287131, 'W_1KI': 5.32820954028055, 'W_D': 4.372972813666085, 'J_D': 62.31526816534997, 'W_D_1KI': 1.0172069815459606, 'J_D_1KI': 0.23661478984553633}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729, "TIME_S_1KI": 2.4973505897093755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.92240665435787, "W": 23.947333156007627, "J_1KI": 79.49541783310521, "W_1KI": 5.600405321797855, "W_D": 5.373333156007625, "J_D": 76.27222314262384, "W_D_1KI": 1.2566260888698844, "J_D_1KI": 0.2938788795299075}

89
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output
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@ -1,89 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.31911325454711914}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.3207, 0.9700, 0.3262, ..., 0.9362, 0.9941, 0.3912])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 0.31911325454711914 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3290 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.077399730682373}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.5291, 0.8021, 0.1066, ..., 0.7881, 0.0805, 0.4870])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 8.077399730682373 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4276 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.1501, 0.9759, 0.0443, ..., 0.6183, 0.1649, 0.2013])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 10.67867112159729 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.1501, 0.9759, 0.0443, ..., 0.6183, 0.1649, 0.2013])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 10.67867112159729 seconds
[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28]
[20.32, 20.36, 20.52, 24.24, 26.84, 29.28, 30.8, 30.56, 26.92, 26.36, 25.44, 25.44, 25.36, 25.4]
14.19458293914795
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627}
[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28, 20.76, 20.88, 20.68, 20.84, 20.88, 21.0, 20.76, 20.88, 20.84, 20.96]
371.48
18.574
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627, 'J_1KI': 79.49541783310521, 'W_1KI': 5.600405321797855, 'W_D': 5.373333156007625, 'J_D': 76.27222314262384, 'W_D_1KI': 1.2566260888698844, 'J_D_1KI': 0.2938788795299075}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json
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@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4151, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944, "TIME_S_1KI": 2.8412091648742335, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 356.85992715835573, "W": 25.058368416517443, "J_1KI": 85.96962832049041, "W_1KI": 6.036706436164163, "W_D": 5.961368416517441, "J_D": 84.89672845101359, "W_D_1KI": 1.4361282622301712, "J_D_1KI": 0.3459716362876828}

89
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output
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@ -1,89 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.33879852294921875}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.1080, 0.7126, 0.2741, ..., 0.0141, 0.6709, 0.0416])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 0.33879852294921875 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3099 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 7.838690519332886}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.5517, 0.4219, 0.5041, ..., 0.2191, 0.6881, 0.0206])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 7.838690519332886 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4151 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.6544, 0.9539, 0.4979, ..., 0.4670, 0.0344, 0.9767])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 11.793859243392944 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.6544, 0.9539, 0.4979, ..., 0.4670, 0.0344, 0.9767])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 11.793859243392944 seconds
[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48]
[22.96, 22.84, 22.8, 26.76, 28.44, 29.48, 30.2, 28.12, 28.12, 28.04, 26.36, 27.0, 27.12, 27.12]
14.241147756576538
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443}
[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48, 21.08, 20.44, 20.4, 20.6, 20.6, 20.6, 20.64, 20.48, 20.48, 20.6]
381.94
19.097
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443, 'J_1KI': 85.96962832049041, 'W_1KI': 6.036706436164163, 'W_D': 5.961368416517441, 'J_D': 84.89672845101359, 'W_D_1KI': 1.4361282622301712, 'J_D_1KI': 0.3459716362876828}

1
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json
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@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3898, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087, "TIME_S_1KI": 2.6363732571354768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.2206564903259, "W": 22.616425322779282, "J_1KI": 76.76261069531193, "W_1KI": 5.802058830882319, "W_D": 4.17842532277928, "J_D": 55.28155534458152, "W_D_1KI": 1.0719408216468136, "J_D_1KI": 0.27499764536860277}

89
pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output
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@ -1,89 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.29033493995666504}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.0287, 0.9302, 0.4533, ..., 0.1887, 0.8093, 0.0476])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 0.29033493995666504 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3616 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 9.739661455154419}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.1589, 0.7243, 0.3638, ..., 0.5413, 0.9750, 0.3668])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 9.739661455154419 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3898 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.5554, 0.1424, 0.7572, ..., 0.7612, 0.5304, 0.9292])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 10.276582956314087 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.5554, 0.1424, 0.7572, ..., 0.7612, 0.5304, 0.9292])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 10.276582956314087 seconds
[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24]
[20.12, 20.44, 21.0, 22.68, 25.16, 26.56, 26.56, 27.28, 26.48, 26.6, 24.04, 24.28, 24.76]
13.230236530303955
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282}
[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24, 20.4, 20.36, 20.36, 20.48, 20.52, 20.4, 20.52, 20.52, 20.68, 20.68]
368.76000000000005
18.438000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282, 'J_1KI': 76.76261069531193, 'W_1KI': 5.802058830882319, 'W_D': 4.17842532277928, 'J_D': 55.28155534458152, 'W_D_1KI': 1.0719408216468136, 'J_D_1KI': 0.27499764536860277}

1
pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4769, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.119500637054443, "TIME_S_1KI": 2.1219334529365574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 844.3043718338013, "W": 66.0, "J_1KI": 177.0401282939403, "W_1KI": 13.839379324806039, "W_D": 30.517749999999992, "J_D": 390.3980264171361, "W_D_1KI": 6.3991927028727185, "J_D_1KI": 1.3418311392058542}

59
pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.output Normal file
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@ -0,0 +1,59 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.22014713287353516}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.0446, 0.3085, 0.7701, ..., 0.1983, 0.0942, 0.8281])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 0.22014713287353516 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4769', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
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{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3394, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.34037709236145, "TIME_S_1KI": 3.0466638457163966, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 844.1848246479035, "W": 66.18, "J_1KI": 248.72858710898748, "W_1KI": 19.49911608721273, "W_D": 30.526500000000013, "J_D": 389.3926873619558, "W_D_1KI": 8.99425456688274, "J_D_1KI": 2.6500455412147144}

59
pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.output Normal file
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.309314489364624}
tensor(indices=tensor([[ 106, 329, 1040, ..., 155, 160, 12170],
[ 2, 2, 2, ..., 31355, 31360, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
tensor([0.8467, 0.4792, 0.3688, ..., 0.8111, 0.4001, 0.1665])
Matrix Type: SuiteSparse
Matrix: as-caida_G_120
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106510
Density: 0.0001081713341839054
Time: 0.309314489364624 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '3394', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.34037709236145}
tensor(indices=tensor([[ 106, 329, 1040, ..., 155, 160, 12170],
[ 2, 2, 2, ..., 31355, 31360, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
tensor([0.5583, 0.2788, 0.6104, ..., 0.7605, 0.2906, 0.0391])
Matrix Type: SuiteSparse
Matrix: as-caida_G_120
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106510
Density: 0.0001081713341839054
Time: 10.34037709236145 seconds
tensor(indices=tensor([[ 106, 329, 1040, ..., 155, 160, 12170],
[ 2, 2, 2, ..., 31355, 31360, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
tensor([0.5583, 0.2788, 0.6104, ..., 0.7605, 0.2906, 0.0391])
Matrix Type: SuiteSparse
Matrix: as-caida_G_120
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106510
Density: 0.0001081713341839054
Time: 10.34037709236145 seconds
[40.3, 44.55, 39.36, 39.43, 39.43, 39.38, 39.86, 38.9, 38.87, 39.02]
[66.18]
12.755890369415283
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3394, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.34037709236145, 'TIME_S_1KI': 3.0466638457163966, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.1848246479035, 'W': 66.18}
[40.3, 44.55, 39.36, 39.43, 39.43, 39.38, 39.86, 38.9, 38.87, 39.02, 40.16, 39.0, 38.87, 38.79, 39.53, 39.93, 39.44, 39.04, 39.36, 39.18]
713.0699999999999
35.653499999999994
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3394, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.34037709236145, 'TIME_S_1KI': 3.0466638457163966, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.1848246479035, 'W': 66.18, 'J_1KI': 248.72858710898748, 'W_1KI': 19.49911608721273, 'W_D': 30.526500000000013, 'J_D': 389.3926873619558, 'W_D_1KI': 8.99425456688274, 'J_D_1KI': 2.6500455412147144}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130629, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848, "TIME_S_1KI": 0.08456685409111948, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1447.5023779034616, "W": 103.45, "J_1KI": 11.081018593906878, "W_1KI": 0.7919374717711994, "W_D": 67.28425, "J_D": 941.4607237356305, "W_D_1KI": 0.5150789640891379, "J_D_1KI": 0.003943067497180089}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.02265310287475586}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.1109, 0.3688, 0.8394, ..., 0.5778, 0.7474, 0.0459])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 0.02265310287475586 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46351', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 3.7256975173950195}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.7624, 0.1488, 0.7288, ..., 0.4517, 0.7426, 0.4871])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 3.7256975173950195 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130629', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.6772, 0.5623, 0.5296, ..., 0.8301, 0.9620, 0.4995])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 11.046883583068848 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 63, 63, ..., 70025, 70025, 70026]),
col_indices=tensor([ 111, 761, 822, ..., 978, 978, 12170]),
values=tensor([4., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=70026, layout=torch.sparse_csr)
tensor([0.6772, 0.5623, 0.5296, ..., 0.8301, 0.9620, 0.4995])
Matrix Type: SuiteSparse
Matrix: as-caida_G_005
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 70026
Density: 7.111825976492498e-05
Time: 11.046883583068848 seconds
[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12]
[103.45]
13.992289781570435
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45}
[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12, 40.63, 39.49, 44.41, 39.16, 39.85, 39.65, 39.59, 39.3, 39.78, 39.59]
723.315
36.16575
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45, 'J_1KI': 11.081018593906878, 'W_1KI': 0.7919374717711994, 'W_D': 67.28425, 'J_D': 941.4607237356305, 'W_D_1KI': 0.5150789640891379, 'J_D_1KI': 0.003943067497180089}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122232, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186, "TIME_S_1KI": 0.08191718068357864, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.7570972156525, "W": 103.87, "J_1KI": 11.001677933893355, "W_1KI": 0.8497774723476668, "W_D": 67.6435, "J_D": 875.7492702946663, "W_D_1KI": 0.5534025459781399, "J_D_1KI": 0.004527476814403265}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.02150440216064453}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.2547, 0.3736, 0.4755, ..., 0.0068, 0.6237, 0.5320])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 0.02150440216064453 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '48827', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.194327354431152}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.7417, 0.4748, 0.0091, ..., 0.5058, 0.6479, 0.7190])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 4.194327354431152 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122232', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.8581, 0.1137, 0.8207, ..., 0.0910, 0.4048, 0.6394])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 10.012900829315186 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 4, ..., 77124, 77124, 77124]),
col_indices=tensor([1040, 2054, 4842, ..., 160, 160, 8230]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=77124, layout=torch.sparse_csr)
tensor([0.8581, 0.1137, 0.8207, ..., 0.0910, 0.4048, 0.6394])
Matrix Type: SuiteSparse
Matrix: as-caida_G_015
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 77124
Density: 7.832697378273889e-05
Time: 10.012900829315186 seconds
[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74]
[103.87]
12.946539878845215
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87}
[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74, 39.91, 39.57, 39.25, 39.28, 39.45, 39.43, 39.72, 39.1, 39.4, 39.09]
724.53
36.2265
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87, 'J_1KI': 11.001677933893355, 'W_1KI': 0.8497774723476668, 'W_D': 67.6435, 'J_D': 875.7492702946663, 'W_D_1KI': 0.5534025459781399, 'J_D_1KI': 0.004527476814403265}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589, "TIME_S_1KI": 0.10023047952266595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1377.8382610416413, "W": 104.04, "J_1KI": 11.47070598113223, "W_1KI": 0.8661482875172747, "W_D": 68.01325, "J_D": 900.7233574374318, "W_D_1KI": 0.5662203000382956, "J_D_1KI": 0.004713867197574848}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.028760671615600586}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.0546, 0.6478, 0.5019, ..., 0.1774, 0.5884, 0.7696])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 0.028760671615600586 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36508', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 3.191291332244873}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.7513, 0.6718, 0.2286, ..., 0.8031, 0.6348, 0.1488])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 3.191291332244873 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120118', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.0727, 0.2026, 0.8265, ..., 0.2293, 0.8547, 0.4127])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 12.039484739303589 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 3, ..., 85845, 85847, 85850]),
col_indices=tensor([ 346, 13811, 21783, ..., 15310, 17998, 31377]),
values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379),
nnz=85850, layout=torch.sparse_csr)
tensor([0.0727, 0.2026, 0.8265, ..., 0.2293, 0.8547, 0.4127])
Matrix Type: SuiteSparse
Matrix: as-caida_G_025
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 85850
Density: 8.718908121010495e-05
Time: 12.039484739303589 seconds
[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23]
[104.04]
13.243351221084595
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04}
[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23, 40.57, 39.23, 39.41, 39.27, 39.7, 44.55, 39.47, 39.31, 39.26, 39.61]
720.5350000000001
36.02675000000001
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04, 'J_1KI': 11.47070598113223, 'W_1KI': 0.8661482875172747, 'W_D': 68.01325, 'J_D': 900.7233574374318, 'W_D_1KI': 0.5662203000382956, 'J_D_1KI': 0.004713867197574848}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117637, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627, "TIME_S_1KI": 0.09034036808721975, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1348.652883746624, "W": 104.36999999999999, "J_1KI": 11.464529729138144, "W_1KI": 0.8872208573833061, "W_D": 68.05524999999999, "J_D": 879.3993404867051, "W_D_1KI": 0.5785190883820566, "J_D_1KI": 0.00491783272594555}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.029236316680908203}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.3402, 0.2499, 0.3172, ..., 0.1488, 0.4072, 0.2122])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 0.029236316680908203 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35914', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 3.2055723667144775}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.5217, 0.6973, 0.2862, ..., 0.9929, 0.5018, 0.4794])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 3.2055723667144775 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117637', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.6504, 0.2249, 0.2739, ..., 0.8117, 0.7999, 0.0068])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 10.62736988067627 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 87559, 87559, 87560]),
col_indices=tensor([ 1809, 21783, 106, ..., 10144, 882, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=87560, layout=torch.sparse_csr)
tensor([0.6504, 0.2249, 0.2739, ..., 0.8117, 0.7999, 0.0068])
Matrix Type: SuiteSparse
Matrix: as-caida_G_035
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 87560
Density: 8.892575364888514e-05
Time: 10.62736988067627 seconds
[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38]
[104.37]
12.921844244003296
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999}
[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38, 40.08, 39.64, 42.5, 41.53, 39.76, 39.42, 39.32, 39.21, 40.44, 41.29]
726.2950000000001
36.314750000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999, 'J_1KI': 11.464529729138144, 'W_1KI': 0.8872208573833061, 'W_D': 68.05524999999999, 'J_D': 879.3993404867051, 'W_D_1KI': 0.5785190883820566, 'J_D_1KI': 0.00491783272594555}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459, "TIME_S_1KI": 0.08677222450439022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1476.8643749785424, "W": 104.97, "J_1KI": 11.395822241090013, "W_1KI": 0.8099724530660432, "W_D": 69.20224999999999, "J_D": 973.6337781590818, "W_D_1KI": 0.5339803390510582, "J_D_1KI": 0.004120314043157312}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.027070283889770508}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.9579, 0.3823, 0.7927, ..., 0.9257, 0.1735, 0.9344])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 0.027070283889770508 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38787', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 3.142535448074341}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.7648, 0.3979, 0.1181, ..., 0.8603, 0.9960, 0.9728])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 3.142535448074341 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129597', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.1180, 0.3748, 0.1643, ..., 0.9664, 0.3966, 0.4847])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 11.245419979095459 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 89150, 89150, 89152]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 2232, 16085]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=89152, layout=torch.sparse_csr)
tensor([0.1180, 0.3748, 0.1643, ..., 0.9664, 0.3966, 0.4847])
Matrix Type: SuiteSparse
Matrix: as-caida_G_045
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 89152
Density: 9.054258553341032e-05
Time: 11.245419979095459 seconds
[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3]
[104.97]
14.069394826889038
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97}
[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3, 40.3, 39.27, 39.34, 39.54, 39.86, 39.21, 39.39, 41.12, 39.24, 39.74]
715.355
35.76775
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97, 'J_1KI': 11.395822241090013, 'W_1KI': 0.8099724530660432, 'W_D': 69.20224999999999, 'J_D': 973.6337781590818, 'W_D_1KI': 0.5339803390510582, 'J_D_1KI': 0.004120314043157312}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496, "TIME_S_1KI": 0.09067436739580567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1444.9410432815553, "W": 104.47000000000001, "J_1KI": 11.830009687753233, "W_1KI": 0.855315943737617, "W_D": 68.78025000000001, "J_D": 951.3104833173753, "W_D_1KI": 0.5631171095937516, "J_D_1KI": 0.004610347870460215}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.02579522132873535}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.4101, 0.8209, 0.7582, ..., 0.7042, 0.4089, 0.9250])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 0.02579522132873535 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '40705', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 3.499220609664917}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.5165, 0.5886, 0.2570, ..., 0.5351, 0.5985, 0.2855])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 3.499220609664917 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122142', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.1241, 0.2147, 0.8696, ..., 0.1307, 0.0728, 0.1644])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 11.075148582458496 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 91475, 91475, 91476]),
col_indices=tensor([21783, 106, 329, ..., 160, 882, 17255]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=91476, layout=torch.sparse_csr)
tensor([0.1241, 0.2147, 0.8696, ..., 0.1307, 0.0728, 0.1644])
Matrix Type: SuiteSparse
Matrix: as-caida_G_055
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 91476
Density: 9.290283509348351e-05
Time: 11.075148582458496 seconds
[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66]
[104.47]
13.831157684326172
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001}
[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66, 39.88, 39.36, 39.59, 39.25, 40.14, 39.15, 39.32, 39.55, 39.92, 39.6]
713.7950000000001
35.689750000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001, 'J_1KI': 11.830009687753233, 'W_1KI': 0.855315943737617, 'W_D': 68.78025000000001, 'J_D': 951.3104833173753, 'W_D_1KI': 0.5631171095937516, 'J_D_1KI': 0.004610347870460215}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121043, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519, "TIME_S_1KI": 0.08666592661125815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.8674520850182, "W": 104.75, "J_1KI": 11.540258024710377, "W_1KI": 0.8653949422932347, "W_D": 68.499, "J_D": 913.4512992875575, "W_D_1KI": 0.5659063308080599, "J_D_1KI": 0.004675250372248373}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.028383970260620117}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.6987, 0.1536, 0.6933, ..., 0.9556, 0.5512, 0.6559])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 0.028383970260620117 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36992', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 3.2088987827301025}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.2523, 0.5417, 0.6382, ..., 0.2729, 0.0339, 0.5004])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 3.2088987827301025 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121043', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.9743, 0.5130, 0.5318, ..., 0.8200, 0.9366, 0.1557])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 10.490303754806519 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 95068, 95068, 95068]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=95068, layout=torch.sparse_csr)
tensor([0.9743, 0.5130, 0.5318, ..., 0.8200, 0.9366, 0.1557])
Matrix Type: SuiteSparse
Matrix: as-caida_G_065
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 95068
Density: 9.655086281283934e-05
Time: 10.490303754806519 seconds
[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67]
[104.75]
13.33525013923645
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75}
[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67, 40.18, 39.27, 39.88, 39.63, 40.85, 43.9, 39.97, 39.48, 39.27, 39.18]
725.02
36.251
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75, 'J_1KI': 11.540258024710377, 'W_1KI': 0.8653949422932347, 'W_D': 68.499, 'J_D': 913.4512992875575, 'W_D_1KI': 0.5659063308080599, 'J_D_1KI': 0.004675250372248373}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120423, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314, "TIME_S_1KI": 0.08943129150862493, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.2409217905997, "W": 104.71, "J_1KI": 11.984761397661574, "W_1KI": 0.8695182813914285, "W_D": 68.72225, "J_D": 947.213861498654, "W_D_1KI": 0.5706737915514478, "J_D_1KI": 0.0047389102708905095}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.026674270629882812}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.6187, 0.1169, 0.0618, ..., 0.0036, 0.3565, 0.7239])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 0.026674270629882812 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '39363', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 3.4321558475494385}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.5402, 0.1074, 0.0917, ..., 0.8328, 0.8213, 0.8141])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 3.4321558475494385 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120423', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.9766, 0.5800, 0.0793, ..., 0.9152, 0.2119, 0.8249])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 10.76958441734314 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 97491, 97491, 97492]),
col_indices=tensor([22754, 22754, 106, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=97492, layout=torch.sparse_csr)
tensor([0.9766, 0.5800, 0.0793, ..., 0.9152, 0.2119, 0.8249])
Matrix Type: SuiteSparse
Matrix: as-caida_G_075
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 97492
Density: 9.901267216465406e-05
Time: 10.76958441734314 seconds
[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23]
[104.71]
13.783219575881958
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71}
[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23, 53.53, 39.71, 40.17, 39.13, 39.61, 39.81, 39.45, 39.24, 39.21, 39.43]
719.7549999999999
35.98774999999999
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71, 'J_1KI': 11.984761397661574, 'W_1KI': 0.8695182813914285, 'W_D': 68.72225, 'J_D': 947.213861498654, 'W_D_1KI': 0.5706737915514478, 'J_D_1KI': 0.0047389102708905095}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123818, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852, "TIME_S_1KI": 0.10213301566095818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1404.5768914031983, "W": 105.84, "J_1KI": 11.343882887812743, "W_1KI": 0.8548030173318903, "W_D": 69.798, "J_D": 926.2722776470184, "W_D_1KI": 0.5637144841622381, "J_D_1KI": 0.004552766836503886}

85
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output
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@ -1,85 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.027594804763793945}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.3579, 0.6537, 0.3650, ..., 0.6368, 0.7499, 0.3578])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 0.027594804763793945 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38050', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 3.2266926765441895}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.5666, 0.3607, 0.0681, ..., 0.1783, 0.0421, 0.2428])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 3.2266926765441895 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123818', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.9239, 0.0838, 0.6171, ..., 0.0890, 0.6862, 0.2789])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 12.64590573310852 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99165, 99165, 99166]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=99166, layout=torch.sparse_csr)
tensor([0.9239, 0.0838, 0.6171, ..., 0.0890, 0.6862, 0.2789])
Matrix Type: SuiteSparse
Matrix: as-caida_G_085
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 99166
Density: 0.0001007127830784073
Time: 12.64590573310852 seconds
[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12]
[105.84]
13.270756721496582
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84}
[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12, 40.73, 39.74, 39.65, 39.19, 44.6, 39.27, 39.31, 39.32, 39.56, 39.64]
720.8399999999999
36.041999999999994
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84, 'J_1KI': 11.343882887812743, 'W_1KI': 0.8548030173318903, 'W_D': 69.798, 'J_D': 926.2722776470184, 'W_D_1KI': 0.5637144841622381, 'J_D_1KI': 0.004552766836503886}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json
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@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123367, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541, "TIME_S_1KI": 0.08752578008324383, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.98059548378, "W": 106.36000000000001, "J_1KI": 11.202190176333865, "W_1KI": 0.8621430366305415, "W_D": 70.495, "J_D": 915.9714373695851, "W_D_1KI": 0.5714250974733924, "J_D_1KI": 0.004631912079189673}

110
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output
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@ -1,110 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.02869391441345215}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.6977, 0.1794, 0.1733, ..., 0.5021, 0.2371, 0.5915])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 0.02869391441345215 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36593', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 3.28193736076355}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.2038, 0.2117, 0.0495, ..., 0.7353, 0.4058, 0.7220])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 3.28193736076355 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117073', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.964244604110718}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.2057, 0.4266, 0.2763, ..., 0.1377, 0.7100, 0.3501])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 9.964244604110718 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123367', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.2200, 0.4810, 0.3293, ..., 0.7198, 0.3850, 0.3915])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 10.797792911529541 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 102289, 102289,
102290]),
col_indices=tensor([ 106, 329, 1040, ..., 882, 25970, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=102290, layout=torch.sparse_csr)
tensor([0.2200, 0.4810, 0.3293, ..., 0.7198, 0.3850, 0.3915])
Matrix Type: SuiteSparse
Matrix: as-caida_G_095
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 102290
Density: 0.00010388551097241275
Time: 10.797792911529541 seconds
[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6]
[106.36]
12.9934241771698
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001}
[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6, 41.41, 39.21, 39.7, 39.75, 39.19, 39.65, 39.19, 40.91, 42.9, 39.08]
717.3000000000001
35.865
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001, 'J_1KI': 11.202190176333865, 'W_1KI': 0.8621430366305415, 'W_D': 70.495, 'J_D': 915.9714373695851, 'W_D_1KI': 0.5714250974733924, 'J_D_1KI': 0.004631912079189673}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130315, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334, "TIME_S_1KI": 0.0862452585287445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1479.8992812085153, "W": 106.81, "J_1KI": 11.356323379568856, "W_1KI": 0.8196293596285923, "W_D": 71.0115, "J_D": 983.8954012502431, "W_D_1KI": 0.5449219199631662, "J_D_1KI": 0.004181574799241578}

110
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output
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@ -1,110 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.02874302864074707}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.0653, 0.6055, 0.4558, ..., 0.6884, 0.5872, 0.3001])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 0.02874302864074707 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36530', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 3.1528003215789795}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.0149, 0.2762, 0.5141, ..., 0.7208, 0.6938, 0.9073])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 3.1528003215789795 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121658', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 9.802452087402344}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.9575, 0.4028, 0.3578, ..., 0.2947, 0.5779, 0.9432])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 9.802452087402344 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130315', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.3162, 0.7565, 0.7515, ..., 0.4628, 0.3648, 0.1630])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 11.23905086517334 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 104725, 104725,
104726]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=104726, layout=torch.sparse_csr)
tensor([0.3162, 0.7565, 0.7515, ..., 0.4628, 0.3648, 0.1630])
Matrix Type: SuiteSparse
Matrix: as-caida_G_105
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 104726
Density: 0.00010635950749923647
Time: 11.23905086517334 seconds
[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98]
[106.81]
13.855437517166138
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81}
[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98, 41.03, 39.28, 39.81, 39.38, 39.32, 39.19, 40.5, 39.21, 39.67, 39.6]
715.97
35.798500000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81, 'J_1KI': 11.356323379568856, 'W_1KI': 0.8196293596285923, 'W_D': 71.0115, 'J_D': 983.8954012502431, 'W_D_1KI': 0.5449219199631662, 'J_D_1KI': 0.004181574799241578}

1
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122384, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164, "TIME_S_1KI": 0.08680102310549716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.5208738970757, "W": 106.31, "J_1KI": 11.223042831555397, "W_1KI": 0.8686593018695254, "W_D": 70.50425, "J_D": 910.9120409505963, "W_D_1KI": 0.5760904203163812, "J_D_1KI": 0.004707236406036584}

89
pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output
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@ -1,89 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.029188871383666992}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.1969, 0.7804, 0.2686, ..., 0.5827, 0.0798, 0.7838])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 0.029188871383666992 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35972', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 3.086211681365967}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.5660, 0.8309, 0.9656, ..., 0.1156, 0.8355, 0.1569])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 3.086211681365967 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122384', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.4590, 0.4390, 0.2483, ..., 0.8018, 0.3092, 0.2454])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 10.623056411743164 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106311, 106311,
106312]),
col_indices=tensor([ 106, 329, 1040, ..., 160, 882, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106312, layout=torch.sparse_csr)
tensor([0.4590, 0.4390, 0.2483, ..., 0.8018, 0.3092, 0.2454])
Matrix Type: SuiteSparse
Matrix: as-caida_G_115
Matrix Format: csr
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 106312
Density: 0.00010797024579625715
Time: 10.623056411743164 seconds
[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32]
[106.31]
12.919959306716919
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31}
[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32, 39.97, 39.29, 39.38, 39.24, 39.46, 39.22, 39.78, 41.14, 39.67, 39.16]
716.115
35.80575
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31, 'J_1KI': 11.223042831555397, 'W_1KI': 0.8686593018695254, 'W_D': 70.50425, 'J_D': 910.9120409505963, 'W_D_1KI': 0.5760904203163812, 'J_D_1KI': 0.004707236406036584}

1
pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3781, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.045656204223633, "TIME_S_1KI": 2.6568781285965706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.7539411067963, "W": 54.2, "J_1KI": 194.59242028743623, "W_1KI": 14.334832055011901, "W_D": 36.8215, "J_D": 499.84434949195384, "W_D_1KI": 9.738561227188574, "J_D_1KI": 2.575657558103299}

59
pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.output Normal file
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@ -0,0 +1,59 @@
['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', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2776789665222168}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.3307, 0.3897, 0.8418, ..., 0.5769, 0.7460, 0.7214])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 0.2776789665222168 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', 'coo', '3781', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.045656204223633}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.8381, 0.6468, 0.8832, ..., 0.6716, 0.5945, 0.2145])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.045656204223633 seconds
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.8381, 0.6468, 0.8832, ..., 0.6716, 0.5945, 0.2145])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.045656204223633 seconds
[18.97, 19.7, 18.61, 18.68, 18.95, 18.76, 18.82, 18.79, 23.19, 18.83]
[54.2]
13.574795961380005
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 3781, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.045656204223633, 'TIME_S_1KI': 2.6568781285965706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.7539411067963, 'W': 54.2}
[18.97, 19.7, 18.61, 18.68, 18.95, 18.76, 18.82, 18.79, 23.19, 18.83, 18.72, 18.54, 18.93, 18.5, 22.74, 19.06, 19.06, 19.0, 18.57, 18.82]
347.57
17.3785
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 3781, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.045656204223633, 'TIME_S_1KI': 2.6568781285965706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.7539411067963, 'W': 54.2, 'J_1KI': 194.59242028743623, 'W_1KI': 14.334832055011901, 'W_D': 36.8215, 'J_D': 499.84434949195384, 'W_D_1KI': 9.738561227188574, 'J_D_1KI': 2.575657558103299}

1
pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3534, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.09178638458252, "TIME_S_1KI": 2.8556271603232934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.7126415205003, "W": 54.38, "J_1KI": 209.03017586884556, "W_1KI": 15.387662705149973, "W_D": 37.230500000000006, "J_D": 505.7491908813716, "W_D_1KI": 10.534946236559142, "J_D_1KI": 2.981026099762066}

59
pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.output Normal file
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@ -0,0 +1,59 @@
['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', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.29705047607421875}
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
[ 0, 0, 2, ..., 31377, 31378, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 1.]),
size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
tensor([0.5345, 0.3485, 0.5768, ..., 0.6584, 0.2810, 0.8312])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 80948
Density: 8.221062021893506e-05
Time: 0.29705047607421875 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', 'coo', '3534', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.09178638458252}
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
[ 0, 0, 2, ..., 31377, 31378, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 1.]),
size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
tensor([0.6572, 0.3345, 0.1097, ..., 0.7719, 0.7425, 0.7121])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 80948
Density: 8.221062021893506e-05
Time: 10.09178638458252 seconds
tensor(indices=tensor([[ 1040, 5699, 106, ..., 31378, 17998, 31377],
[ 0, 0, 2, ..., 31377, 31378, 31378]]),
values=tensor([3., 3., 3., ..., 3., 3., 1.]),
size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
tensor([0.6572, 0.3345, 0.1097, ..., 0.7719, 0.7425, 0.7121])
Matrix Type: SuiteSparse
Matrix: as-caida_G_020
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 80948
Density: 8.221062021893506e-05
Time: 10.09178638458252 seconds
[19.07, 18.48, 19.1, 18.47, 18.95, 18.68, 18.6, 18.62, 18.69, 19.55]
[54.38]
13.584270715713501
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 3534, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.09178638458252, 'TIME_S_1KI': 2.8556271603232934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.7126415205003, 'W': 54.38}
[19.07, 18.48, 19.1, 18.47, 18.95, 18.68, 18.6, 18.62, 18.69, 19.55, 19.01, 19.54, 18.68, 18.64, 18.65, 19.28, 18.79, 18.69, 22.88, 18.87]
342.99
17.1495
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 3534, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.09178638458252, 'TIME_S_1KI': 2.8556271603232934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.7126415205003, 'W': 54.38, 'J_1KI': 209.03017586884556, 'W_1KI': 15.387662705149973, 'W_D': 37.230500000000006, 'J_D': 505.7491908813716, 'W_D_1KI': 10.534946236559142, 'J_D_1KI': 2.981026099762066}

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