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Added more synthetic data!

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cephi 2024-12-17 13:27:18 -05:00
parent 8e7cfec71c
commit a512a4dadc
24 changed files with 700 additions and 0 deletions
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pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json Normal file
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{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 644.013694524765, "TIME_S_1KI": 644.013694524765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 15694.744610672022, "W": 24.244425924397902, "J_1KI": 15694.744610672022, "W_1KI": 24.244425924397902, "W_D": 5.900425924397901, "J_D": 3819.668828884149, "W_D_1KI": 5.900425924397901, "J_D_1KI": 5.900425924397901}

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pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.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 synthetic csr 1000 -ss 10000 -sd 0.3 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 644.013694524765}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3023, 6063, ..., 29994048,
29997006, 30000000]),
col_indices=tensor([ 0, 6, 7, ..., 9989, 9991, 9992]),
values=tensor([0.2093, 0.3818, 0.7570, ..., 0.3871, 0.5537, 0.9186]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 644.013694524765 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3023, 6063, ..., 29994048,
29997006, 30000000]),
col_indices=tensor([ 0, 6, 7, ..., 9989, 9991, 9992]),
values=tensor([0.2093, 0.3818, 0.7570, ..., 0.3871, 0.5537, 0.9186]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 644.013694524765 seconds
[20.6, 20.56, 20.52, 20.4, 20.44, 20.64, 20.76, 20.6, 20.68, 20.6]
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647.354763507843
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 644.013694524765, 'TIME_S_1KI': 644.013694524765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15694.744610672022, 'W': 24.244425924397902}
[20.6, 20.56, 20.52, 20.4, 20.44, 20.64, 20.76, 20.6, 20.68, 20.6, 20.12, 20.2, 20.36, 20.36, 20.2, 20.08, 20.24, 20.0, 20.12, 20.12]
366.88
18.344
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 644.013694524765, 'TIME_S_1KI': 644.013694524765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15694.744610672022, 'W': 24.244425924397902, 'J_1KI': 15694.744610672022, 'W_1KI': 24.244425924397902, 'W_D': 5.900425924397901, 'J_D': 3819.668828884149, 'W_D_1KI': 5.900425924397901, 'J_D_1KI': 5.900425924397901}

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pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json Normal file
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{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654, "TIME_S_1KI": 78.25872588157654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4354.838327121737, "W": 46.22946318790178, "J_1KI": 4354.838327121737, "W_1KI": 46.22946318790178, "W_D": 27.22746318790178, "J_D": 2564.8405165128734, "W_D_1KI": 27.22746318790178, "J_D_1KI": 27.22746318790178}

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pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.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 synthetic csr 1000 -ss 500000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 62, 108, ..., 24999902,
24999957, 25000000]),
col_indices=tensor([ 8122, 13146, 17492, ..., 475877, 485043,
496973]),
values=tensor([0.3209, 0.8894, 0.3965, ..., 0.0269, 0.4047, 0.8747]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.9161, 0.4114, 0.3584, ..., 0.3416, 0.2120, 0.9282])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 78.25872588157654 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 62, 108, ..., 24999902,
24999957, 25000000]),
col_indices=tensor([ 8122, 13146, 17492, ..., 475877, 485043,
496973]),
values=tensor([0.3209, 0.8894, 0.3965, ..., 0.0269, 0.4047, 0.8747]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.9161, 0.4114, 0.3584, ..., 0.3416, 0.2120, 0.9282])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 78.25872588157654 seconds
[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8]
[20.96, 20.88, 21.6, 21.6, 22.56, 24.32, 26.6, 27.68, 30.32, 31.76, 31.8, 31.36, 31.8, 32.64, 38.12, 43.32, 49.08, 53.24, 53.16, 52.56, 52.56, 52.56, 52.6, 52.28, 52.68, 53.0, 53.04, 53.24, 53.08, 53.48, 53.36, 53.16, 53.16, 52.68, 52.84, 52.88, 53.12, 52.96, 52.88, 53.2, 53.16, 53.0, 53.0, 52.96, 53.12, 52.84, 52.76, 53.08, 52.96, 52.84, 52.96, 52.92, 53.12, 53.2, 53.04, 53.28, 53.16, 53.12, 52.68, 52.96, 52.88, 52.72, 53.04, 53.08, 52.76, 52.76, 52.92, 53.32, 53.28, 53.32, 53.44, 53.28, 53.32, 53.4, 53.48, 53.48, 53.4, 53.4, 53.44, 53.12, 53.32, 53.44, 53.56, 53.52, 53.4, 53.36, 53.12, 53.12, 53.12, 53.16]
94.20049524307251
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178}
[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8, 21.64, 21.52, 21.28, 21.2, 21.12, 20.8, 20.84, 20.76, 21.0, 21.08]
380.03999999999996
19.002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178, 'J_1KI': 4354.838327121737, 'W_1KI': 46.22946318790178, 'W_D': 27.22746318790178, 'J_D': 2564.8405165128734, 'W_D_1KI': 27.22746318790178, 'J_D_1KI': 27.22746318790178}

1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317, "TIME_S_1KI": 324.79648518562317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11877.425473241823, "W": 35.646067141867775, "J_1KI": 11877.425473241823, "W_1KI": 35.646067141867775, "W_D": 16.970067141867776, "J_D": 5654.50059192373, "W_D_1KI": 16.970067141867776, "J_D_1KI": 16.970067141867776}

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pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output Normal file
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@ -0,0 +1,45 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 495, 976, ..., 24999061,
24999546, 25000000]),
col_indices=tensor([ 151, 490, 496, ..., 49361, 49363, 49505]),
values=tensor([0.8177, 0.6830, 0.3837, ..., 0.7178, 0.0658, 0.9045]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.7632, 0.2578, 0.8207, ..., 0.6267, 0.5426, 0.4264])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 324.79648518562317 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 495, 976, ..., 24999061,
24999546, 25000000]),
col_indices=tensor([ 151, 490, 496, ..., 49361, 49363, 49505]),
values=tensor([0.8177, 0.6830, 0.3837, ..., 0.7178, 0.0658, 0.9045]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.7632, 0.2578, 0.8207, ..., 0.6267, 0.5426, 0.4264])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 324.79648518562317 seconds
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333.20437359809875
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775}
[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88, 20.44, 20.44, 20.64, 20.68, 20.68, 20.68, 20.68, 20.84, 20.88, 20.88]
373.52
18.676
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775, 'J_1KI': 11877.425473241823, 'W_1KI': 35.646067141867775, 'W_D': 16.970067141867776, 'J_D': 5654.50059192373, 'W_D_1KI': 16.970067141867776, 'J_D_1KI': 16.970067141867776}

1
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 161.33362221717834, "TIME_S_1KI": 161.33362221717834, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4173.615943679809, "W": 24.123159694380117, "J_1KI": 4173.615943679809, "W_1KI": 24.123159694380117, "W_D": 5.260159694380114, "J_D": 910.0750749447333, "W_D_1KI": 5.260159694380114, "J_D_1KI": 5.260159694380114}

45
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@ -0,0 +1,45 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.3 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 161.33362221717834}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1457, 2918, ..., 7497005,
7498478, 7500000]),
col_indices=tensor([ 5, 6, 7, ..., 4995, 4996, 4998]),
values=tensor([0.1779, 0.8323, 0.7588, ..., 0.2380, 0.4426, 0.6569]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 161.33362221717834 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1457, 2918, ..., 7497005,
7498478, 7500000]),
col_indices=tensor([ 5, 6, 7, ..., 4995, 4996, 4998]),
values=tensor([0.1779, 0.8323, 0.7588, ..., 0.2380, 0.4426, 0.6569]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 161.33362221717834 seconds
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173.01282238960266
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 161.33362221717834, 'TIME_S_1KI': 161.33362221717834, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4173.615943679809, 'W': 24.123159694380117}
[20.88, 20.96, 21.04, 20.92, 21.44, 21.32, 21.2, 21.24, 21.24, 20.44, 20.6, 20.44, 20.8, 20.72, 20.84, 21.0, 20.96, 20.96, 20.88, 20.68]
377.26000000000005
18.863000000000003
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 161.33362221717834, 'TIME_S_1KI': 161.33362221717834, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4173.615943679809, 'W': 24.123159694380117, 'J_1KI': 4173.615943679809, 'W_1KI': 24.123159694380117, 'W_D': 5.260159694380114, 'J_D': 910.0750749447333, 'W_D_1KI': 5.260159694380114, 'J_D_1KI': 5.260159694380114}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1423, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.225417137145996, "TIME_S_1KI": 7.185816681058324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2165.9509678459167, "W": 114.32, "J_1KI": 1522.1018748038769, "W_1KI": 80.33731553056921, "W_D": 78.57325, "J_D": 1488.679206475675, "W_D_1KI": 55.21661981728742, "J_D_1KI": 38.802965437306696}

65
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@ -0,0 +1,65 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.375466823577881}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2918, 5897, ..., 29994058,
29997053, 30000000]),
col_indices=tensor([ 2, 5, 6, ..., 9983, 9996, 9997]),
values=tensor([0.7175, 0.6857, 0.0471, ..., 0.3859, 0.1988, 0.0619]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.8128, 0.9693, 0.2211, ..., 0.0242, 0.9676, 0.8056])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 7.375466823577881 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1423', '-ss', '10000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.225417137145996}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3014, 6025, ..., 29993963,
29996988, 30000000]),
col_indices=tensor([ 3, 15, 23, ..., 9994, 9996, 9999]),
values=tensor([0.7948, 0.4683, 0.9975, ..., 0.7409, 0.6873, 0.4627]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 10.225417137145996 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3014, 6025, ..., 29993963,
29996988, 30000000]),
col_indices=tensor([ 3, 15, 23, ..., 9994, 9996, 9999]),
values=tensor([0.7948, 0.4683, 0.9975, ..., 0.7409, 0.6873, 0.4627]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 10.225417137145996 seconds
[40.04, 41.53, 39.4, 39.29, 40.65, 39.48, 39.42, 39.79, 39.24, 39.4]
[114.32]
18.94638705253601
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1423, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.225417137145996, 'TIME_S_1KI': 7.185816681058324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2165.9509678459167, 'W': 114.32}
[40.04, 41.53, 39.4, 39.29, 40.65, 39.48, 39.42, 39.79, 39.24, 39.4, 40.83, 40.13, 39.25, 39.79, 39.29, 39.6, 39.88, 39.16, 39.35, 39.1]
714.935
35.74675
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1423, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.225417137145996, 'TIME_S_1KI': 7.185816681058324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2165.9509678459167, 'W': 114.32, 'J_1KI': 1522.1018748038769, 'W_1KI': 80.33731553056921, 'W_D': 78.57325, 'J_D': 1488.679206475675, 'W_D_1KI': 55.21661981728742, 'J_D_1KI': 38.802965437306696}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1316, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738, "TIME_S_1KI": 7.964192915107703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2349.371359462738, "W": 119.72, "J_1KI": 1785.2365953364272, "W_1KI": 90.9726443768997, "W_D": 83.6515, "J_D": 1641.5673093559742, "W_D_1KI": 63.564969604863215, "J_D_1KI": 48.3016486359143}

89
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@ -0,0 +1,89 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.476217031478882}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 58, 110, ..., 24999904,
24999948, 25000000]),
col_indices=tensor([ 6107, 36475, 44542, ..., 455197, 482838,
484709]),
values=tensor([0.8741, 0.1087, 0.7265, ..., 0.9387, 0.2139, 0.8984]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.2008, 0.1464, 0.5363, ..., 0.5258, 0.1478, 0.0153])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 8.476217031478882 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1238', '-ss', '500000', '-sd', '0.0001', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.872223138809204}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 42, 103, ..., 24999895,
24999951, 25000000]),
col_indices=tensor([ 1782, 32597, 35292, ..., 490788, 494408,
495086]),
values=tensor([0.7532, 0.4055, 0.7849, ..., 0.0826, 0.2837, 0.9366]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.6389, 0.8135, 0.6286, ..., 0.0387, 0.4513, 0.2151])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 9.872223138809204 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1316', '-ss', '500000', '-sd', '0.0001', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 49, 93, ..., 24999907,
24999956, 25000000]),
col_indices=tensor([ 1315, 9605, 36829, ..., 467295, 483577,
490282]),
values=tensor([0.4504, 0.6377, 0.8962, ..., 0.8735, 0.2973, 0.1824]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.1673, 0.3950, 0.6065, ..., 0.2020, 0.8580, 0.9739])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 10.480877876281738 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 49, 93, ..., 24999907,
24999956, 25000000]),
col_indices=tensor([ 1315, 9605, 36829, ..., 467295, 483577,
490282]),
values=tensor([0.4504, 0.6377, 0.8962, ..., 0.8735, 0.2973, 0.1824]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.1673, 0.3950, 0.6065, ..., 0.2020, 0.8580, 0.9739])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 10.480877876281738 seconds
[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68]
[119.72]
19.623883724212646
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72}
[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68, 41.19, 39.75, 40.31, 41.27, 39.67, 39.76, 39.72, 39.58, 39.67, 40.17]
721.3699999999999
36.06849999999999
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72, 'J_1KI': 1785.2365953364272, 'W_1KI': 90.9726443768997, 'W_D': 83.6515, 'J_D': 1641.5673093559742, 'W_D_1KI': 63.564969604863215, 'J_D_1KI': 48.3016486359143}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1728, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495, "TIME_S_1KI": 6.277456741642069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2262.675802116394, "W": 116.47, "J_1KI": 1309.418866965506, "W_1KI": 67.40162037037037, "W_D": 80.83224999999999, "J_D": 1570.3372207918164, "W_D_1KI": 46.7779224537037, "J_D_1KI": 27.070556975522976}

65
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@ -0,0 +1,65 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.073575019836426}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 511, 999, ..., 24998996,
24999515, 25000000]),
col_indices=tensor([ 50, 140, 163, ..., 49849, 49891, 49909]),
values=tensor([0.6896, 0.4241, 0.6835, ..., 0.3809, 0.0330, 0.1679]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.8743, 0.1159, 0.4633, ..., 0.1043, 0.2471, 0.3798])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 6.073575019836426 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1728', '-ss', '50000', '-sd', '0.01', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 553, 1047, ..., 24998955,
24999507, 25000000]),
col_indices=tensor([ 47, 133, 262, ..., 49731, 49773, 49776]),
values=tensor([0.8665, 0.6889, 0.7366, ..., 0.8541, 0.3572, 0.3739]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.0392, 0.1826, 0.6698, ..., 0.4937, 0.5808, 0.8286])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 10.847445249557495 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 553, 1047, ..., 24998955,
24999507, 25000000]),
col_indices=tensor([ 47, 133, 262, ..., 49731, 49773, 49776]),
values=tensor([0.8665, 0.6889, 0.7366, ..., 0.8541, 0.3572, 0.3739]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.0392, 0.1826, 0.6698, ..., 0.4937, 0.5808, 0.8286])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 10.847445249557495 seconds
[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51]
[116.47]
19.427112579345703
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47}
[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51, 40.76, 39.62, 39.66, 39.43, 39.52, 39.33, 39.97, 39.4, 39.29, 39.41]
712.7550000000001
35.637750000000004
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47, 'J_1KI': 1309.418866965506, 'W_1KI': 67.40162037037037, 'W_D': 80.83224999999999, 'J_D': 1570.3372207918164, 'W_D_1KI': 46.7779224537037, 'J_D_1KI': 27.070556975522976}

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.466439723968506, "TIME_S_1KI": 0.5417972732150588, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1987.1806412553788, "W": 136.19, "J_1KI": 102.86678958770985, "W_1KI": 7.04990164613314, "W_D": 100.84025, "J_D": 1471.3840418485402, "W_D_1KI": 5.2200150119059945, "J_D_1KI": 0.2702150849935808}

85
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['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.6186139583587646}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1488, 3016, ..., 7497085,
7498552, 7500000]),
col_indices=tensor([ 6, 10, 18, ..., 4990, 4991, 4992]),
values=tensor([0.0319, 0.7689, 0.5437, ..., 0.3456, 0.3746, 0.7534]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.3033, 0.5024, 0.5149, ..., 0.3250, 0.9742, 0.7218])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 0.6186139583587646 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16973', '-ss', '5000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.225086212158203}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1496, 2988, ..., 7496994,
7498506, 7500000]),
col_indices=tensor([ 3, 6, 16, ..., 4989, 4992, 4998]),
values=tensor([0.5767, 0.7515, 0.3496, ..., 0.2965, 0.3271, 0.6066]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.6092, 0.5985, 0.2131, ..., 0.5175, 0.3653, 0.9642])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 9.225086212158203 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19318', '-ss', '5000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.466439723968506}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1490, 3014, ..., 7497019,
7498487, 7500000]),
col_indices=tensor([ 4, 5, 6, ..., 4990, 4995, 4998]),
values=tensor([0.5409, 0.4022, 0.8537, ..., 0.7192, 0.8151, 0.0953]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.466439723968506 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1490, 3014, ..., 7497019,
7498487, 7500000]),
col_indices=tensor([ 4, 5, 6, ..., 4990, 4995, 4998]),
values=tensor([0.5409, 0.4022, 0.8537, ..., 0.7192, 0.8151, 0.0953]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.466439723968506 seconds
[39.82, 39.03, 39.04, 38.94, 39.09, 39.36, 39.4, 38.84, 39.28, 40.17]
[136.19]
14.591237545013428
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.466439723968506, 'TIME_S_1KI': 0.5417972732150588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.1806412553788, 'W': 136.19}
[39.82, 39.03, 39.04, 38.94, 39.09, 39.36, 39.4, 38.84, 39.28, 40.17, 40.47, 39.28, 39.08, 39.29, 39.92, 39.06, 39.2, 39.0, 39.43, 39.05]
706.9950000000001
35.34975000000001
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.466439723968506, 'TIME_S_1KI': 0.5417972732150588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.1806412553788, 'W': 136.19, 'J_1KI': 102.86678958770985, 'W_1KI': 7.04990164613314, 'W_D': 100.84025, 'J_D': 1471.3840418485402, 'W_D_1KI': 5.2200150119059945, 'J_D_1KI': 0.2702150849935808}

1
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 12.322984457015991, "TIME_S_1KI": 12.322984457015991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4274.3693664526945, "W": 53.17000000000001, "J_1KI": 4274.3693664526945, "W_1KI": 53.17000000000001, "W_D": 36.76275000000001, "J_D": 2955.380335274757, "W_D_1KI": 36.76275000000001, "J_D_1KI": 36.76275000000001}

45
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@ -0,0 +1,45 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 12.322984457015991}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3028, 6045, ..., 29993985,
29996995, 30000000]),
col_indices=tensor([ 0, 1, 2, ..., 9992, 9993, 9997]),
values=tensor([0.0070, 0.5345, 0.8585, ..., 0.9349, 0.6772, 0.9628]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 12.322984457015991 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3028, 6045, ..., 29993985,
29996995, 30000000]),
col_indices=tensor([ 0, 1, 2, ..., 9992, 9993, 9997]),
values=tensor([0.0070, 0.5345, 0.8585, ..., 0.9349, 0.6772, 0.9628]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 12.322984457015991 seconds
[18.78, 17.72, 18.05, 18.08, 21.63, 19.29, 17.99, 17.89, 17.96, 17.7]
[53.17]
80.39062190055847
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 12.322984457015991, 'TIME_S_1KI': 12.322984457015991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4274.3693664526945, 'W': 53.17000000000001}
[18.78, 17.72, 18.05, 18.08, 21.63, 19.29, 17.99, 17.89, 17.96, 17.7, 18.37, 17.7, 18.04, 17.87, 17.81, 17.74, 17.89, 18.06, 17.94, 18.12]
328.145
16.407249999999998
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 12.322984457015991, 'TIME_S_1KI': 12.322984457015991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4274.3693664526945, 'W': 53.17000000000001, 'J_1KI': 4274.3693664526945, 'W_1KI': 53.17000000000001, 'W_D': 36.76275000000001, 'J_D': 2955.380335274757, 'W_D_1KI': 36.76275000000001, 'J_D_1KI': 36.76275000000001}

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186, "TIME_S_1KI": 15.963828563690184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3837.0465870952603, "W": 57.08, "J_1KI": 3837.0465870952603, "W_1KI": 57.08, "W_D": 41.032999999999994, "J_D": 2758.3309847280975, "W_D_1KI": 41.032999999999994, "J_D_1KI": 41.032999999999994}

47
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output Normal file
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@ -0,0 +1,47 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 49, 110, ..., 24999910,
24999948, 25000000]),
col_indices=tensor([ 6869, 7642, 11671, ..., 455502, 470939,
478512]),
values=tensor([0.8757, 0.6946, 0.8023, ..., 0.8472, 0.7183, 0.5606]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.4126, 0.9616, 0.6093, ..., 0.3863, 0.8636, 0.0433])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 15.963828563690186 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 49, 110, ..., 24999910,
24999948, 25000000]),
col_indices=tensor([ 6869, 7642, 11671, ..., 455502, 470939,
478512]),
values=tensor([0.8757, 0.6946, 0.8023, ..., 0.8472, 0.7183, 0.5606]),
size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.4126, 0.9616, 0.6093, ..., 0.3863, 0.8636, 0.0433])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([500000, 500000])
Rows: 500000
Size: 250000000000
NNZ: 25000000
Density: 0.0001
Time: 15.963828563690186 seconds
[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59]
[57.08]
67.22225975990295
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08}
[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59, 18.37, 17.79, 17.89, 17.88, 18.0, 17.73, 17.87, 17.96, 17.67, 17.76]
320.94000000000005
16.047000000000004
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08, 'J_1KI': 3837.0465870952603, 'W_1KI': 57.08, 'W_D': 41.032999999999994, 'J_D': 2758.3309847280975, 'W_D_1KI': 41.032999999999994, 'J_D_1KI': 41.032999999999994}

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389, "TIME_S_1KI": 10.122996807098389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3257.7272432255745, "W": 53.79, "J_1KI": 3257.7272432255745, "W_1KI": 53.79, "W_D": 37.451750000000004, "J_D": 2268.2206038571003, "W_D_1KI": 37.451750000000004, "J_D_1KI": 37.451750000000004}

45
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@ -0,0 +1,45 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 509, 1001, ..., 24999037,
24999516, 25000000]),
col_indices=tensor([ 11, 136, 275, ..., 49665, 49739, 49958]),
values=tensor([0.3161, 0.2173, 0.0956, ..., 0.2198, 0.0588, 0.5951]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.4562, 0.2414, 0.7128, ..., 0.4854, 0.8312, 0.1880])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 10.122996807098389 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 509, 1001, ..., 24999037,
24999516, 25000000]),
col_indices=tensor([ 11, 136, 275, ..., 49665, 49739, 49958]),
values=tensor([0.3161, 0.2173, 0.0956, ..., 0.2198, 0.0588, 0.5951]),
size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
tensor([0.4562, 0.2414, 0.7128, ..., 0.4854, 0.8312, 0.1880])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([50000, 50000])
Rows: 50000
Size: 2500000000
NNZ: 25000000
Density: 0.01
Time: 10.122996807098389 seconds
[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81]
[53.79]
60.56380820274353
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79}
[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81, 18.18, 18.11, 17.85, 17.68, 17.63, 17.92, 17.78, 17.67, 18.05, 18.04]
326.765
16.33825
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79, 'J_1KI': 3257.7272432255745, 'W_1KI': 53.79, 'W_D': 37.451750000000004, 'J_D': 2268.2206038571003, 'W_D_1KI': 37.451750000000004, 'J_D_1KI': 37.451750000000004}

1
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5572, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.464208841323853, "TIME_S_1KI": 1.8779987152411795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1351.3364907836915, "W": 85.76, "J_1KI": 242.5227011456733, "W_1KI": 15.391241923905243, "W_D": 69.66475, "J_D": 1097.7206016362309, "W_D_1KI": 12.502647164393395, "J_D_1KI": 2.2438347387640696}

65
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@ -0,0 +1,65 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.8840982913970947}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1512, 3045, ..., 7497001,
7498469, 7500000]),
col_indices=tensor([ 1, 3, 6, ..., 4985, 4987, 4999]),
values=tensor([0.2926, 0.1712, 0.0979, ..., 0.0306, 0.4197, 0.0742]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.8071, 0.3432, 0.7096, ..., 0.6319, 0.9579, 0.4287])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 1.8840982913970947 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', 'synthetic', 'csr', '5572', '-ss', '5000', '-sd', '0.3', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.464208841323853}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1440, 2946, ..., 7496997,
7498526, 7500000]),
col_indices=tensor([ 4, 11, 17, ..., 4977, 4981, 4999]),
values=tensor([0.5270, 0.2118, 0.1968, ..., 0.5430, 0.9062, 0.9328]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.464208841323853 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1440, 2946, ..., 7496997,
7498526, 7500000]),
col_indices=tensor([ 4, 11, 17, ..., 4977, 4981, 4999]),
values=tensor([0.5270, 0.2118, 0.1968, ..., 0.5430, 0.9062, 0.9328]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.464208841323853 seconds
[18.1, 18.05, 18.13, 17.56, 18.21, 17.81, 17.97, 17.66, 18.07, 17.92]
[85.76]
15.757188558578491
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5572, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.464208841323853, 'TIME_S_1KI': 1.8779987152411795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.3364907836915, 'W': 85.76}
[18.1, 18.05, 18.13, 17.56, 18.21, 17.81, 17.97, 17.66, 18.07, 17.92, 17.91, 17.43, 18.3, 17.79, 17.88, 17.63, 18.23, 17.64, 17.78, 17.6]
321.905
16.09525
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5572, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.464208841323853, 'TIME_S_1KI': 1.8779987152411795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.3364907836915, 'W': 85.76, 'J_1KI': 242.5227011456733, 'W_1KI': 15.391241923905243, 'W_D': 69.66475, 'J_D': 1097.7206016362309, 'W_D_1KI': 12.502647164393395, 'J_D_1KI': 2.2438347387640696}