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cephi 2024-12-17 16:13:28 -05:00
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2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.60694670677185, "TIME_S_1KI": 106.60694670677185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2300.291395263673, "W": 20.29267619003776, "J_1KI": 2300.291395263673, "W_1KI": 20.29267619003776, "W_D": 5.365676190037762, "J_D": 608.2302134094255, "W_D_1KI": 5.365676190037762, "J_D_1KI": 5.365676190037762}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.56549715995789, "TIME_S_1KI": 106.56549715995789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2685.1749729537964, "W": 24.108298502680796, "J_1KI": 2685.1749729537964, "W_1KI": 24.108298502680796, "W_D": 5.534298502680798, "J_D": 616.4084881644253, "W_D_1KI": 5.534298502680798, "J_D_1KI": 5.534298502680798}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
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@ -1,14 +1,14 @@
['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 0.05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.60694670677185}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.56549715995789}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 513, 983, ..., 4998990,
4999536, 5000000]),
col_indices=tensor([ 3, 6, 54, ..., 9902, 9976, 9979]),
values=tensor([0.3821, 0.3276, 0.4096, ..., 0.9878, 0.3843, 0.9439]),
tensor(crow_indices=tensor([ 0, 506, 1012, ..., 4998963,
4999486, 5000000]),
col_indices=tensor([ 12, 18, 20, ..., 9951, 9962, 9995]),
values=tensor([0.8717, 0.8420, 0.2460, ..., 0.1141, 0.5771, 0.8192]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.8065, 0.5635, 0.0733, ..., 0.7202, 0.3714, 0.0072])
tensor([0.3519, 0.6034, 0.3549, ..., 0.4924, 0.0253, 0.7056])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 106.60694670677185 seconds
Time: 106.56549715995789 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, 513, 983, ..., 4998990,
4999536, 5000000]),
col_indices=tensor([ 3, 6, 54, ..., 9902, 9976, 9979]),
values=tensor([0.3821, 0.3276, 0.4096, ..., 0.9878, 0.3843, 0.9439]),
tensor(crow_indices=tensor([ 0, 506, 1012, ..., 4998963,
4999486, 5000000]),
col_indices=tensor([ 12, 18, 20, ..., 9951, 9962, 9995]),
values=tensor([0.8717, 0.8420, 0.2460, ..., 0.1141, 0.5771, 0.8192]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.8065, 0.5635, 0.0733, ..., 0.7202, 0.3714, 0.0072])
tensor([0.3519, 0.6034, 0.3549, ..., 0.4924, 0.0253, 0.7056])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 106.60694670677185 seconds
Time: 106.56549715995789 seconds
[16.56, 16.36, 16.32, 16.32, 16.44, 16.44, 16.32, 16.32, 16.32, 16.04]
[16.0, 16.2, 16.52, 20.8, 21.96, 24.64, 26.28, 24.4, 23.76, 23.12, 21.52, 21.52, 20.64, 20.52, 20.48, 20.32, 20.28, 20.28, 20.28, 20.6, 20.68, 20.88, 20.8, 20.8, 20.64, 20.6, 20.6, 20.4, 20.32, 20.48, 20.32, 20.16, 20.32, 20.36, 20.24, 20.4, 20.4, 20.56, 20.48, 20.48, 20.84, 20.92, 20.8, 20.68, 20.48, 20.44, 20.28, 20.68, 20.68, 20.56, 20.52, 20.4, 20.24, 20.28, 20.32, 20.32, 20.56, 20.6, 20.56, 20.76, 21.0, 21.0, 21.0, 21.04, 21.0, 20.8, 20.56, 20.4, 20.32, 20.24, 20.32, 20.72, 20.68, 20.68, 20.84, 20.8, 20.56, 20.56, 20.72, 20.8, 20.72, 20.92, 20.92, 20.88, 20.92, 20.92, 20.88, 20.88, 20.68, 20.32, 20.12, 20.08, 20.12, 20.4, 20.48, 20.56, 20.64, 20.52, 20.52, 20.4, 20.32, 20.28, 20.24, 20.24, 20.36, 20.52, 20.32, 20.32, 20.44, 20.44, 20.44, 20.44]
113.35574340820312
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.60694670677185, 'TIME_S_1KI': 106.60694670677185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.291395263673, 'W': 20.29267619003776}
[16.56, 16.36, 16.32, 16.32, 16.44, 16.44, 16.32, 16.32, 16.32, 16.04, 16.52, 16.72, 16.8, 16.96, 17.08, 17.12, 17.0, 16.64, 16.52, 16.6]
298.53999999999996
14.926999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.60694670677185, 'TIME_S_1KI': 106.60694670677185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.291395263673, 'W': 20.29267619003776, 'J_1KI': 2300.291395263673, 'W_1KI': 20.29267619003776, 'W_D': 5.365676190037762, 'J_D': 608.2302134094255, 'W_D_1KI': 5.365676190037762, 'J_D_1KI': 5.365676190037762}
[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6]
[20.64, 20.64, 21.08, 23.16, 24.52, 27.56, 29.68, 29.72, 28.4, 27.2, 27.2, 24.36, 24.36, 24.36, 24.84, 24.88, 24.76, 24.96, 24.72, 24.4, 24.36, 24.16, 24.12, 24.12, 24.24, 24.64, 24.72, 24.76, 24.88, 24.76, 24.4, 24.4, 24.32, 24.32, 24.24, 24.24, 24.44, 24.36, 24.16, 24.16, 24.2, 24.24, 24.4, 24.68, 24.56, 24.6, 24.52, 24.48, 24.48, 24.24, 24.36, 24.36, 24.44, 24.52, 24.52, 24.32, 24.44, 24.28, 24.04, 23.96, 23.96, 24.04, 24.16, 24.28, 24.36, 24.44, 24.48, 24.56, 24.64, 24.56, 24.48, 24.24, 24.24, 24.24, 24.2, 24.28, 24.36, 24.2, 24.4, 24.12, 24.16, 24.24, 24.48, 24.48, 24.8, 24.8, 24.92, 24.92, 24.96, 24.84, 24.72, 24.88, 24.72, 24.68, 24.48, 24.28, 24.0, 24.04, 24.04, 24.16, 24.32, 24.28, 24.32, 24.32, 24.48, 24.4, 24.68, 24.8, 24.64, 24.48]
111.37969660758972
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796}
[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6, 20.64, 20.6, 20.6, 20.72, 20.8, 20.6, 20.52, 20.28, 20.68, 20.6]
371.47999999999996
18.573999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796, 'J_1KI': 2685.1749729537964, 'W_1KI': 24.108298502680796, 'W_D': 5.534298502680798, 'J_D': 616.4084881644253, 'W_D_1KI': 5.534298502680798, 'J_D_1KI': 5.534298502680798}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 22.49751091003418, "TIME_S_1KI": 224.9751091003418, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 714.621188735962, "W": 24.29855166830563, "J_1KI": 7146.211887359621, "W_1KI": 242.98551668305632, "W_D": 5.645551668305629, "J_D": 166.03585676002507, "W_D_1KI": 56.45551668305629, "J_D_1KI": 564.5551668305628}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 213.33555269241333, "TIME_S_1KI": 213.33555269241333, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5226.200544624329, "W": 24.133137838334523, "J_1KI": 5226.200544624329, "W_1KI": 24.133137838334523, "W_D": 5.8551378383345245, "J_D": 1267.97123376751, "W_D_1KI": 5.8551378383345245, "J_D_1KI": 5.8551378383345245}

44
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
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@ -1,14 +1,14 @@
['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 100 -ss 10000 -sd 0.1 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 22.49751091003418}
['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 0.1 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 213.33555269241333}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1002, 1924, ..., 9998003,
9998995, 10000000]),
col_indices=tensor([ 0, 2, 31, ..., 9969, 9996, 9997]),
values=tensor([0.9177, 0.7034, 0.7745, ..., 0.5598, 0.0709, 0.5319]),
tensor(crow_indices=tensor([ 0, 1053, 2094, ..., 9997974,
9998964, 10000000]),
col_indices=tensor([ 7, 12, 13, ..., 9961, 9986, 9993]),
values=tensor([0.1704, 0.0678, 0.1172, ..., 0.2208, 0.7370, 0.2820]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.7893, 0.1843, 0.8169, ..., 0.5734, 0.3496, 0.5102])
tensor([0.7446, 0.0915, 0.8679, ..., 0.5982, 0.0596, 0.6566])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 22.49751091003418 seconds
Time: 213.33555269241333 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, 1002, 1924, ..., 9998003,
9998995, 10000000]),
col_indices=tensor([ 0, 2, 31, ..., 9969, 9996, 9997]),
values=tensor([0.9177, 0.7034, 0.7745, ..., 0.5598, 0.0709, 0.5319]),
tensor(crow_indices=tensor([ 0, 1053, 2094, ..., 9997974,
9998964, 10000000]),
col_indices=tensor([ 7, 12, 13, ..., 9961, 9986, 9993]),
values=tensor([0.1704, 0.0678, 0.1172, ..., 0.2208, 0.7370, 0.2820]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.7893, 0.1843, 0.8169, ..., 0.5734, 0.3496, 0.5102])
tensor([0.7446, 0.0915, 0.8679, ..., 0.5982, 0.0596, 0.6566])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 22.49751091003418 seconds
Time: 213.33555269241333 seconds
[20.52, 20.56, 20.88, 20.92, 21.04, 21.0, 20.96, 20.72, 20.72, 20.72]
[20.52, 20.44, 20.56, 24.12, 26.0, 27.96, 31.12, 31.04, 30.6, 29.2, 26.72, 25.56, 25.56, 24.04, 24.24, 24.24, 24.04, 24.2, 24.16, 24.08, 24.12, 24.28, 24.16, 24.28, 24.28, 24.28, 24.36, 24.48, 24.44]
29.410032272338867
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 22.49751091003418, 'TIME_S_1KI': 224.9751091003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 714.621188735962, 'W': 24.29855166830563}
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373.06000000000006
18.653000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 22.49751091003418, 'TIME_S_1KI': 224.9751091003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 714.621188735962, 'W': 24.29855166830563, 'J_1KI': 7146.211887359621, 'W_1KI': 242.98551668305632, 'W_D': 5.645551668305629, 'J_D': 166.03585676002507, 'W_D_1KI': 56.45551668305629, 'J_D_1KI': 564.5551668305628}
[20.4, 20.4, 20.4, 20.36, 20.36, 20.16, 20.32, 20.2, 20.2, 20.28]
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216.55702543258667
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523}
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365.55999999999995
18.278
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523, 'J_1KI': 5226.200544624329, 'W_1KI': 24.133137838334523, 'W_D': 5.8551378383345245, 'J_D': 1267.97123376751, 'W_D_1KI': 5.8551378383345245, 'J_D_1KI': 5.8551378383345245}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.36744213104248, "TIME_S_1KI": 423.6744213104248, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1267.0327228164674, "W": 24.507310404611225, "J_1KI": 12670.327228164673, "W_1KI": 245.07310404611223, "W_D": 5.971310404611227, "J_D": 308.71791134262116, "W_D_1KI": 59.71310404611227, "J_D_1KI": 597.1310404611228}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518, "TIME_S_1KI": 424.4943735599518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10473.538437499996, "W": 24.301530579701517, "J_1KI": 10473.538437499996, "W_1KI": 24.301530579701517, "W_D": 5.865530579701517, "J_D": 2527.942006835934, "W_D_1KI": 5.865530579701517, "J_D_1KI": 5.865530579701517}

44
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
View File

@ -1,14 +1,14 @@
['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 100 -ss 10000 -sd 0.2 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.36744213104248}
['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 0.2 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1958, 3991, ..., 19996077,
19998030, 20000000]),
col_indices=tensor([ 7, 14, 15, ..., 9992, 9994, 9996]),
values=tensor([0.5268, 0.0225, 0.1392, ..., 0.0209, 0.3714, 0.4352]),
tensor(crow_indices=tensor([ 0, 1949, 3966, ..., 19996026,
19998069, 20000000]),
col_indices=tensor([ 7, 21, 26, ..., 9986, 9990, 9999]),
values=tensor([0.4961, 0.8613, 0.9281, ..., 0.1556, 0.7430, 0.8560]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.3357, 0.5966, 0.1485, ..., 0.1370, 0.3281, 0.6890])
tensor([0.5082, 0.3131, 0.5448, ..., 0.5922, 0.3726, 0.5476])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 42.36744213104248 seconds
Time: 424.4943735599518 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, 1958, 3991, ..., 19996077,
19998030, 20000000]),
col_indices=tensor([ 7, 14, 15, ..., 9992, 9994, 9996]),
values=tensor([0.5268, 0.0225, 0.1392, ..., 0.0209, 0.3714, 0.4352]),
tensor(crow_indices=tensor([ 0, 1949, 3966, ..., 19996026,
19998069, 20000000]),
col_indices=tensor([ 7, 21, 26, ..., 9986, 9990, 9999]),
values=tensor([0.4961, 0.8613, 0.9281, ..., 0.1556, 0.7430, 0.8560]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.3357, 0.5966, 0.1485, ..., 0.1370, 0.3281, 0.6890])
tensor([0.5082, 0.3131, 0.5448, ..., 0.5922, 0.3726, 0.5476])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 42.36744213104248 seconds
Time: 424.4943735599518 seconds
[20.84, 20.72, 20.6, 20.6, 20.4, 20.36, 20.4, 20.32, 20.32, 20.4]
[20.44, 20.6, 20.96, 23.28, 24.24, 25.88, 29.52, 30.04, 29.56, 29.8, 30.12, 27.4, 27.4, 26.84, 25.8, 24.52, 24.6, 24.56, 24.52, 24.48, 24.4, 24.48, 24.4, 24.52, 24.52, 24.68, 24.84, 24.72, 24.8, 24.72, 24.68, 24.52, 24.56, 24.52, 24.4, 24.32, 24.48, 24.48, 24.2, 24.28, 24.4, 24.12, 24.24, 24.16, 24.32, 24.44, 24.36, 24.32, 24.36, 24.36, 24.24]
51.70019483566284
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.36744213104248, 'TIME_S_1KI': 423.6744213104248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.0327228164674, 'W': 24.507310404611225}
[20.84, 20.72, 20.6, 20.6, 20.4, 20.36, 20.4, 20.32, 20.32, 20.4, 20.56, 20.36, 20.44, 20.72, 20.76, 20.8, 20.96, 20.92, 20.76, 20.76]
370.71999999999997
18.535999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.36744213104248, 'TIME_S_1KI': 423.6744213104248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.0327228164674, 'W': 24.507310404611225, 'J_1KI': 12670.327228164673, 'W_1KI': 245.07310404611223, 'W_D': 5.971310404611227, 'J_D': 308.71791134262116, 'W_D_1KI': 59.71310404611227, 'J_D_1KI': 597.1310404611228}
[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52]
[20.4, 20.4, 20.84, 23.24, 23.96, 25.96, 29.08, 30.08, 29.8, 30.04, 30.84, 27.8, 27.8, 27.2, 26.24, 24.6, 24.44, 24.32, 24.24, 24.32, 24.2, 24.44, 24.6, 24.6, 24.72, 24.72, 24.84, 24.88, 24.92, 24.96, 24.6, 24.48, 24.36, 24.16, 24.36, 24.64, 24.68, 24.68, 24.72, 24.68, 24.52, 24.52, 24.4, 24.44, 24.24, 24.36, 24.4, 24.48, 24.44, 24.44, 24.44, 24.24, 24.28, 24.24, 24.44, 24.4, 24.28, 24.24, 24.28, 24.24, 24.48, 24.84, 24.84, 24.56, 24.64, 24.6, 24.88, 24.68, 24.56, 24.56, 24.4, 24.4, 24.32, 24.36, 24.44, 24.44, 24.36, 24.48, 24.64, 24.8, 24.68, 24.92, 24.88, 24.76, 24.88, 24.68, 24.36, 24.36, 24.4, 24.44, 24.68, 24.76, 24.56, 24.64, 24.24, 24.32, 24.72, 24.6, 24.96, 24.96, 25.12, 25.04, 24.44, 24.36, 24.28, 24.16, 24.4, 24.44, 24.56, 24.68, 24.72, 24.48, 24.48, 24.4, 24.36, 24.24, 24.2, 24.52, 24.64, 24.84, 24.8, 24.56, 24.28, 24.16, 24.16, 24.16, 24.28, 24.4, 24.6, 24.64, 24.52, 24.56, 24.44, 24.36, 24.56, 24.84, 24.64, 24.64, 24.84, 24.72, 24.48, 24.6, 24.64, 24.68, 24.6, 24.4, 24.4, 24.16, 24.12, 24.12, 24.28, 24.4, 24.48, 24.76, 24.6, 24.44, 24.28, 24.32, 24.24, 24.32, 24.4, 24.4, 24.56, 24.48, 24.48, 24.28, 24.68, 24.52, 24.32, 24.4, 24.56, 24.28, 24.4, 24.72, 24.72, 24.76, 24.76, 24.72, 24.64, 24.56, 24.6, 24.56, 24.64, 24.48, 24.44, 24.4, 24.4, 24.36, 24.32, 24.48, 24.48, 24.48, 24.32, 24.4, 24.08, 24.16, 24.28, 24.44, 24.48, 24.48, 24.6, 24.56, 24.44, 24.4, 24.44, 24.44, 24.64, 24.72, 24.44, 24.48, 24.32, 24.32, 24.4, 24.28, 24.52, 24.56, 24.68, 24.68, 24.72, 24.88, 24.96, 25.04, 24.84, 24.84, 24.84, 24.6, 24.64, 24.6, 24.6, 24.6, 24.36, 24.28, 24.32, 24.48, 24.48, 24.44, 24.44, 24.56, 24.56, 24.84, 24.72, 24.8, 24.72, 24.6, 24.56, 24.68, 24.88, 24.88, 24.88, 24.88, 24.48, 24.48, 24.32, 24.32, 24.36, 24.36, 24.44, 24.32, 24.36, 24.44, 24.32, 24.32, 24.28, 24.64, 24.8, 24.8, 24.96, 24.76, 24.8, 24.68, 24.52, 24.64, 24.48, 24.48, 24.56, 24.48, 24.32, 24.28, 24.32, 24.36, 24.28, 24.36, 24.4, 24.28, 24.36, 24.36, 24.32, 24.16, 24.04, 24.2, 24.2, 24.32, 24.36, 24.52, 24.36, 24.44, 24.56, 24.64, 24.64, 24.84, 24.8, 24.8, 24.64, 24.48, 24.52, 24.4, 24.6, 24.32, 24.24, 24.2, 24.2, 24.16, 24.2, 24.56, 24.56, 24.68, 24.8, 24.8, 24.68, 24.64, 24.68, 24.6, 24.64, 24.64, 24.6, 24.6, 24.4, 24.28, 24.36, 24.4, 24.48, 24.44, 24.48, 24.52, 24.36, 24.36, 24.44, 24.32, 24.24, 24.44, 24.44, 24.68, 24.56, 24.4, 24.52, 24.4, 24.24, 24.24, 24.4, 24.44, 24.48, 24.6, 24.76, 24.76, 24.64, 24.72, 24.44, 24.48, 24.6, 24.56, 24.56, 24.64, 24.72, 24.56, 24.4, 24.24, 24.2, 24.16, 24.08, 24.04, 24.12, 24.32, 24.32, 24.36, 24.36, 24.6, 24.56, 24.52, 24.76, 24.6, 24.68, 24.56, 24.76, 24.8, 24.76, 24.76, 24.88, 24.64, 24.76, 24.52, 24.44, 24.2, 24.68, 24.48, 24.84, 25.0, 25.12, 25.12, 24.96, 24.84, 24.52, 24.28, 24.2, 24.48, 24.48, 24.52, 24.52, 24.56, 24.6, 24.6, 24.76, 24.76, 25.0, 24.88, 24.92, 24.88, 24.64, 24.72, 24.64, 24.68, 24.68, 24.56, 24.56, 24.36, 24.28, 24.32]
430.982666015625
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517}
[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52, 20.2, 20.36, 20.48, 20.56, 20.28, 20.2, 20.2, 20.24, 20.52, 20.4]
368.72
18.436
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517, 'J_1KI': 10473.538437499996, 'W_1KI': 24.301530579701517, 'W_D': 5.865530579701517, 'J_D': 2527.942006835934, 'W_D_1KI': 5.865530579701517, 'J_D_1KI': 5.865530579701517}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "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": 64.48762941360474, "TIME_S_1KI": 644.8762941360474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.6232081508633, "W": 24.560001352289554, "J_1KI": 18666.23208150863, "W_1KI": 245.60001352289555, "W_D": 6.134001352289552, "J_D": 466.19986370420406, "W_D_1KI": 61.34001352289552, "J_D_1KI": 613.4001352289552}
{"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": 637.8268127441406, "TIME_S_1KI": 637.8268127441406, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 15996.775521488189, "W": 24.370595996658984, "J_1KI": 15996.775521488189, "W_1KI": 24.370595996658984, "W_D": 5.917595996658985, "J_D": 3884.2896906743035, "W_D_1KI": 5.917595996658985, "J_D_1KI": 5.917595996658985}

44
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
View File

@ -1,14 +1,14 @@
['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 100 -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": 64.48762941360474}
['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": 637.8268127441406}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 3018, 6012, ..., 29994075,
29997046, 30000000]),
col_indices=tensor([ 12, 13, 14, ..., 9975, 9986, 9989]),
values=tensor([0.7172, 0.9331, 0.0150, ..., 0.2187, 0.8326, 0.8808]),
tensor(crow_indices=tensor([ 0, 2961, 5942, ..., 29993896,
29996981, 30000000]),
col_indices=tensor([ 2, 5, 14, ..., 9994, 9995, 9996]),
values=tensor([0.0155, 0.6045, 0.5805, ..., 0.2136, 0.4050, 0.0107]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.9528, 0.5520, 0.9475, ..., 0.9876, 0.0053, 0.6635])
tensor([0.2173, 0.9101, 0.0107, ..., 0.2401, 0.6161, 0.6478])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 64.48762941360474 seconds
Time: 637.8268127441406 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, 3018, 6012, ..., 29994075,
29997046, 30000000]),
col_indices=tensor([ 12, 13, 14, ..., 9975, 9986, 9989]),
values=tensor([0.7172, 0.9331, 0.0150, ..., 0.2187, 0.8326, 0.8808]),
tensor(crow_indices=tensor([ 0, 2961, 5942, ..., 29993896,
29996981, 30000000]),
col_indices=tensor([ 2, 5, 14, ..., 9994, 9995, 9996]),
values=tensor([0.0155, 0.6045, 0.5805, ..., 0.2136, 0.4050, 0.0107]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.9528, 0.5520, 0.9475, ..., 0.9876, 0.0053, 0.6635])
tensor([0.2173, 0.9101, 0.0107, ..., 0.2401, 0.6161, 0.6478])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 64.48762941360474 seconds
Time: 637.8268127441406 seconds
[20.6, 20.44, 20.32, 20.24, 20.36, 20.64, 20.68, 20.8, 20.92, 20.92]
[20.68, 20.44, 20.2, 24.28, 25.52, 26.88, 28.28, 30.32, 31.12, 31.36, 32.28, 31.6, 29.96, 28.72, 28.72, 27.68, 26.76, 25.88, 24.68, 25.0, 25.04, 25.08, 24.72, 24.36, 24.4, 24.2, 24.2, 24.16, 24.36, 24.32, 24.36, 24.28, 24.28, 24.32, 24.44, 24.56, 24.52, 24.84, 24.84, 24.84, 25.0, 24.92, 24.92, 24.6, 24.52, 24.48, 24.4, 24.32, 24.4, 24.0, 24.24, 24.24, 24.32, 24.4, 24.6, 24.72, 24.92, 24.72, 24.64, 24.64, 24.8, 24.88, 24.8, 24.8, 24.92, 24.8, 24.52, 24.44, 24.24, 24.16, 24.16, 24.12, 24.16, 24.32, 24.6]
76.00256943702698
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, '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': 64.48762941360474, 'TIME_S_1KI': 644.8762941360474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.6232081508633, 'W': 24.560001352289554}
[20.6, 20.44, 20.32, 20.24, 20.36, 20.64, 20.68, 20.8, 20.92, 20.92, 20.28, 20.52, 20.52, 20.56, 20.2, 20.36, 20.4, 20.2, 20.28, 20.36]
368.52000000000004
18.426000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, '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': 64.48762941360474, 'TIME_S_1KI': 644.8762941360474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.6232081508633, 'W': 24.560001352289554, 'J_1KI': 18666.23208150863, 'W_1KI': 245.60001352289555, 'W_D': 6.134001352289552, 'J_D': 466.19986370420406, 'W_D_1KI': 61.34001352289552, 'J_D_1KI': 613.4001352289552}
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656.396565914154
{'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': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984}
[20.6, 20.64, 20.76, 20.6, 20.44, 20.44, 20.48, 20.64, 20.64, 20.88, 20.28, 20.44, 20.44, 20.64, 20.68, 20.44, 20.36, 20.36, 20.12, 20.12]
369.06
18.453
{'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': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984, 'J_1KI': 15996.775521488189, 'W_1KI': 24.370595996658984, 'W_D': 5.917595996658985, 'J_D': 3884.2896906743035, 'W_D_1KI': 5.917595996658985, 'J_D_1KI': 5.917595996658985}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
View File

@ -1 +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": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.625333547592163, "TIME_S_1KI": 26.625333547592163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 615.7839365768435, "W": 20.26185984115326, "J_1KI": 615.7839365768435, "W_1KI": 20.26185984115326, "W_D": 5.26185984115326, "J_D": 159.91467674255395, "W_D_1KI": 5.26185984115326, "J_D_1KI": 5.26185984115326}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.830852508544922, "TIME_S_1KI": 26.830852508544922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.3529372215271, "W": 23.831275335163394, "J_1KI": 724.3529372215271, "W_1KI": 23.831275335163394, "W_D": 5.2862753351633955, "J_D": 160.67663237214092, "W_D_1KI": 5.2862753351633955, "J_D_1KI": 5.2862753351633955}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
View File

@ -1,14 +1,14 @@
['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 0.05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.625333547592163}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.830852508544922}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 251, 521, ..., 1249514,
1249753, 1250000]),
col_indices=tensor([ 14, 21, 29, ..., 4968, 4983, 4999]),
values=tensor([0.5630, 0.8243, 0.2167, ..., 0.8539, 0.0380, 0.9608]),
tensor(crow_indices=tensor([ 0, 229, 490, ..., 1249494,
1249740, 1250000]),
col_indices=tensor([ 18, 28, 58, ..., 4928, 4934, 4938]),
values=tensor([0.5574, 0.5312, 0.0435, ..., 0.2785, 0.4540, 0.5116]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.8150, 0.5277, 0.3367, ..., 0.0434, 0.1834, 0.0206])
tensor([0.0667, 0.4882, 0.3630, ..., 0.8923, 0.8020, 0.4280])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,16 +16,16 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 26.625333547592163 seconds
Time: 26.830852508544922 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, 251, 521, ..., 1249514,
1249753, 1250000]),
col_indices=tensor([ 14, 21, 29, ..., 4968, 4983, 4999]),
values=tensor([0.5630, 0.8243, 0.2167, ..., 0.8539, 0.0380, 0.9608]),
tensor(crow_indices=tensor([ 0, 229, 490, ..., 1249494,
1249740, 1250000]),
col_indices=tensor([ 18, 28, 58, ..., 4928, 4934, 4938]),
values=tensor([0.5574, 0.5312, 0.0435, ..., 0.2785, 0.4540, 0.5116]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.8150, 0.5277, 0.3367, ..., 0.0434, 0.1834, 0.0206])
tensor([0.0667, 0.4882, 0.3630, ..., 0.8923, 0.8020, 0.4280])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -33,13 +33,13 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 26.625333547592163 seconds
Time: 26.830852508544922 seconds
[16.8, 16.84, 16.76, 16.6, 16.6, 16.68, 17.2, 17.2, 17.04, 17.0]
[16.84, 16.4, 16.52, 21.4, 23.24, 25.84, 26.88, 26.88, 24.16, 22.8, 20.48, 20.64, 20.68, 20.72, 20.68, 20.44, 20.28, 20.32, 20.28, 20.28, 20.12, 20.12, 20.08, 20.12, 19.96, 20.12, 20.04, 19.92, 19.96, 20.2]
30.391283988952637
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.625333547592163, 'TIME_S_1KI': 26.625333547592163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 615.7839365768435, 'W': 20.26185984115326}
[16.8, 16.84, 16.76, 16.6, 16.6, 16.68, 17.2, 17.2, 17.04, 17.0, 16.4, 16.32, 16.36, 16.36, 16.56, 16.52, 16.56, 16.68, 16.44, 16.36]
300.0
15.0
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.625333547592163, 'TIME_S_1KI': 26.625333547592163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 615.7839365768435, 'W': 20.26185984115326, 'J_1KI': 615.7839365768435, 'W_1KI': 20.26185984115326, 'W_D': 5.26185984115326, 'J_D': 159.91467674255395, 'W_D_1KI': 5.26185984115326, 'J_D_1KI': 5.26185984115326}
[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88]
[20.64, 20.72, 20.92, 24.36, 26.68, 28.84, 29.6, 27.08, 27.08, 26.84, 23.8, 24.0, 24.32, 24.44, 24.48, 24.52, 24.36, 24.12, 24.24, 24.2, 24.2, 24.28, 24.28, 24.28, 24.2, 24.28, 24.16, 24.12, 24.08, 24.08]
30.395055532455444
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394}
[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88, 20.44, 20.56, 20.56, 20.6, 20.6, 20.48, 20.32, 20.2, 20.2, 20.2]
370.9
18.544999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394, 'J_1KI': 724.3529372215271, 'W_1KI': 23.831275335163394, 'W_D': 5.2862753351633955, 'J_D': 160.67663237214092, 'W_D_1KI': 5.2862753351633955, 'J_D_1KI': 5.2862753351633955}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 199, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.763426303863525, "TIME_S_1KI": 54.08756936614837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 349.11062700271606, "W": 22.846498625739535, "J_1KI": 1754.3247588076183, "W_1KI": 114.80652575748509, "W_D": 4.138498625739533, "J_D": 63.23918048667905, "W_D_1KI": 20.79647550622881, "J_D_1KI": 104.50490204135079}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 52.60684037208557, "TIME_S_1KI": 52.60684037208557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.747187700272, "W": 24.19749919495284, "J_1KI": 1396.747187700272, "W_1KI": 24.19749919495284, "W_D": 5.574499194952839, "J_D": 321.77565171742464, "W_D_1KI": 5.574499194952839, "J_D_1KI": 5.574499194952839}

62
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
View File

@ -1,14 +1,14 @@
['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 100 -ss 5000 -sd 0.1 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.264772415161133}
['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 0.1 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 52.60684037208557}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 501, 994, ..., 2498989,
tensor(crow_indices=tensor([ 0, 495, 1009, ..., 2499036,
2499501, 2500000]),
col_indices=tensor([ 0, 2, 18, ..., 4969, 4994, 4999]),
values=tensor([0.5742, 0.5865, 0.7760, ..., 0.1661, 0.0806, 0.2598]),
col_indices=tensor([ 3, 5, 7, ..., 4975, 4989, 4996]),
values=tensor([0.3153, 0.9204, 0.9402, ..., 0.8644, 0.1243, 0.2567]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.0961, 0.5757, 0.9033, ..., 0.7471, 0.5676, 0.5058])
tensor([0.0882, 0.6551, 0.2953, ..., 0.5102, 0.2382, 0.8339])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,16 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 5.264772415161133 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 synthetic csr 199 -ss 5000 -sd 0.1 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.763426303863525}
Time: 52.60684037208557 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, 497, 1003, ..., 2498980,
2499516, 2500000]),
col_indices=tensor([ 3, 16, 28, ..., 4972, 4988, 4998]),
values=tensor([0.4080, 0.5007, 0.4190, ..., 0.3549, 0.8542, 0.4591]),
tensor(crow_indices=tensor([ 0, 495, 1009, ..., 2499036,
2499501, 2500000]),
col_indices=tensor([ 3, 5, 7, ..., 4975, 4989, 4996]),
values=tensor([0.3153, 0.9204, 0.9402, ..., 0.8644, 0.1243, 0.2567]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.8009, 0.9770, 0.1809, ..., 0.4100, 0.2770, 0.6242])
tensor([0.0882, 0.6551, 0.2953, ..., 0.5102, 0.2382, 0.8339])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,30 +33,13 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.763426303863525 seconds
Time: 52.60684037208557 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, 497, 1003, ..., 2498980,
2499516, 2500000]),
col_indices=tensor([ 3, 16, 28, ..., 4972, 4988, 4998]),
values=tensor([0.4080, 0.5007, 0.4190, ..., 0.3549, 0.8542, 0.4591]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.8009, 0.9770, 0.1809, ..., 0.4100, 0.2770, 0.6242])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.763426303863525 seconds
[20.76, 20.6, 20.8, 20.68, 20.68, 20.84, 20.92, 20.84, 20.72, 20.72]
[20.88, 20.92, 22.2, 23.32, 23.32, 25.32, 26.76, 27.36, 26.4, 26.08, 24.52, 24.48, 24.6, 24.56, 24.12]
15.28070592880249
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.763426303863525, 'TIME_S_1KI': 54.08756936614837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 349.11062700271606, 'W': 22.846498625739535}
[20.76, 20.6, 20.8, 20.68, 20.68, 20.84, 20.92, 20.84, 20.72, 20.72, 20.68, 20.48, 20.92, 20.92, 20.92, 20.88, 20.92, 20.88, 20.68, 20.8]
374.16
18.708000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.763426303863525, 'TIME_S_1KI': 54.08756936614837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 349.11062700271606, 'W': 22.846498625739535, 'J_1KI': 1754.3247588076183, 'W_1KI': 114.80652575748509, 'W_D': 4.138498625739533, 'J_D': 63.23918048667905, 'W_D_1KI': 20.79647550622881, 'J_D_1KI': 104.50490204135079}
[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64]
[21.04, 20.8, 23.84, 25.56, 28.32, 28.32, 30.0, 30.76, 27.04, 26.12, 23.96, 24.04, 23.96, 24.4, 24.48, 24.44, 24.4, 24.4, 24.4, 24.44, 24.52, 24.64, 24.44, 24.48, 24.36, 24.52, 24.36, 24.36, 24.28, 24.28, 24.2, 24.2, 24.04, 24.12, 24.04, 24.04, 24.08, 24.16, 24.12, 23.96, 24.0, 24.04, 24.04, 23.92, 23.92, 24.16, 24.32, 24.52, 24.68, 24.64, 24.24, 24.12, 24.2, 24.2, 24.2, 24.36, 24.36]
57.72279095649719
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284}
[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64, 20.48, 20.4, 20.44, 20.76, 20.76, 20.96, 21.12, 21.08, 21.0, 20.76]
372.46000000000004
18.623
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284, 'J_1KI': 1396.747187700272, 'W_1KI': 24.19749919495284, 'W_D': 5.574499194952839, 'J_D': 321.77565171742464, 'W_D_1KI': 5.574499194952839, 'J_D_1KI': 5.574499194952839}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.224013328552246, "TIME_S_1KI": 112.24013328552246, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 352.91180219650266, "W": 23.07981204778166, "J_1KI": 3529.118021965027, "W_1KI": 230.7981204778166, "W_D": 4.68881204778166, "J_D": 71.69629919505113, "W_D_1KI": 46.8881204778166, "J_D_1KI": 468.88120477816597}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323, "TIME_S_1KI": 105.2479407787323, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2877.626370677949, "W": 24.097306664358964, "J_1KI": 2877.626370677949, "W_1KI": 24.097306664358964, "W_D": 5.6223066643589625, "J_D": 671.3985984718811, "W_D_1KI": 5.6223066643589625, "J_D_1KI": 5.6223066643589625}

44
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
View File

@ -1,14 +1,14 @@
['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 100 -ss 5000 -sd 0.2 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.224013328552246}
['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 0.2 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1006, 2027, ..., 4998032,
4998984, 5000000]),
col_indices=tensor([ 1, 6, 9, ..., 4994, 4996, 4999]),
values=tensor([0.8034, 0.7589, 0.9109, ..., 0.7227, 0.1781, 0.2537]),
tensor(crow_indices=tensor([ 0, 987, 1961, ..., 4998062,
4998993, 5000000]),
col_indices=tensor([ 2, 8, 14, ..., 4993, 4994, 4998]),
values=tensor([0.2058, 0.7964, 0.8021, ..., 0.0689, 0.5253, 0.0161]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.0665, 0.6506, 0.6868, ..., 0.4823, 0.5910, 0.0127])
tensor([0.1259, 0.8957, 0.2222, ..., 0.6970, 0.5570, 0.6933])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,16 +16,16 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 11.224013328552246 seconds
Time: 105.2479407787323 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, 1006, 2027, ..., 4998032,
4998984, 5000000]),
col_indices=tensor([ 1, 6, 9, ..., 4994, 4996, 4999]),
values=tensor([0.8034, 0.7589, 0.9109, ..., 0.7227, 0.1781, 0.2537]),
tensor(crow_indices=tensor([ 0, 987, 1961, ..., 4998062,
4998993, 5000000]),
col_indices=tensor([ 2, 8, 14, ..., 4993, 4994, 4998]),
values=tensor([0.2058, 0.7964, 0.8021, ..., 0.0689, 0.5253, 0.0161]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.0665, 0.6506, 0.6868, ..., 0.4823, 0.5910, 0.0127])
tensor([0.1259, 0.8957, 0.2222, ..., 0.6970, 0.5570, 0.6933])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -33,13 +33,13 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 11.224013328552246 seconds
Time: 105.2479407787323 seconds
[20.76, 20.72, 20.88, 20.84, 20.8, 20.64, 20.6, 20.44, 20.64, 20.6]
[20.56, 20.4, 21.2, 21.2, 22.36, 25.64, 28.2, 28.88, 28.52, 27.12, 25.64, 24.52, 24.64, 24.76, 24.72]
15.29093050956726
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.224013328552246, 'TIME_S_1KI': 112.24013328552246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 352.91180219650266, 'W': 23.07981204778166}
[20.76, 20.72, 20.88, 20.84, 20.8, 20.64, 20.6, 20.44, 20.64, 20.6, 20.16, 20.04, 20.04, 20.24, 20.04, 20.16, 20.12, 20.2, 20.4, 20.52]
367.82000000000005
18.391000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.224013328552246, 'TIME_S_1KI': 112.24013328552246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 352.91180219650266, 'W': 23.07981204778166, 'J_1KI': 3529.118021965027, 'W_1KI': 230.7981204778166, 'W_D': 4.68881204778166, 'J_D': 71.69629919505113, 'W_D_1KI': 46.8881204778166, 'J_D_1KI': 468.88120477816597}
[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76]
[20.8, 20.6, 20.6, 24.4, 25.4, 28.96, 30.96, 31.56, 28.6, 27.8, 25.4, 24.24, 24.24, 24.24, 24.24, 24.24, 24.16, 24.04, 24.12, 24.2, 24.2, 24.28, 24.36, 24.2, 24.32, 24.4, 24.56, 24.56, 24.56, 24.44, 24.44, 24.24, 24.2, 24.16, 24.04, 24.32, 24.24, 24.32, 24.4, 24.4, 24.36, 24.4, 24.6, 24.8, 24.72, 24.88, 24.88, 24.64, 24.48, 24.48, 24.2, 24.12, 24.12, 24.28, 24.48, 24.56, 24.56, 24.6, 24.28, 24.16, 24.16, 24.04, 24.08, 24.24, 24.24, 24.64, 24.72, 24.6, 24.48, 24.12, 24.16, 24.08, 24.16, 24.2, 23.84, 23.92, 23.92, 23.92, 23.76, 24.2, 24.16, 24.28, 24.64, 24.44, 24.36, 24.52, 24.36, 24.4, 24.48, 24.48, 24.56, 24.56, 24.56, 24.36, 24.36, 24.2, 24.32, 24.08, 24.16, 24.24, 24.4, 24.4, 24.44, 24.68, 24.56, 24.4, 24.28, 24.4, 24.32, 24.4, 24.6, 24.48, 24.44, 24.6, 24.6, 24.48, 24.28, 24.24]
119.4169294834137
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964}
[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76, 20.2, 20.32, 20.2, 20.12, 20.32, 20.24, 20.6, 20.72, 20.8, 20.8]
369.5
18.475
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964, 'J_1KI': 2877.626370677949, 'W_1KI': 24.097306664358964, 'W_D': 5.6223066643589625, 'J_D': 671.3985984718811, 'W_D_1KI': 5.6223066643589625, 'J_D_1KI': 5.6223066643589625}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "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": 16.860999822616577, "TIME_S_1KI": 168.60999822616577, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.9433403301238, "W": 23.72394119074236, "J_1KI": 5069.433403301237, "W_1KI": 237.2394119074236, "W_D": 5.171941190742359, "J_D": 110.51625537872305, "W_D_1KI": 51.71941190742359, "J_D_1KI": 517.1941190742359}
{"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": 171.51510739326477, "TIME_S_1KI": 171.51510739326477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4017.952876434326, "W": 24.359705854077962, "J_1KI": 4017.9528764343263, "W_1KI": 24.359705854077962, "W_D": 5.79370585407796, "J_D": 955.6288257770532, "W_D_1KI": 5.79370585407796, "J_D_1KI": 5.79370585407796}

44
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
View File

@ -1,14 +1,14 @@
['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 100 -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": 16.860999822616577}
['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": 171.51510739326477}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1507, 3002, ..., 7496981,
7498462, 7500000]),
col_indices=tensor([ 0, 3, 8, ..., 4995, 4997, 4998]),
values=tensor([0.0922, 0.0923, 0.7842, ..., 0.1175, 0.1649, 0.6291]),
tensor(crow_indices=tensor([ 0, 1461, 2933, ..., 7497082,
7498527, 7500000]),
col_indices=tensor([ 0, 1, 3, ..., 4992, 4997, 4999]),
values=tensor([0.2348, 0.4295, 0.7390, ..., 0.0266, 0.0621, 0.6356]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.7654, 0.0824, 0.1261, ..., 0.9395, 0.8981, 0.0893])
tensor([0.3584, 0.9157, 0.1902, ..., 0.2272, 0.0135, 0.3908])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,16 +16,16 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 16.860999822616577 seconds
Time: 171.51510739326477 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, 1507, 3002, ..., 7496981,
7498462, 7500000]),
col_indices=tensor([ 0, 3, 8, ..., 4995, 4997, 4998]),
values=tensor([0.0922, 0.0923, 0.7842, ..., 0.1175, 0.1649, 0.6291]),
tensor(crow_indices=tensor([ 0, 1461, 2933, ..., 7497082,
7498527, 7500000]),
col_indices=tensor([ 0, 1, 3, ..., 4992, 4997, 4999]),
values=tensor([0.2348, 0.4295, 0.7390, ..., 0.0266, 0.0621, 0.6356]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.7654, 0.0824, 0.1261, ..., 0.9395, 0.8981, 0.0893])
tensor([0.3584, 0.9157, 0.1902, ..., 0.2272, 0.0135, 0.3908])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -33,13 +33,13 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 16.860999822616577 seconds
Time: 171.51510739326477 seconds
[20.72, 20.68, 20.76, 20.76, 20.72, 20.56, 20.6, 20.4, 20.4, 20.28]
[20.28, 20.12, 20.36, 22.84, 23.44, 27.24, 29.08, 29.68, 28.6, 28.0, 25.76, 24.4, 24.4, 24.44, 24.48, 24.56, 24.52, 24.52, 24.48, 24.52, 24.6]
21.368428468704224
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, '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': 16.860999822616577, 'TIME_S_1KI': 168.60999822616577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.9433403301238, 'W': 23.72394119074236}
[20.72, 20.68, 20.76, 20.76, 20.72, 20.56, 20.6, 20.4, 20.4, 20.28, 20.48, 20.36, 20.4, 20.28, 20.48, 20.48, 20.84, 20.96, 21.12, 21.0]
371.04
18.552
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, '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': 16.860999822616577, 'TIME_S_1KI': 168.60999822616577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.9433403301238, 'W': 23.72394119074236, 'J_1KI': 5069.433403301237, 'W_1KI': 237.2394119074236, 'W_D': 5.171941190742359, 'J_D': 110.51625537872305, 'W_D_1KI': 51.71941190742359, 'J_D_1KI': 517.1941190742359}
[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56]
[20.6, 20.48, 20.88, 21.96, 23.44, 26.4, 27.84, 28.8, 28.48, 27.04, 25.6, 24.28, 24.28, 24.48, 24.64, 24.64, 24.6, 24.6, 24.4, 24.24, 24.36, 24.48, 24.44, 24.52, 24.6, 24.6, 24.96, 25.04, 25.04, 25.12, 24.68, 24.56, 24.64, 24.6, 24.68, 24.92, 24.84, 24.84, 24.56, 24.48, 24.8, 24.56, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.6, 24.6, 24.68, 24.68, 24.56, 24.48, 24.6, 24.48, 24.48, 24.56, 24.64, 24.52, 24.48, 24.6, 24.6, 24.52, 24.24, 24.32, 24.44, 24.32, 24.44, 24.44, 24.64, 24.84, 24.6, 24.76, 24.76, 24.8, 24.92, 25.08, 25.04, 24.92, 24.52, 24.6, 24.56, 24.6, 24.48, 24.6, 24.56, 24.56, 24.4, 24.36, 24.48, 24.48, 24.64, 24.64, 24.52, 24.56, 24.52, 24.44, 24.52, 24.52, 24.52, 24.48, 24.32, 24.52, 24.84, 25.04, 25.04, 25.0, 24.92, 24.68, 24.36, 24.36, 24.36, 24.24, 24.36, 24.36, 24.8, 24.72, 24.84, 24.68, 24.44, 24.56, 24.64, 24.72, 24.72, 25.16, 25.56, 25.68, 25.56, 25.36, 24.88, 24.8, 24.72, 24.52, 24.48, 24.6, 24.6, 24.6, 24.44, 24.44, 24.36, 24.44, 24.56, 24.56, 24.76, 24.64, 24.48, 24.72, 24.72, 24.72, 24.56, 24.92, 24.8, 24.56, 24.72, 24.8, 24.88, 24.88, 24.96, 24.84, 24.6, 24.6, 24.16]
164.9425859451294
{'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': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962}
[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56, 20.0, 20.24, 20.64, 20.6, 20.8, 20.76, 20.44, 20.48, 20.4, 20.36]
371.32000000000005
18.566000000000003
{'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': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962, 'J_1KI': 4017.9528764343263, 'W_1KI': 24.359705854077962, 'W_D': 5.79370585407796, 'J_D': 955.6288257770532, 'W_D_1KI': 5.79370585407796, 'J_D_1KI': 5.79370585407796}

1
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 20.996277332305908, "TIME_S_1KI": 209.96277332305908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 668.5575802040102, "W": 24.39593264447633, "J_1KI": 6685.575802040102, "W_1KI": 243.9593264447633, "W_D": 5.836932644476324, "J_D": 159.9580397877693, "W_D_1KI": 58.36932644476324, "J_D_1KI": 583.6932644476324}

45
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
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@ -1,45 +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 synthetic csr 100 -ss 5000 -sd 0.4 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 20.996277332305908}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2028, 3979, ..., 9995991,
9997998, 10000000]),
col_indices=tensor([ 0, 3, 5, ..., 4997, 4998, 4999]),
values=tensor([0.5630, 0.7878, 0.5063, ..., 0.7425, 0.7666, 0.8643]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1259, 0.6554, 0.1049, ..., 0.7276, 0.1818, 0.8981])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 20.996277332305908 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, 2028, 3979, ..., 9995991,
9997998, 10000000]),
col_indices=tensor([ 0, 3, 5, ..., 4997, 4998, 4999]),
values=tensor([0.5630, 0.7878, 0.5063, ..., 0.7425, 0.7666, 0.8643]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1259, 0.6554, 0.1049, ..., 0.7276, 0.1818, 0.8981])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 20.996277332305908 seconds
[20.64, 20.64, 20.68, 20.84, 20.64, 20.56, 20.36, 20.48, 20.44, 20.6]
[20.92, 21.16, 21.52, 23.96, 23.96, 25.64, 28.24, 30.08, 29.68, 29.36, 27.16, 26.72, 25.12, 25.24, 25.48, 25.4, 25.28, 25.28, 25.08, 24.72, 24.36, 24.44, 24.32, 24.44, 24.68, 24.6, 24.52]
27.40446901321411
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 20.996277332305908, 'TIME_S_1KI': 209.96277332305908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.5575802040102, 'W': 24.39593264447633}
[20.64, 20.64, 20.68, 20.84, 20.64, 20.56, 20.36, 20.48, 20.44, 20.6, 20.44, 20.32, 20.6, 20.76, 20.76, 20.76, 20.96, 20.76, 20.56, 20.44]
371.18000000000006
18.559000000000005
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 20.996277332305908, 'TIME_S_1KI': 209.96277332305908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.5575802040102, 'W': 24.39593264447633, 'J_1KI': 6685.575802040102, 'W_1KI': 243.9593264447633, 'W_D': 5.836932644476324, 'J_D': 159.9580397877693, 'W_D_1KI': 58.36932644476324, 'J_D_1KI': 583.6932644476324}

1
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
View File

@ -1 +0,0 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.35210919380188, "TIME_S_1KI": 263.5210919380188, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 811.2840313434602, "W": 24.192575243793513, "J_1KI": 8112.840313434603, "W_1KI": 241.92575243793513, "W_D": 5.690575243793514, "J_D": 190.8301525540354, "W_D_1KI": 56.90575243793514, "J_D_1KI": 569.0575243793514}

45
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
View File

@ -1,45 +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 synthetic csr 100 -ss 5000 -sd 0.5 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.35210919380188}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2515, 4982, ..., 12494949,
12497477, 12500000]),
col_indices=tensor([ 0, 2, 3, ..., 4997, 4998, 4999]),
values=tensor([0.8539, 0.6304, 0.9472, ..., 0.9422, 0.2299, 0.9872]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.9794, 0.7866, 0.7649, ..., 0.0670, 0.2063, 0.9592])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 26.35210919380188 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, 2515, 4982, ..., 12494949,
12497477, 12500000]),
col_indices=tensor([ 0, 2, 3, ..., 4997, 4998, 4999]),
values=tensor([0.8539, 0.6304, 0.9472, ..., 0.9422, 0.2299, 0.9872]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.9794, 0.7866, 0.7649, ..., 0.0670, 0.2063, 0.9592])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 26.35210919380188 seconds
[20.32, 20.32, 20.56, 20.52, 20.8, 20.8, 20.84, 20.84, 20.56, 20.4]
[20.56, 20.68, 20.84, 22.32, 23.2, 25.76, 28.56, 30.4, 30.84, 30.24, 27.28, 27.28, 26.24, 24.76, 24.6, 24.68, 24.6, 24.4, 24.44, 24.36, 24.16, 24.28, 24.2, 24.2, 24.24, 24.24, 24.16, 24.04, 24.0, 24.0, 24.24, 24.4, 24.52]
33.534422159194946
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.35210919380188, 'TIME_S_1KI': 263.5210919380188, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 811.2840313434602, 'W': 24.192575243793513}
[20.32, 20.32, 20.56, 20.52, 20.8, 20.8, 20.84, 20.84, 20.56, 20.4, 20.56, 20.68, 20.6, 20.6, 20.6, 20.52, 20.24, 20.28, 20.48, 20.32]
370.03999999999996
18.502
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.35210919380188, 'TIME_S_1KI': 263.5210919380188, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 811.2840313434602, 'W': 24.192575243793513, 'J_1KI': 8112.840313434603, 'W_1KI': 241.92575243793513, 'W_D': 5.690575243793514, 'J_D': 190.8301525540354, 'W_D_1KI': 56.90575243793514, 'J_D_1KI': 569.0575243793514}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27505, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.25606393814087, "TIME_S_1KI": 0.37287998320817556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2107.3890700149536, "W": 153.89000000000001, "J_1KI": 76.6183992006891, "W_1KI": 5.594982730412653, "W_D": 118.26825000000002, "J_D": 1619.580332573891, "W_D_1KI": 4.299881839665516, "J_D_1KI": 0.156330915821324}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27894, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.822905540466309, "TIME_S_1KI": 0.3880012024258374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.6757108688353, "W": 151.95, "J_1KI": 75.34508176915593, "W_1KI": 5.447408044740804, "W_D": 116.03349999999999, "J_D": 1604.90153732872, "W_D_1KI": 4.159801390980138, "J_D_1KI": 0.14912889477952743}

74
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
View File

@ -1,14 +1,14 @@
['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', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.4628758430480957}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.4579291343688965}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 508, 974, ..., 4999019,
4999492, 5000000]),
col_indices=tensor([ 9, 34, 50, ..., 9951, 9957, 9978]),
values=tensor([0.7868, 0.5776, 0.2287, ..., 0.8734, 0.0439, 0.0393]),
tensor(crow_indices=tensor([ 0, 485, 1001, ..., 4998993,
4999541, 5000000]),
col_indices=tensor([ 5, 20, 61, ..., 9897, 9942, 9998]),
values=tensor([0.7241, 0.0945, 0.6836, ..., 0.9220, 0.2796, 0.2745]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.6523, 0.3584, 0.2115, ..., 0.2592, 0.0051, 0.7390])
tensor([0.6528, 0.9454, 0.7224, ..., 0.5670, 0.2826, 0.8750])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 0.4628758430480957 seconds
Time: 0.4579291343688965 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22684', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.659529685974121}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22929', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.630967855453491}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 519, 1023, ..., 4999047,
4999545, 5000000]),
col_indices=tensor([ 4, 44, 83, ..., 9892, 9941, 9972]),
values=tensor([0.8741, 0.5769, 0.9569, ..., 0.2090, 0.9404, 0.5070]),
tensor(crow_indices=tensor([ 0, 483, 987, ..., 4998961,
4999465, 5000000]),
col_indices=tensor([ 47, 67, 96, ..., 9993, 9994, 9997]),
values=tensor([0.9705, 0.3882, 0.2458, ..., 0.5796, 0.8899, 0.6056]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.1912, 0.0895, 0.2612, ..., 0.6252, 0.2980, 0.9838])
tensor([0.2409, 0.7584, 0.7571, ..., 0.5444, 0.5564, 0.6333])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,19 +36,19 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 8.659529685974121 seconds
Time: 8.630967855453491 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27505', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.25606393814087}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27894', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.822905540466309}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 494, 944, ..., 4998986,
4999507, 5000000]),
col_indices=tensor([ 48, 74, 75, ..., 9915, 9966, 9976]),
values=tensor([0.3182, 0.9601, 0.3370, ..., 0.9931, 0.0889, 0.2292]),
tensor(crow_indices=tensor([ 0, 505, 994, ..., 4999000,
4999548, 5000000]),
col_indices=tensor([ 26, 89, 94, ..., 9963, 9967, 9969]),
values=tensor([0.5738, 0.3729, 0.9664, ..., 0.7914, 0.9130, 0.5932]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.0431, 0.2285, 0.4438, ..., 0.2766, 0.7465, 0.1407])
tensor([0.2560, 0.0745, 0.3692, ..., 0.2024, 0.4618, 0.6540])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -56,16 +56,16 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.25606393814087 seconds
Time: 10.822905540466309 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, 494, 944, ..., 4998986,
4999507, 5000000]),
col_indices=tensor([ 48, 74, 75, ..., 9915, 9966, 9976]),
values=tensor([0.3182, 0.9601, 0.3370, ..., 0.9931, 0.0889, 0.2292]),
tensor(crow_indices=tensor([ 0, 505, 994, ..., 4999000,
4999548, 5000000]),
col_indices=tensor([ 26, 89, 94, ..., 9963, 9967, 9969]),
values=tensor([0.5738, 0.3729, 0.9664, ..., 0.7914, 0.9130, 0.5932]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.0431, 0.2285, 0.4438, ..., 0.2766, 0.7465, 0.1407])
tensor([0.2560, 0.0745, 0.3692, ..., 0.2024, 0.4618, 0.6540])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -73,13 +73,13 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.25606393814087 seconds
Time: 10.822905540466309 seconds
[40.36, 40.12, 40.4, 39.54, 39.59, 39.1, 39.19, 40.07, 39.54, 39.22]
[153.89]
13.69412612915039
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.25606393814087, 'TIME_S_1KI': 0.37287998320817556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2107.3890700149536, 'W': 153.89000000000001}
[40.36, 40.12, 40.4, 39.54, 39.59, 39.1, 39.19, 40.07, 39.54, 39.22, 40.73, 39.18, 39.33, 39.63, 39.65, 39.27, 39.19, 39.18, 39.78, 39.04]
712.435
35.62175
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.25606393814087, 'TIME_S_1KI': 0.37287998320817556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2107.3890700149536, 'W': 153.89000000000001, 'J_1KI': 76.6183992006891, 'W_1KI': 5.594982730412653, 'W_D': 118.26825000000002, 'J_D': 1619.580332573891, 'W_D_1KI': 4.299881839665516, 'J_D_1KI': 0.156330915821324}
[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73]
[151.95]
13.831363677978516
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95}
[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73, 40.26, 39.63, 39.55, 39.93, 41.22, 40.33, 39.48, 39.56, 39.48, 39.49]
718.3299999999999
35.9165
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95, 'J_1KI': 75.34508176915593, 'W_1KI': 5.447408044740804, 'W_D': 116.03349999999999, 'J_D': 1604.90153732872, 'W_D_1KI': 4.159801390980138, 'J_D_1KI': 0.14912889477952743}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.452549695968628, "TIME_S_1KI": 2.6664842132639412, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1764.6434702682495, "W": 122.04, "J_1KI": 410.85994651181596, "W_1KI": 28.41443538998836, "W_D": 80.21600000000001, "J_D": 1159.8872550888063, "W_D_1KI": 18.676600698486613, "J_D_1KI": 4.348451850637163}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4679, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.427535057067871, "TIME_S_1KI": 2.4423028546843066, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1873.982270450592, "W": 124.72, "J_1KI": 400.5091409383612, "W_1KI": 26.655268219705064, "W_D": 88.71, "J_D": 1332.9134638524054, "W_D_1KI": 18.959179311818765, "J_D_1KI": 4.051972496648593}

78
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
View File

@ -1,14 +1,14 @@
['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', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.4442625045776367}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.382111072540283}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1035, 2082, ..., 9998102,
9999051, 10000000]),
col_indices=tensor([ 19, 30, 32, ..., 9959, 9982, 9985]),
values=tensor([0.5712, 0.3257, 0.7476, ..., 0.5702, 0.2998, 0.6598]),
tensor(crow_indices=tensor([ 0, 1024, 2032, ..., 9997994,
9998974, 10000000]),
col_indices=tensor([ 25, 59, 80, ..., 9969, 9975, 9986]),
values=tensor([0.6759, 0.5147, 0.7066, ..., 0.5276, 0.4088, 0.2550]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.5243, 0.8505, 0.9216, ..., 0.4616, 0.9699, 0.2962])
tensor([0.6863, 0.6243, 0.0191, ..., 0.9166, 0.1487, 0.8503])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 2.4442625045776367 seconds
Time: 2.382111072540283 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4295', '-ss', '10000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.452549695968628}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4407', '-ss', '10000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.887728929519653}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1025, 1966, ..., 9998059,
9999093, 10000000]),
col_indices=tensor([ 1, 7, 10, ..., 9966, 9969, 9981]),
values=tensor([0.1039, 0.2120, 0.0962, ..., 0.1651, 0.8455, 0.3269]),
tensor(crow_indices=tensor([ 0, 980, 2002, ..., 9998027,
9998983, 10000000]),
col_indices=tensor([ 0, 5, 25, ..., 9979, 9984, 9986]),
values=tensor([0.6732, 0.9055, 0.4649, ..., 0.1468, 0.7629, 0.6148]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.9433, 0.5217, 0.8143, ..., 0.2476, 0.9485, 0.9722])
tensor([0.4732, 0.0327, 0.4956, ..., 0.7189, 0.9869, 0.4026])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,19 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 11.452549695968628 seconds
Time: 9.887728929519653 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4679', '-ss', '10000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.427535057067871}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1025, 1966, ..., 9998059,
9999093, 10000000]),
col_indices=tensor([ 1, 7, 10, ..., 9966, 9969, 9981]),
values=tensor([0.1039, 0.2120, 0.0962, ..., 0.1651, 0.8455, 0.3269]),
tensor(crow_indices=tensor([ 0, 1053, 2097, ..., 9998095,
9999109, 10000000]),
col_indices=tensor([ 10, 15, 24, ..., 9977, 9991, 9995]),
values=tensor([0.4155, 0.1468, 0.0149, ..., 0.0226, 0.9383, 0.4708]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.9433, 0.5217, 0.8143, ..., 0.2476, 0.9485, 0.9722])
tensor([0.3975, 0.8045, 0.4645, ..., 0.1781, 0.4097, 0.1046])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +56,30 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 11.452549695968628 seconds
Time: 11.427535057067871 seconds
[40.21, 39.72, 39.55, 39.95, 39.94, 40.02, 39.57, 39.94, 44.79, 39.53]
[122.04]
14.459549903869629
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.452549695968628, 'TIME_S_1KI': 2.6664842132639412, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1764.6434702682495, 'W': 122.04}
[40.21, 39.72, 39.55, 39.95, 39.94, 40.02, 39.57, 39.94, 44.79, 39.53, 40.97, 39.48, 39.49, 39.81, 60.91, 51.02, 67.71, 65.48, 67.26, 42.97]
836.48
41.824
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.452549695968628, 'TIME_S_1KI': 2.6664842132639412, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1764.6434702682495, 'W': 122.04, 'J_1KI': 410.85994651181596, 'W_1KI': 28.41443538998836, 'W_D': 80.21600000000001, 'J_D': 1159.8872550888063, 'W_D_1KI': 18.676600698486613, 'J_D_1KI': 4.348451850637163}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1053, 2097, ..., 9998095,
9999109, 10000000]),
col_indices=tensor([ 10, 15, 24, ..., 9977, 9991, 9995]),
values=tensor([0.4155, 0.1468, 0.0149, ..., 0.0226, 0.9383, 0.4708]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.3975, 0.8045, 0.4645, ..., 0.1781, 0.4097, 0.1046])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 11.427535057067871 seconds
[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01]
[124.72]
15.02551531791687
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72}
[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01, 40.22, 39.64, 40.11, 39.47, 39.58, 39.84, 39.97, 39.93, 39.51, 39.53]
720.2
36.010000000000005
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72, 'J_1KI': 400.5091409383612, 'W_1KI': 26.655268219705064, 'W_D': 88.71, 'J_D': 1332.9134638524054, 'W_D_1KI': 18.959179311818765, 'J_D_1KI': 4.051972496648593}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2191, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.797947645187378, "TIME_S_1KI": 4.928319326876941, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2022.1372631835936, "W": 118.88, "J_1KI": 922.9289197551773, "W_1KI": 54.25832952989502, "W_D": 82.67325, "J_D": 1406.2639594001769, "W_D_1KI": 37.733112733911454, "J_D_1KI": 17.22186797531331}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2251, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059, "TIME_S_1KI": 4.83686624236342, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2027.23151884079, "W": 120.43, "J_1KI": 900.591523252239, "W_1KI": 53.500666370501996, "W_D": 84.27900000000001, "J_D": 1418.6917311000825, "W_D_1KI": 37.44069302532208, "J_D_1KI": 16.632915604319006}

78
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
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@ -1,14 +1,14 @@
['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', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.791375398635864}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.959461212158203}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1984, 3988, ..., 19996067,
19998037, 20000000]),
col_indices=tensor([ 4, 5, 9, ..., 9988, 9990, 9993]),
values=tensor([0.6195, 0.8354, 0.3980, ..., 0.7932, 0.7837, 0.6598]),
tensor(crow_indices=tensor([ 0, 1995, 4040, ..., 19996024,
19998015, 20000000]),
col_indices=tensor([ 0, 11, 12, ..., 9985, 9992, 9995]),
values=tensor([0.1207, 0.1695, 0.9340, ..., 0.6555, 0.7804, 0.2569]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.5668, 0.7998, 0.9844, ..., 0.6200, 0.3521, 0.6910])
tensor([0.9596, 0.9534, 0.3471, ..., 0.1162, 0.8421, 0.0589])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 4.791375398635864 seconds
Time: 4.959461212158203 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2191', '-ss', '10000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.797947645187378}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2117', '-ss', '10000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.870691061019897}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2053, 4071, ..., 19995990,
19998011, 20000000]),
col_indices=tensor([ 2, 5, 7, ..., 9978, 9979, 9995]),
values=tensor([0.3718, 0.8252, 0.7364, ..., 0.7436, 0.4903, 0.8833]),
tensor(crow_indices=tensor([ 0, 2060, 4088, ..., 19995982,
19997996, 20000000]),
col_indices=tensor([ 3, 8, 18, ..., 9972, 9995, 9999]),
values=tensor([0.9088, 0.2769, 0.7723, ..., 0.9463, 0.8275, 0.8743]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1328, 0.8897, 0.5612, ..., 0.9517, 0.3970, 0.1461])
tensor([0.1663, 0.5238, 0.4734, ..., 0.4751, 0.9551, 0.4862])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,19 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 10.797947645187378 seconds
Time: 9.870691061019897 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2251', '-ss', '10000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2053, 4071, ..., 19995990,
19998011, 20000000]),
col_indices=tensor([ 2, 5, 7, ..., 9978, 9979, 9995]),
values=tensor([0.3718, 0.8252, 0.7364, ..., 0.7436, 0.4903, 0.8833]),
tensor(crow_indices=tensor([ 0, 1987, 4056, ..., 19996028,
19998022, 20000000]),
col_indices=tensor([ 5, 19, 20, ..., 9991, 9993, 9995]),
values=tensor([0.9673, 0.5215, 0.1097, ..., 0.0767, 0.9506, 0.8361]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1328, 0.8897, 0.5612, ..., 0.9517, 0.3970, 0.1461])
tensor([0.1308, 0.6150, 0.9184, ..., 0.0574, 0.4962, 0.6674])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +56,30 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 10.797947645187378 seconds
Time: 10.887785911560059 seconds
[40.47, 41.35, 39.66, 40.26, 40.03, 39.5, 40.17, 39.67, 39.73, 39.92]
[118.88]
17.009902954101562
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2191, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.797947645187378, 'TIME_S_1KI': 4.928319326876941, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.1372631835936, 'W': 118.88}
[40.47, 41.35, 39.66, 40.26, 40.03, 39.5, 40.17, 39.67, 39.73, 39.92, 40.22, 39.71, 39.72, 40.09, 39.98, 40.13, 39.63, 39.44, 45.03, 39.46]
724.135
36.20675
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2191, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.797947645187378, 'TIME_S_1KI': 4.928319326876941, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.1372631835936, 'W': 118.88, 'J_1KI': 922.9289197551773, 'W_1KI': 54.25832952989502, 'W_D': 82.67325, 'J_D': 1406.2639594001769, 'W_D_1KI': 37.733112733911454, 'J_D_1KI': 17.22186797531331}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1987, 4056, ..., 19996028,
19998022, 20000000]),
col_indices=tensor([ 5, 19, 20, ..., 9991, 9993, 9995]),
values=tensor([0.9673, 0.5215, 0.1097, ..., 0.0767, 0.9506, 0.8361]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1308, 0.6150, 0.9184, ..., 0.0574, 0.4962, 0.6674])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 10.887785911560059 seconds
[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0]
[120.43]
16.833276748657227
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43}
[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0, 40.84, 39.74, 40.34, 40.47, 40.2, 40.2, 39.72, 39.73, 39.66, 39.68]
723.02
36.150999999999996
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43, 'J_1KI': 900.591523252239, 'W_1KI': 53.500666370501996, 'W_D': 84.27900000000001, 'J_D': 1418.6917311000825, 'W_D_1KI': 37.44069302532208, 'J_D_1KI': 16.632915604319006}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1433, "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": 10.876516819000244, "TIME_S_1KI": 7.5900326720169184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2139.1934151315686, "W": 115.81999999999998, "J_1KI": 1492.8076867631323, "W_1KI": 80.82344731332866, "W_D": 79.70624999999998, "J_D": 1472.173071531951, "W_D_1KI": 55.62194696441031, "J_D_1KI": 38.81503626267293}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1475, "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": 10.550647735595703, "TIME_S_1KI": 7.152981515658104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2246.3171599555017, "W": 116.09, "J_1KI": 1522.9268881054247, "W_1KI": 78.7050847457627, "W_D": 80.02975, "J_D": 1548.5588830385805, "W_D_1KI": 54.25745762711865, "J_D_1KI": 36.78471703533467}

58
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
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@ -1,14 +1,14 @@
['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": 7.326756000518799}
{"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": 7.117977619171143}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3009, 6089, ..., 29994053,
29996999, 30000000]),
col_indices=tensor([ 1, 9, 11, ..., 9985, 9993, 9999]),
values=tensor([0.2577, 0.0805, 0.4624, ..., 0.3276, 0.0599, 0.1828]),
tensor(crow_indices=tensor([ 0, 2996, 5947, ..., 29993941,
29997016, 30000000]),
col_indices=tensor([ 2, 4, 5, ..., 9994, 9995, 9997]),
values=tensor([0.7643, 0.7440, 0.4862, ..., 0.6436, 0.3641, 0.9418]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.9046, 0.5378, 0.0670, ..., 0.4938, 0.1002, 0.8451])
tensor([0.8566, 0.8595, 0.2293, ..., 0.0057, 0.7338, 0.0583])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 7.326756000518799 seconds
Time: 7.117977619171143 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1433', '-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": 10.876516819000244}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1475', '-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": 10.550647735595703}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2984, 5966, ..., 29993973,
29997036, 30000000]),
col_indices=tensor([ 2, 6, 8, ..., 9989, 9994, 9995]),
values=tensor([0.0728, 0.9990, 0.4694, ..., 0.7329, 0.2175, 0.4979]),
tensor(crow_indices=tensor([ 0, 2975, 5983, ..., 29994011,
29997029, 30000000]),
col_indices=tensor([ 7, 9, 13, ..., 9994, 9995, 9998]),
values=tensor([0.9780, 0.9139, 0.8008, ..., 0.7340, 0.6785, 0.0713]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.9122, 0.6942, 0.6684, ..., 0.3538, 0.9426, 0.8104])
tensor([0.9158, 0.5873, 0.3730, ..., 0.5358, 0.1305, 0.5051])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,16 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 10.876516819000244 seconds
Time: 10.550647735595703 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, 2984, 5966, ..., 29993973,
29997036, 30000000]),
col_indices=tensor([ 2, 6, 8, ..., 9989, 9994, 9995]),
values=tensor([0.0728, 0.9990, 0.4694, ..., 0.7329, 0.2175, 0.4979]),
tensor(crow_indices=tensor([ 0, 2975, 5983, ..., 29994011,
29997029, 30000000]),
col_indices=tensor([ 7, 9, 13, ..., 9994, 9995, 9998]),
values=tensor([0.9780, 0.9139, 0.8008, ..., 0.7340, 0.6785, 0.0713]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.9122, 0.6942, 0.6684, ..., 0.3538, 0.9426, 0.8104])
tensor([0.9158, 0.5873, 0.3730, ..., 0.5358, 0.1305, 0.5051])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +53,13 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 10.876516819000244 seconds
Time: 10.550647735595703 seconds
[40.68, 39.67, 39.67, 39.75, 39.81, 39.61, 39.83, 39.6, 39.64, 40.06]
[115.82]
18.469982862472534
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1433, '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': 10.876516819000244, 'TIME_S_1KI': 7.5900326720169184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2139.1934151315686, 'W': 115.81999999999998}
[40.68, 39.67, 39.67, 39.75, 39.81, 39.61, 39.83, 39.6, 39.64, 40.06, 40.61, 39.6, 39.69, 39.61, 40.38, 44.77, 40.11, 39.94, 40.18, 39.48]
722.2750000000001
36.11375
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1433, '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': 10.876516819000244, 'TIME_S_1KI': 7.5900326720169184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2139.1934151315686, 'W': 115.81999999999998, 'J_1KI': 1492.8076867631323, 'W_1KI': 80.82344731332866, 'W_D': 79.70624999999998, 'J_D': 1472.173071531951, 'W_D_1KI': 55.62194696441031, 'J_D_1KI': 38.81503626267293}
[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31]
[116.09]
19.349790334701538
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, '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': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09}
[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31, 41.51, 39.76, 39.79, 39.7, 39.79, 40.38, 40.29, 39.67, 39.96, 39.84]
721.2049999999999
36.060249999999996
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, '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': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09, 'J_1KI': 1522.9268881054247, 'W_1KI': 78.7050847457627, 'W_D': 80.02975, 'J_D': 1548.5588830385805, 'W_D_1KI': 54.25745762711865, 'J_D_1KI': 36.78471703533467}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91710, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.455259799957275, "TIME_S_1KI": 0.11400348707836959, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1752.8075734996796, "W": 134.53, "J_1KI": 19.112502164427866, "W_1KI": 1.466906553265729, "W_D": 98.01950000000001, "J_D": 1277.107871483326, "W_D_1KI": 1.0687983862174244, "J_D_1KI": 0.011654109543315062}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92460, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.897745609283447, "TIME_S_1KI": 0.1178644344503942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1737.196626586914, "W": 132.16, "J_1KI": 18.788628883700127, "W_1KI": 1.4293748648064026, "W_D": 96.69825, "J_D": 1271.0644196190835, "W_D_1KI": 1.0458387410772227, "J_D_1KI": 0.011311256122401284}

74
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
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@ -1,14 +1,14 @@
['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', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.15620112419128418}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.15929031372070312}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 240, 508, ..., 1249498,
1249743, 1250000]),
col_indices=tensor([ 1, 2, 46, ..., 4888, 4964, 4980]),
values=tensor([0.7368, 0.9867, 0.0616, ..., 0.4088, 0.7518, 0.0307]),
tensor(crow_indices=tensor([ 0, 260, 503, ..., 1249486,
1249755, 1250000]),
col_indices=tensor([ 4, 17, 88, ..., 4971, 4985, 4987]),
values=tensor([0.6362, 0.8148, 0.9153, ..., 0.4566, 0.5649, 0.0413]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.2511, 0.5490, 0.8698, ..., 0.0135, 0.9603, 0.4779])
tensor([0.3069, 0.3781, 0.2833, ..., 0.0090, 0.7599, 0.7166])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 0.15620112419128418 seconds
Time: 0.15929031372070312 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67221', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 7.696179628372192}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65917', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 7.485628128051758}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 260, 523, ..., 1249493,
1249731, 1250000]),
col_indices=tensor([ 11, 36, 51, ..., 4933, 4983, 4999]),
values=tensor([0.1688, 0.6439, 0.5409, ..., 0.9889, 0.0264, 0.5294]),
tensor(crow_indices=tensor([ 0, 243, 508, ..., 1249492,
1249760, 1250000]),
col_indices=tensor([ 22, 30, 57, ..., 4895, 4917, 4934]),
values=tensor([0.3367, 0.7320, 0.6215, ..., 0.3721, 0.4144, 0.7665]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.2628, 0.4260, 0.4558, ..., 0.6039, 0.8509, 0.7408])
tensor([0.8371, 0.0930, 0.7011, ..., 0.7680, 0.1649, 0.0938])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,19 +36,19 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 7.696179628372192 seconds
Time: 7.485628128051758 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91710', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.455259799957275}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92460', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.897745609283447}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 271, 506, ..., 1249448,
1249721, 1250000]),
col_indices=tensor([ 29, 30, 76, ..., 4981, 4997, 4999]),
values=tensor([0.0426, 0.5256, 0.4347, ..., 0.1903, 0.6901, 0.8658]),
tensor(crow_indices=tensor([ 0, 258, 501, ..., 1249501,
1249766, 1250000]),
col_indices=tensor([ 3, 26, 36, ..., 4973, 4974, 4975]),
values=tensor([0.1574, 0.4230, 0.3584, ..., 0.2058, 0.0488, 0.1761]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.0426, 0.2115, 0.6413, ..., 0.2013, 0.2155, 0.0145])
tensor([0.1704, 0.2409, 0.1947, ..., 0.5321, 0.6051, 0.5827])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -56,16 +56,16 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 10.455259799957275 seconds
Time: 10.897745609283447 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, 271, 506, ..., 1249448,
1249721, 1250000]),
col_indices=tensor([ 29, 30, 76, ..., 4981, 4997, 4999]),
values=tensor([0.0426, 0.5256, 0.4347, ..., 0.1903, 0.6901, 0.8658]),
tensor(crow_indices=tensor([ 0, 258, 501, ..., 1249501,
1249766, 1250000]),
col_indices=tensor([ 3, 26, 36, ..., 4973, 4974, 4975]),
values=tensor([0.1574, 0.4230, 0.3584, ..., 0.2058, 0.0488, 0.1761]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.0426, 0.2115, 0.6413, ..., 0.2013, 0.2155, 0.0145])
tensor([0.1704, 0.2409, 0.1947, ..., 0.5321, 0.6051, 0.5827])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -73,13 +73,13 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 10.455259799957275 seconds
Time: 10.897745609283447 seconds
[40.22, 39.01, 39.13, 39.06, 38.89, 38.77, 38.89, 45.02, 39.49, 38.74]
[134.53]
13.029120445251465
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91710, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.455259799957275, 'TIME_S_1KI': 0.11400348707836959, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.8075734996796, 'W': 134.53}
[40.22, 39.01, 39.13, 39.06, 38.89, 38.77, 38.89, 45.02, 39.49, 38.74, 39.45, 38.91, 38.91, 38.77, 45.05, 51.64, 42.38, 38.87, 38.81, 38.81]
730.2099999999999
36.51049999999999
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91710, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.455259799957275, 'TIME_S_1KI': 0.11400348707836959, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.8075734996796, 'W': 134.53, 'J_1KI': 19.112502164427866, 'W_1KI': 1.466906553265729, 'W_D': 98.01950000000001, 'J_D': 1277.107871483326, 'W_D_1KI': 1.0687983862174244, 'J_D_1KI': 0.011654109543315062}
[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41]
[132.16]
13.144647598266602
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16}
[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41, 40.8, 39.06, 39.17, 39.04, 39.15, 39.09, 39.05, 39.4, 39.89, 39.43]
709.2349999999999
35.461749999999995
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16, 'J_1KI': 18.788628883700127, 'W_1KI': 1.4293748648064026, 'W_D': 96.69825, 'J_D': 1271.0644196190835, 'W_D_1KI': 1.0458387410772227, 'J_D_1KI': 0.011311256122401284}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53196, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.621366739273071, "TIME_S_1KI": 0.19966476312642062, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1859.8867098927496, "W": 135.45, "J_1KI": 34.962905291614966, "W_1KI": 2.5462440785021427, "W_D": 100.0975, "J_D": 1374.4555920523405, "W_D_1KI": 1.8816734340927888, "J_D_1KI": 0.03537246097625364}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53552, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.574474811553955, "TIME_S_1KI": 0.19746180929851276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1844.3806961250305, "W": 137.23, "J_1KI": 34.44093023836702, "W_1KI": 2.562556020316701, "W_D": 101.71924999999999, "J_D": 1367.113758830547, "W_D_1KI": 1.8994481998804897, "J_D_1KI": 0.03546922990514808}

74
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
View File

@ -1,14 +1,14 @@
['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', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3697686195373535}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.24988102912902832}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 482, 953, ..., 2499004,
2499522, 2500000]),
col_indices=tensor([ 8, 9, 10, ..., 4989, 4992, 4993]),
values=tensor([0.1808, 0.3744, 0.3617, ..., 0.4915, 0.9116, 0.1304]),
tensor(crow_indices=tensor([ 0, 499, 1048, ..., 2499022,
2499519, 2500000]),
col_indices=tensor([ 0, 10, 32, ..., 4963, 4977, 4991]),
values=tensor([0.6726, 0.1161, 0.9278, ..., 0.2840, 0.7697, 0.2554]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.8787, 0.8438, 0.3305, ..., 0.0934, 0.4772, 0.3202])
tensor([0.1233, 0.5107, 0.9675, ..., 0.9055, 0.5032, 0.4140])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 0.3697686195373535 seconds
Time: 0.24988102912902832 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28396', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.604789972305298}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42019', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.238577604293823}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 510, 995, ..., 2498986,
2499511, 2500000]),
col_indices=tensor([ 1, 2, 8, ..., 4984, 4986, 4997]),
values=tensor([0.0748, 0.9968, 0.9443, ..., 0.0526, 0.3043, 0.5465]),
tensor(crow_indices=tensor([ 0, 520, 1002, ..., 2498984,
2499504, 2500000]),
col_indices=tensor([ 4, 20, 21, ..., 4945, 4966, 4991]),
values=tensor([0.6805, 0.1607, 0.7488, ..., 0.5436, 0.2045, 0.6809]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.9671, 0.4248, 0.6295, ..., 0.7757, 0.3159, 0.1103])
tensor([0.8501, 0.5629, 0.1238, ..., 0.7287, 0.6927, 0.0708])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,19 +36,19 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 5.604789972305298 seconds
Time: 8.238577604293823 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53196', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.621366739273071}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53552', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.574474811553955}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 515, 1007, ..., 2498937,
2499481, 2500000]),
col_indices=tensor([ 6, 15, 19, ..., 4986, 4991, 4993]),
values=tensor([0.5542, 0.8952, 0.8012, ..., 0.8325, 0.7201, 0.7015]),
tensor(crow_indices=tensor([ 0, 495, 1006, ..., 2499053,
2499503, 2500000]),
col_indices=tensor([ 5, 23, 39, ..., 4985, 4986, 4988]),
values=tensor([0.9333, 0.4318, 0.5588, ..., 0.9224, 0.9203, 0.9677]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.4746, 0.7865, 0.3346, ..., 0.0574, 0.7499, 0.4825])
tensor([0.4521, 0.6662, 0.5969, ..., 0.4716, 0.4029, 0.2909])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -56,16 +56,16 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.621366739273071 seconds
Time: 10.574474811553955 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, 515, 1007, ..., 2498937,
2499481, 2500000]),
col_indices=tensor([ 6, 15, 19, ..., 4986, 4991, 4993]),
values=tensor([0.5542, 0.8952, 0.8012, ..., 0.8325, 0.7201, 0.7015]),
tensor(crow_indices=tensor([ 0, 495, 1006, ..., 2499053,
2499503, 2500000]),
col_indices=tensor([ 5, 23, 39, ..., 4985, 4986, 4988]),
values=tensor([0.9333, 0.4318, 0.5588, ..., 0.9224, 0.9203, 0.9677]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.4746, 0.7865, 0.3346, ..., 0.0574, 0.7499, 0.4825])
tensor([0.4521, 0.6662, 0.5969, ..., 0.4716, 0.4029, 0.2909])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -73,13 +73,13 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.621366739273071 seconds
Time: 10.574474811553955 seconds
[39.76, 40.98, 39.03, 38.9, 39.03, 39.28, 39.39, 38.84, 39.07, 38.79]
[135.45]
13.731168031692505
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.621366739273071, 'TIME_S_1KI': 0.19966476312642062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.8867098927496, 'W': 135.45}
[39.76, 40.98, 39.03, 38.9, 39.03, 39.28, 39.39, 38.84, 39.07, 38.79, 39.74, 39.0, 39.18, 39.17, 39.18, 38.97, 39.43, 39.52, 39.42, 39.03]
707.05
35.3525
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.621366739273071, 'TIME_S_1KI': 0.19966476312642062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.8867098927496, 'W': 135.45, 'J_1KI': 34.962905291614966, 'W_1KI': 2.5462440785021427, 'W_D': 100.0975, 'J_D': 1374.4555920523405, 'W_D_1KI': 1.8816734340927888, 'J_D_1KI': 0.03537246097625364}
[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55]
[137.23]
13.440069198608398
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23}
[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55, 39.94, 39.28, 39.24, 39.36, 39.26, 39.73, 39.25, 39.64, 39.4, 39.09]
710.2149999999999
35.510749999999994
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23, 'J_1KI': 34.44093023836702, 'W_1KI': 2.562556020316701, 'W_D': 101.71924999999999, 'J_D': 1367.113758830547, 'W_D_1KI': 1.8994481998804897, 'J_D_1KI': 0.03546922990514808}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
View File

@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28983, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.485858678817749, "TIME_S_1KI": 0.36179341955000344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1935.3792489004136, "W": 138.58, "J_1KI": 66.77636024222522, "W_1KI": 4.781423593140807, "W_D": 102.671, "J_D": 1433.8816774704458, "W_D_1KI": 3.542455922437291, "J_D_1KI": 0.12222530181269332}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946, "TIME_S_1KI": 0.3619989229285199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1928.9614020395277, "W": 139.14, "J_1KI": 67.09430963615748, "W_1KI": 4.839652173913042, "W_D": 103.18299999999998, "J_D": 1430.47307996726, "W_D_1KI": 3.588973913043478, "J_D_1KI": 0.12483387523629487}

74
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
View File

@ -1,14 +1,14 @@
['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', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43319129943847656}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43890881538391113}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 992, 2019, ..., 4998034,
4999022, 5000000]),
col_indices=tensor([ 6, 7, 25, ..., 4976, 4987, 4995]),
values=tensor([0.8659, 0.6827, 0.8740, ..., 0.4360, 0.6938, 0.9338]),
tensor(crow_indices=tensor([ 0, 1013, 1983, ..., 4997973,
4999012, 5000000]),
col_indices=tensor([ 4, 12, 14, ..., 4994, 4995, 4999]),
values=tensor([0.7248, 0.5151, 0.3464, ..., 0.0289, 0.6444, 0.6982]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.7963, 0.9033, 0.3372, ..., 0.2486, 0.4072, 0.8365])
tensor([0.8578, 0.9750, 0.0786, ..., 0.8483, 0.5183, 0.1076])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 0.43319129943847656 seconds
Time: 0.43890881538391113 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24238', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 8.780949115753174}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23922', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 8.73651671409607}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 970, 1988, ..., 4997978,
4998978, 5000000]),
col_indices=tensor([ 16, 17, 19, ..., 4996, 4997, 4999]),
values=tensor([0.1543, 0.7354, 0.5297, ..., 0.9670, 0.3753, 0.9860]),
tensor(crow_indices=tensor([ 0, 1001, 1993, ..., 4997975,
4998974, 5000000]),
col_indices=tensor([ 0, 6, 7, ..., 4986, 4993, 4997]),
values=tensor([0.9981, 0.9465, 0.8571, ..., 0.5801, 0.6800, 0.5830]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.8318, 0.0236, 0.3656, ..., 0.1748, 0.2631, 0.0655])
tensor([0.7841, 0.9192, 0.6592, ..., 0.6410, 0.6781, 0.3236])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,19 +36,19 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 8.780949115753174 seconds
Time: 8.73651671409607 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28983', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.485858678817749}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28750', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1046, 2086, ..., 4998016,
4999015, 5000000]),
col_indices=tensor([ 1, 2, 7, ..., 4963, 4988, 4989]),
values=tensor([0.8714, 0.5660, 0.3384, ..., 0.1998, 0.0405, 0.9940]),
tensor(crow_indices=tensor([ 0, 962, 2002, ..., 4997962,
4998964, 5000000]),
col_indices=tensor([ 3, 4, 7, ..., 4990, 4997, 4999]),
values=tensor([0.7874, 0.2567, 0.8616, ..., 0.3490, 0.8581, 0.1320]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.6062, 0.0119, 0.7376, ..., 0.3549, 0.0670, 0.7680])
tensor([0.5757, 0.3666, 0.5675, ..., 0.4574, 0.0199, 0.8861])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -56,16 +56,16 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 10.485858678817749 seconds
Time: 10.407469034194946 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, 1046, 2086, ..., 4998016,
4999015, 5000000]),
col_indices=tensor([ 1, 2, 7, ..., 4963, 4988, 4989]),
values=tensor([0.8714, 0.5660, 0.3384, ..., 0.1998, 0.0405, 0.9940]),
tensor(crow_indices=tensor([ 0, 962, 2002, ..., 4997962,
4998964, 5000000]),
col_indices=tensor([ 3, 4, 7, ..., 4990, 4997, 4999]),
values=tensor([0.7874, 0.2567, 0.8616, ..., 0.3490, 0.8581, 0.1320]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.6062, 0.0119, 0.7376, ..., 0.3549, 0.0670, 0.7680])
tensor([0.5757, 0.3666, 0.5675, ..., 0.4574, 0.0199, 0.8861])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -73,13 +73,13 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 10.485858678817749 seconds
Time: 10.407469034194946 seconds
[40.02, 39.97, 39.89, 40.7, 39.75, 39.29, 39.24, 40.56, 39.28, 39.34]
[138.58]
13.965790510177612
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28983, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.485858678817749, 'TIME_S_1KI': 0.36179341955000344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1935.3792489004136, 'W': 138.58}
[40.02, 39.97, 39.89, 40.7, 39.75, 39.29, 39.24, 40.56, 39.28, 39.34, 40.14, 39.41, 39.41, 40.87, 40.15, 39.38, 39.46, 39.42, 41.94, 39.42]
718.1800000000001
35.909000000000006
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28983, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.485858678817749, 'TIME_S_1KI': 0.36179341955000344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1935.3792489004136, 'W': 138.58, 'J_1KI': 66.77636024222522, 'W_1KI': 4.781423593140807, 'W_D': 102.671, 'J_D': 1433.8816774704458, 'W_D_1KI': 3.542455922437291, 'J_D_1KI': 0.12222530181269332}
[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54]
[139.14]
13.863456964492798
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14}
[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54, 42.22, 40.02, 39.42, 39.79, 40.33, 39.7, 39.95, 40.8, 39.86, 39.82]
719.1400000000001
35.95700000000001
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14, 'J_1KI': 67.09430963615748, 'W_1KI': 4.839652173913042, 'W_D': 103.18299999999998, 'J_D': 1430.47307996726, 'W_D_1KI': 3.588973913043478, 'J_D_1KI': 0.12483387523629487}

2
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19322, "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": 10.55522608757019, "TIME_S_1KI": 0.5462802032693401, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1980.0031997823714, "W": 138.03, "J_1KI": 102.47402959229746, "W_1KI": 7.143670427491978, "W_D": 102.176, "J_D": 1465.6872197418213, "W_D_1KI": 5.288065417658628, "J_D_1KI": 0.2736810587754181}
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18993, "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": 10.31110143661499, "TIME_S_1KI": 0.5428895612391402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1973.4541555023193, "W": 138.56, "J_1KI": 103.90428871175271, "W_1KI": 7.295319328173537, "W_D": 102.64975000000001, "J_D": 1461.9989585650565, "W_D_1KI": 5.404609593007951, "J_D_1KI": 0.2845579736222793}

74
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
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@ -1,14 +1,14 @@
['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": 0.6236028671264648}
{"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": 0.6186671257019043}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1508, 3089, ..., 7496976,
7498474, 7500000]),
col_indices=tensor([ 7, 9, 15, ..., 4993, 4994, 4999]),
values=tensor([0.5303, 0.4048, 0.9163, ..., 0.8482, 0.2205, 0.3341]),
tensor(crow_indices=tensor([ 0, 1447, 2956, ..., 7496936,
7498490, 7500000]),
col_indices=tensor([ 4, 5, 6, ..., 4990, 4991, 4998]),
values=tensor([0.3748, 0.3720, 0.6580, ..., 0.1069, 0.0058, 0.6452]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.9586, 0.6247, 0.6872, ..., 0.0060, 0.2602, 0.2097])
tensor([0.1517, 0.2007, 0.5208, ..., 0.9824, 0.7905, 0.4002])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 0.6236028671264648 seconds
Time: 0.6186671257019043 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16837', '-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": 9.149490594863892}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16971', '-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": 9.381728649139404}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 2963, ..., 7497013,
7498520, 7500000]),
col_indices=tensor([ 3, 5, 7, ..., 4995, 4998, 4999]),
values=tensor([0.0553, 0.4159, 0.6036, ..., 0.7243, 0.9131, 0.1926]),
tensor(crow_indices=tensor([ 0, 1500, 3040, ..., 7497021,
7498445, 7500000]),
col_indices=tensor([ 1, 5, 9, ..., 4989, 4995, 4997]),
values=tensor([0.5527, 0.4050, 0.9810, ..., 0.2200, 0.7595, 0.4370]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.5885, 0.8517, 0.1610, ..., 0.2789, 0.8066, 0.5429])
tensor([0.0260, 0.1554, 0.7193, ..., 0.7760, 0.7155, 0.7186])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,19 +36,19 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 9.149490594863892 seconds
Time: 9.381728649139404 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19322', '-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": 10.55522608757019}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18993', '-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": 10.31110143661499}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1484, 2984, ..., 7497085,
7498530, 7500000]),
col_indices=tensor([ 5, 8, 9, ..., 4987, 4993, 4996]),
values=tensor([0.5644, 0.8179, 0.2626, ..., 0.6489, 0.4956, 0.4262]),
tensor(crow_indices=tensor([ 0, 1505, 3034, ..., 7497030,
7498523, 7500000]),
col_indices=tensor([ 3, 4, 6, ..., 4987, 4992, 4993]),
values=tensor([0.4621, 0.5996, 0.9420, ..., 0.5197, 0.1170, 0.3387]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.0893, 0.0032, 0.8731, ..., 0.8525, 0.4366, 0.3134])
tensor([0.7133, 0.0520, 0.6752, ..., 0.8724, 0.9726, 0.0718])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -56,16 +56,16 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.55522608757019 seconds
Time: 10.31110143661499 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, 1484, 2984, ..., 7497085,
7498530, 7500000]),
col_indices=tensor([ 5, 8, 9, ..., 4987, 4993, 4996]),
values=tensor([0.5644, 0.8179, 0.2626, ..., 0.6489, 0.4956, 0.4262]),
tensor(crow_indices=tensor([ 0, 1505, 3034, ..., 7497030,
7498523, 7500000]),
col_indices=tensor([ 3, 4, 6, ..., 4987, 4992, 4993]),
values=tensor([0.4621, 0.5996, 0.9420, ..., 0.5197, 0.1170, 0.3387]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.0893, 0.0032, 0.8731, ..., 0.8525, 0.4366, 0.3134])
tensor([0.7133, 0.0520, 0.6752, ..., 0.8724, 0.9726, 0.0718])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -73,13 +73,13 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.55522608757019 seconds
Time: 10.31110143661499 seconds
[40.26, 39.93, 39.81, 39.63, 39.7, 39.44, 39.58, 40.08, 39.61, 39.95]
[138.03]
14.344730854034424
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19322, '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': 10.55522608757019, 'TIME_S_1KI': 0.5462802032693401, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.0031997823714, 'W': 138.03}
[40.26, 39.93, 39.81, 39.63, 39.7, 39.44, 39.58, 40.08, 39.61, 39.95, 40.68, 39.57, 40.69, 39.43, 39.89, 39.46, 39.71, 40.58, 39.58, 39.89]
717.0799999999999
35.854
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19322, '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': 10.55522608757019, 'TIME_S_1KI': 0.5462802032693401, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.0031997823714, 'W': 138.03, 'J_1KI': 102.47402959229746, 'W_1KI': 7.143670427491978, 'W_D': 102.176, 'J_D': 1465.6872197418213, 'W_D_1KI': 5.288065417658628, 'J_D_1KI': 0.2736810587754181}
[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47]
[138.56]
14.24259638786316
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, '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': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56}
[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47, 40.91, 39.98, 39.59, 39.45, 39.99, 39.93, 40.26, 39.5, 39.6, 39.56]
718.2049999999999
35.91025
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, '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': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56, 'J_1KI': 103.90428871175271, 'W_1KI': 7.295319328173537, 'W_D': 102.64975000000001, 'J_D': 1461.9989585650565, 'W_D_1KI': 5.404609593007951, 'J_D_1KI': 0.2845579736222793}

1
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4731, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.47292685508728, "TIME_S_1KI": 2.425053235063894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1834.0436391162873, "W": 121.03, "J_1KI": 387.66511078340466, "W_1KI": 25.582329317269078, "W_D": 85.37275, "J_D": 1293.70692465806, "W_D_1KI": 18.04539209469457, "J_D_1KI": 3.814287062924238}

105
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
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@ -1,105 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2614312171936035}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2042, 4085, ..., 9995953,
9997989, 10000000]),
col_indices=tensor([ 0, 1, 5, ..., 4996, 4997, 4999]),
values=tensor([0.9986, 0.3546, 0.4267, ..., 0.5093, 0.4180, 0.9781]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.6145, 0.7478, 0.4380, ..., 0.5071, 0.9779, 0.3806])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 0.2614312171936035 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4016', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.667505025863647}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1990, 3989, ..., 9996010,
9997994, 10000000]),
col_indices=tensor([ 1, 4, 5, ..., 4995, 4997, 4999]),
values=tensor([0.6319, 0.2996, 0.5553, ..., 0.4232, 0.5114, 0.0407]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.4664, 0.6985, 0.4117, ..., 0.0590, 0.5393, 0.9628])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 9.667505025863647 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4361', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.678797960281372}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2007, 3964, ..., 9996001,
9997987, 10000000]),
col_indices=tensor([ 1, 7, 8, ..., 4994, 4995, 4997]),
values=tensor([0.8466, 0.2679, 0.7670, ..., 0.5879, 0.3748, 0.0373]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1526, 0.7358, 0.4701, ..., 0.9960, 0.7430, 0.4616])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 9.678797960281372 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4731', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.47292685508728}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1970, 4010, ..., 9995993,
9998023, 10000000]),
col_indices=tensor([ 8, 9, 11, ..., 4991, 4992, 4994]),
values=tensor([0.6655, 0.5354, 0.4656, ..., 0.4879, 0.8481, 0.6598]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.0638, 0.6144, 0.0359, ..., 0.3011, 0.2543, 0.1471])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 11.47292685508728 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, 1970, 4010, ..., 9995993,
9998023, 10000000]),
col_indices=tensor([ 8, 9, 11, ..., 4991, 4992, 4994]),
values=tensor([0.6655, 0.5354, 0.4656, ..., 0.4879, 0.8481, 0.6598]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.0638, 0.6144, 0.0359, ..., 0.3011, 0.2543, 0.1471])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 11.47292685508728 seconds
[39.98, 39.12, 39.4, 39.16, 39.21, 39.21, 39.11, 39.42, 39.73, 39.08]
[121.03]
15.1536283493042
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.47292685508728, 'TIME_S_1KI': 2.425053235063894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.0436391162873, 'W': 121.03}
[39.98, 39.12, 39.4, 39.16, 39.21, 39.21, 39.11, 39.42, 39.73, 39.08, 39.95, 39.19, 39.66, 39.3, 39.21, 39.17, 39.07, 44.88, 39.22, 39.16]
713.145
35.65725
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.47292685508728, 'TIME_S_1KI': 2.425053235063894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.0436391162873, 'W': 121.03, 'J_1KI': 387.66511078340466, 'W_1KI': 25.582329317269078, 'W_D': 85.37275, 'J_D': 1293.70692465806, 'W_D_1KI': 18.04539209469457, 'J_D_1KI': 3.814287062924238}

1
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
View File

@ -1 +0,0 @@
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3442, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.042348861694336, "TIME_S_1KI": 2.9175911858496035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1718.164745798111, "W": 121.03, "J_1KI": 499.1762771057847, "W_1KI": 35.16269610691458, "W_D": 85.32925, "J_D": 1211.350153973341, "W_D_1KI": 24.79060139453806, "J_D_1KI": 7.202382741004667}

65
pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
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@ -1,65 +0,0 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.3049776554107666}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2533, 4997, ..., 12495039,
12497522, 12500000]),
col_indices=tensor([ 0, 3, 4, ..., 4997, 4998, 4999]),
values=tensor([0.1496, 0.2452, 0.1750, ..., 0.2661, 0.4996, 0.2189]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.2427, 0.7858, 0.7306, ..., 0.9706, 0.6342, 0.3926])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 0.3049776554107666 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3442', '-ss', '5000', '-sd', '0.5', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.042348861694336}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2398, 4864, ..., 12494963,
12497460, 12500000]),
col_indices=tensor([ 0, 1, 3, ..., 4991, 4993, 4998]),
values=tensor([0.1337, 0.4343, 0.0128, ..., 0.8248, 0.7529, 0.8640]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.5366, 0.6388, 0.1814, ..., 0.4104, 0.8844, 0.8048])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 10.042348861694336 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, 2398, 4864, ..., 12494963,
12497460, 12500000]),
col_indices=tensor([ 0, 1, 3, ..., 4991, 4993, 4998]),
values=tensor([0.1337, 0.4343, 0.0128, ..., 0.8248, 0.7529, 0.8640]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.5366, 0.6388, 0.1814, ..., 0.4104, 0.8844, 0.8048])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 10.042348861694336 seconds
[40.84, 39.12, 39.49, 39.13, 39.27, 39.35, 39.11, 39.03, 39.19, 39.06]
[121.03]
14.196188926696777
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3442, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.042348861694336, 'TIME_S_1KI': 2.9175911858496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1718.164745798111, 'W': 121.03}
[40.84, 39.12, 39.49, 39.13, 39.27, 39.35, 39.11, 39.03, 39.19, 39.06, 42.71, 40.11, 39.63, 39.49, 40.35, 39.8, 39.81, 39.58, 40.7, 39.1]
714.0150000000001
35.700750000000006
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3442, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.042348861694336, 'TIME_S_1KI': 2.9175911858496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1718.164745798111, 'W': 121.03, 'J_1KI': 499.1762771057847, 'W_1KI': 35.16269610691458, 'W_D': 85.32925, 'J_D': 1211.350153973341, 'W_D_1KI': 24.79060139453806, 'J_D_1KI': 7.202382741004667}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8765, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.565605640411377, "TIME_S_1KI": 1.2054313337605678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1349.8730677318572, "W": 85.96, "J_1KI": 154.0071954058023, "W_1KI": 9.807187678265828, "W_D": 69.70649999999999, "J_D": 1094.636191203475, "W_D_1KI": 7.952823730747289, "J_D_1KI": 0.9073387028804666}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8596, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.36475419998169, "TIME_S_1KI": 1.2057647975781398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.4265056419372, "W": 84.46, "J_1KI": 159.77507045625143, "W_1KI": 9.825500232666355, "W_D": 68.352, "J_D": 1111.4900368652345, "W_D_1KI": 7.95160539785947, "J_D_1KI": 0.9250355279036144}

58
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
View File

@ -1,14 +1,14 @@
['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', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1979267597198486}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.2214851379394531}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 503, 997, ..., 4999030,
4999508, 5000000]),
col_indices=tensor([ 13, 17, 19, ..., 9920, 9929, 9953]),
values=tensor([0.9385, 0.6026, 0.1531, ..., 0.7529, 0.2170, 0.3875]),
tensor(crow_indices=tensor([ 0, 508, 1039, ..., 4998996,
4999496, 5000000]),
col_indices=tensor([ 26, 30, 52, ..., 9932, 9973, 9998]),
values=tensor([0.9442, 0.4994, 0.3456, ..., 0.3930, 0.0474, 0.1408]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.6172, 0.1221, 0.7807, ..., 0.3915, 0.5006, 0.2223])
tensor([0.1466, 0.3658, 0.5068, ..., 0.5229, 0.0306, 0.8484])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 1.1979267597198486 seconds
Time: 1.2214851379394531 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', '8765', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.565605640411377}
['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', '8596', '-ss', '10000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.36475419998169}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 969, ..., 4998985,
4999485, 5000000]),
col_indices=tensor([ 18, 30, 44, ..., 9958, 9974, 9994]),
values=tensor([0.9183, 0.2043, 0.3929, ..., 0.1798, 0.2421, 0.5984]),
tensor(crow_indices=tensor([ 0, 466, 997, ..., 4998966,
4999476, 5000000]),
col_indices=tensor([ 0, 1, 5, ..., 9962, 9965, 9989]),
values=tensor([0.4765, 0.8415, 0.0752, ..., 0.8745, 0.5765, 0.8508]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.9280, 0.7586, 0.0981, ..., 0.8069, 0.8205, 0.0580])
tensor([0.2258, 0.4418, 0.4951, ..., 0.4808, 0.0990, 0.6508])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,16 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.565605640411377 seconds
Time: 10.36475419998169 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, 511, 969, ..., 4998985,
4999485, 5000000]),
col_indices=tensor([ 18, 30, 44, ..., 9958, 9974, 9994]),
values=tensor([0.9183, 0.2043, 0.3929, ..., 0.1798, 0.2421, 0.5984]),
tensor(crow_indices=tensor([ 0, 466, 997, ..., 4998966,
4999476, 5000000]),
col_indices=tensor([ 0, 1, 5, ..., 9962, 9965, 9989]),
values=tensor([0.4765, 0.8415, 0.0752, ..., 0.8745, 0.5765, 0.8508]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.9280, 0.7586, 0.0981, ..., 0.8069, 0.8205, 0.0580])
tensor([0.2258, 0.4418, 0.4951, ..., 0.4808, 0.0990, 0.6508])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +53,13 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.565605640411377 seconds
Time: 10.36475419998169 seconds
[18.27, 17.87, 17.87, 17.65, 18.34, 18.87, 17.99, 17.76, 18.35, 17.83]
[85.96]
15.703502416610718
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.565605640411377, 'TIME_S_1KI': 1.2054313337605678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.8730677318572, 'W': 85.96}
[18.27, 17.87, 17.87, 17.65, 18.34, 18.87, 17.99, 17.76, 18.35, 17.83, 18.43, 17.64, 18.08, 18.62, 18.12, 17.89, 17.87, 17.65, 18.18, 18.11]
325.07
16.2535
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.565605640411377, 'TIME_S_1KI': 1.2054313337605678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.8730677318572, 'W': 85.96, 'J_1KI': 154.0071954058023, 'W_1KI': 9.807187678265828, 'W_D': 69.70649999999999, 'J_D': 1094.636191203475, 'W_D_1KI': 7.952823730747289, 'J_D_1KI': 0.9073387028804666}
[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11]
[84.46]
16.261265754699707
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46}
[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11, 18.58, 18.42, 17.91, 17.71, 17.91, 17.76, 17.61, 17.59, 17.81, 18.34]
322.15999999999997
16.107999999999997
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46, 'J_1KI': 159.77507045625143, 'W_1KI': 9.825500232666355, 'W_D': 68.352, 'J_D': 1111.4900368652345, 'W_D_1KI': 7.95160539785947, 'J_D_1KI': 0.9250355279036144}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2733, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.100283861160278, "TIME_S_1KI": 3.695676495119019, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1379.3402201175688, "W": 80.38, "J_1KI": 504.69821445941045, "W_1KI": 29.410903768752288, "W_D": 64.47149999999999, "J_D": 1106.3465165627001, "W_D_1KI": 23.590010976948406, "J_D_1KI": 8.631544448206515}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.339906692504883, "TIME_S_1KI": 3.855297051642387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1411.3430524110793, "W": 81.1, "J_1KI": 526.227834605175, "W_1KI": 30.2386278896346, "W_D": 64.94024999999999, "J_D": 1130.1229427785277, "W_D_1KI": 24.21336689038031, "J_D_1KI": 9.02810100312465}

58
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
View File

@ -1,14 +1,14 @@
['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', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.8413217067718506}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.9144444465637207}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1005, 2046, ..., 9997977,
9998998, 10000000]),
col_indices=tensor([ 2, 12, 23, ..., 9983, 9984, 9993]),
values=tensor([0.7359, 0.8841, 0.1080, ..., 0.9122, 0.7253, 0.6265]),
tensor(crow_indices=tensor([ 0, 1047, 2059, ..., 9998021,
9999010, 10000000]),
col_indices=tensor([ 5, 8, 10, ..., 9982, 9985, 9996]),
values=tensor([0.6280, 0.3451, 0.1654, ..., 0.9055, 0.0155, 0.3514]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.8932, 0.3673, 0.9234, ..., 0.2176, 0.0275, 0.3760])
tensor([0.4839, 0.5284, 0.4128, ..., 0.3450, 0.9191, 0.1662])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 3.8413217067718506 seconds
Time: 3.9144444465637207 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', '2733', '-ss', '10000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.100283861160278}
['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', '2682', '-ss', '10000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.339906692504883}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 961, 1970, ..., 9997917,
9998991, 10000000]),
col_indices=tensor([ 3, 10, 20, ..., 9978, 9982, 9998]),
values=tensor([0.4857, 0.9168, 0.4028, ..., 0.0388, 0.1577, 0.6588]),
tensor(crow_indices=tensor([ 0, 1046, 1985, ..., 9998014,
9998990, 10000000]),
col_indices=tensor([ 17, 31, 38, ..., 9973, 9978, 9985]),
values=tensor([0.0174, 0.0178, 0.9247, ..., 0.4423, 0.5021, 0.4230]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1504, 0.7286, 0.9298, ..., 0.2641, 0.6031, 0.0488])
tensor([0.8246, 0.4652, 0.0479, ..., 0.0955, 0.8235, 0.1184])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,16 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 10.100283861160278 seconds
Time: 10.339906692504883 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, 961, 1970, ..., 9997917,
9998991, 10000000]),
col_indices=tensor([ 3, 10, 20, ..., 9978, 9982, 9998]),
values=tensor([0.4857, 0.9168, 0.4028, ..., 0.0388, 0.1577, 0.6588]),
tensor(crow_indices=tensor([ 0, 1046, 1985, ..., 9998014,
9998990, 10000000]),
col_indices=tensor([ 17, 31, 38, ..., 9973, 9978, 9985]),
values=tensor([0.0174, 0.0178, 0.9247, ..., 0.4423, 0.5021, 0.4230]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1504, 0.7286, 0.9298, ..., 0.2641, 0.6031, 0.0488])
tensor([0.8246, 0.4652, 0.0479, ..., 0.0955, 0.8235, 0.1184])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +53,13 @@ Rows: 10000
Size: 100000000
NNZ: 10000000
Density: 0.1
Time: 10.100283861160278 seconds
Time: 10.339906692504883 seconds
[18.01, 17.45, 17.58, 17.83, 17.38, 17.63, 17.79, 17.67, 17.39, 17.59]
[80.38]
17.16024160385132
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.100283861160278, 'TIME_S_1KI': 3.695676495119019, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.3402201175688, 'W': 80.38}
[18.01, 17.45, 17.58, 17.83, 17.38, 17.63, 17.79, 17.67, 17.39, 17.59, 18.23, 17.44, 17.92, 17.53, 18.07, 17.84, 17.81, 17.59, 17.47, 17.73]
318.1700000000001
15.908500000000004
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.100283861160278, 'TIME_S_1KI': 3.695676495119019, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.3402201175688, 'W': 80.38, 'J_1KI': 504.69821445941045, 'W_1KI': 29.410903768752288, 'W_D': 64.47149999999999, 'J_D': 1106.3465165627001, 'W_D_1KI': 23.590010976948406, 'J_D_1KI': 8.631544448206515}
[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92]
[81.1]
17.402503728866577
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1}
[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92, 18.28, 17.68, 17.82, 17.91, 18.12, 17.75, 18.95, 17.54, 17.87, 17.65]
323.195
16.15975
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1, 'J_1KI': 526.227834605175, 'W_1KI': 30.2386278896346, 'W_D': 64.94024999999999, 'J_D': 1130.1229427785277, 'W_D_1KI': 24.21336689038031, 'J_D_1KI': 9.02810100312465}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.305012702941895, "TIME_S_1KI": 7.0293401793600925, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2200.8211415290834, "W": 63.38, "J_1KI": 1501.2422520662235, "W_1KI": 43.23328785811733, "W_D": 47.25450000000001, "J_D": 1640.875712091923, "W_D_1KI": 32.233628922237386, "J_D_1KI": 21.98746856905688}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1445, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012, "TIME_S_1KI": 6.98986845445468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2160.905471148491, "W": 62.46, "J_1KI": 1495.4363122134887, "W_1KI": 43.22491349480969, "W_D": 46.34675, "J_D": 1603.4413327721954, "W_D_1KI": 32.07387543252595, "J_D_1KI": 22.196453586523152}

58
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
View File

@ -1,14 +1,14 @@
['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', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.160099029541016}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.263561964035034}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2043, 4140, ..., 19995945,
19998003, 20000000]),
col_indices=tensor([ 9, 18, 21, ..., 9988, 9992, 9993]),
values=tensor([0.2892, 0.5577, 0.6311, ..., 0.1921, 0.3093, 0.8733]),
tensor(crow_indices=tensor([ 0, 1926, 3961, ..., 19995971,
19997972, 20000000]),
col_indices=tensor([ 0, 6, 15, ..., 9987, 9993, 9997]),
values=tensor([0.0314, 0.6999, 0.8853, ..., 0.3076, 0.0508, 0.1862]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1991, 0.2737, 0.2500, ..., 0.3139, 0.4046, 0.9981])
tensor([0.4423, 0.2673, 0.1161, ..., 0.1117, 0.7670, 0.7166])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 7.160099029541016 seconds
Time: 7.263561964035034 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', '1466', '-ss', '10000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.305012702941895}
['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', '1445', '-ss', '10000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1974, 3955, ..., 19996112,
19998028, 20000000]),
col_indices=tensor([ 4, 6, 10, ..., 9992, 9994, 9998]),
values=tensor([0.7033, 0.3961, 0.2301, ..., 0.8377, 0.1653, 0.2698]),
tensor(crow_indices=tensor([ 0, 2031, 4064, ..., 19995881,
19997886, 20000000]),
col_indices=tensor([ 6, 7, 12, ..., 9992, 9994, 9996]),
values=tensor([0.1267, 0.2771, 0.5074, ..., 0.4122, 0.4435, 0.1814]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.6095, 0.7552, 0.1154, ..., 0.2753, 0.8409, 0.9940])
tensor([0.2940, 0.6612, 0.2260, ..., 0.5354, 0.3344, 0.1796])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,16 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 10.305012702941895 seconds
Time: 10.100359916687012 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, 1974, 3955, ..., 19996112,
19998028, 20000000]),
col_indices=tensor([ 4, 6, 10, ..., 9992, 9994, 9998]),
values=tensor([0.7033, 0.3961, 0.2301, ..., 0.8377, 0.1653, 0.2698]),
tensor(crow_indices=tensor([ 0, 2031, 4064, ..., 19995881,
19997886, 20000000]),
col_indices=tensor([ 6, 7, 12, ..., 9992, 9994, 9996]),
values=tensor([0.1267, 0.2771, 0.5074, ..., 0.4122, 0.4435, 0.1814]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.6095, 0.7552, 0.1154, ..., 0.2753, 0.8409, 0.9940])
tensor([0.2940, 0.6612, 0.2260, ..., 0.5354, 0.3344, 0.1796])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +53,13 @@ Rows: 10000
Size: 100000000
NNZ: 20000000
Density: 0.2
Time: 10.305012702941895 seconds
Time: 10.100359916687012 seconds
[18.39, 17.53, 17.66, 17.9, 17.98, 17.61, 17.98, 18.92, 18.01, 17.69]
[63.38]
34.72422122955322
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.305012702941895, 'TIME_S_1KI': 7.0293401793600925, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2200.8211415290834, 'W': 63.38}
[18.39, 17.53, 17.66, 17.9, 17.98, 17.61, 17.98, 18.92, 18.01, 17.69, 18.14, 18.53, 17.76, 17.48, 17.87, 17.7, 17.84, 17.65, 18.06, 17.84]
322.51
16.1255
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.305012702941895, 'TIME_S_1KI': 7.0293401793600925, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2200.8211415290834, 'W': 63.38, 'J_1KI': 1501.2422520662235, 'W_1KI': 43.23328785811733, 'W_D': 47.25450000000001, 'J_D': 1640.875712091923, 'W_D_1KI': 32.233628922237386, 'J_D_1KI': 21.98746856905688}
[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95]
[62.46]
34.59662938117981
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46}
[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95, 18.39, 17.61, 17.65, 17.9, 17.98, 17.61, 17.59, 17.93, 17.63, 17.64]
322.265
16.11325
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46, 'J_1KI': 1495.4363122134887, 'W_1KI': 43.22491349480969, 'W_D': 46.34675, 'J_D': 1603.4413327721954, 'W_D_1KI': 32.07387543252595, 'J_D_1KI': 22.196453586523152}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
View File

@ -1 +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": 11.716556549072266, "TIME_S_1KI": 11.716556549072266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4241.666789231301, "W": 53.31, "J_1KI": 4241.666789231301, "W_1KI": 53.31, "W_D": 37.065, "J_D": 2949.116104724407, "W_D_1KI": 37.065, "J_D_1KI": 37.065}
{"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": 11.531069993972778, "TIME_S_1KI": 11.531069993972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4119.280698208809, "W": 53.23, "J_1KI": 4119.280698208809, "W_1KI": 53.23, "W_D": 37.1785, "J_D": 2877.1121066758633, "W_D_1KI": 37.1785, "J_D_1KI": 37.1785}

42
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
View File

@ -1,14 +1,14 @@
['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": 11.716556549072266}
{"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": 11.531069993972778}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3043, 6100, ..., 29993952,
29997033, 30000000]),
col_indices=tensor([ 1, 6, 7, ..., 9991, 9996, 9999]),
values=tensor([0.2505, 0.5332, 0.4314, ..., 0.9186, 0.8523, 0.0373]),
tensor(crow_indices=tensor([ 0, 3017, 6060, ..., 29994067,
29997064, 30000000]),
col_indices=tensor([ 8, 9, 10, ..., 9986, 9991, 9993]),
values=tensor([0.4919, 0.2111, 0.3595, ..., 0.1115, 0.4648, 0.4893]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.5706, 0.2901, 0.6021, ..., 0.8123, 0.3136, 0.2413])
tensor([0.2808, 0.4380, 0.4720, ..., 0.7949, 0.9847, 0.6708])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 11.716556549072266 seconds
Time: 11.531069993972778 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, 3043, 6100, ..., 29993952,
29997033, 30000000]),
col_indices=tensor([ 1, 6, 7, ..., 9991, 9996, 9999]),
values=tensor([0.2505, 0.5332, 0.4314, ..., 0.9186, 0.8523, 0.0373]),
tensor(crow_indices=tensor([ 0, 3017, 6060, ..., 29994067,
29997064, 30000000]),
col_indices=tensor([ 8, 9, 10, ..., 9986, 9991, 9993]),
values=tensor([0.4919, 0.2111, 0.3595, ..., 0.1115, 0.4648, 0.4893]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.5706, 0.2901, 0.6021, ..., 0.8123, 0.3136, 0.2413])
tensor([0.2808, 0.4380, 0.4720, ..., 0.7949, 0.9847, 0.6708])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 30000000
Density: 0.3
Time: 11.716556549072266 seconds
Time: 11.531069993972778 seconds
[18.37, 17.73, 18.07, 17.59, 17.73, 17.59, 17.79, 17.61, 17.6, 17.94]
[53.31]
79.56606245040894
{'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': 11.716556549072266, 'TIME_S_1KI': 11.716556549072266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4241.666789231301, 'W': 53.31}
[18.37, 17.73, 18.07, 17.59, 17.73, 17.59, 17.79, 17.61, 17.6, 17.94, 18.37, 17.63, 18.42, 21.66, 17.64, 17.42, 18.81, 17.54, 17.75, 17.96]
324.90000000000003
16.245
{'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': 11.716556549072266, 'TIME_S_1KI': 11.716556549072266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4241.666789231301, 'W': 53.31, 'J_1KI': 4241.666789231301, 'W_1KI': 53.31, 'W_D': 37.065, 'J_D': 2949.116104724407, 'W_D_1KI': 37.065, 'J_D_1KI': 37.065}
[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55]
[53.23]
77.38644933700562
{'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': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23}
[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55, 18.93, 17.87, 17.97, 17.66, 17.7, 17.69, 17.69, 17.51, 17.83, 17.79]
321.03
16.051499999999997
{'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': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23, 'J_1KI': 4119.280698208809, 'W_1KI': 53.23, 'W_D': 37.1785, 'J_D': 2877.1121066758633, 'W_D_1KI': 37.1785, 'J_D_1KI': 37.1785}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
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@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43553, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.298628091812134, "TIME_S_1KI": 0.23646196798870647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.7661000442506, "W": 89.26, "J_1KI": 28.993779993209436, "W_1KI": 2.0494569834454572, "W_D": 72.47675000000001, "J_D": 1025.3325447163584, "W_D_1KI": 1.664104654099603, "J_D_1KI": 0.03820872624387764}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 44182, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514305114746094, "TIME_S_1KI": 0.2379771199752409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.0750387310982, "W": 89.25, "J_1KI": 28.723802424767964, "W_1KI": 2.020053415418044, "W_D": 73.10575, "J_D": 1039.51465000242, "W_D_1KI": 1.6546500837445115, "J_D_1KI": 0.03745077370296753}

78
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
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@ -1,14 +1,14 @@
['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', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.24108004570007324}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.25171899795532227}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 254, 488, ..., 1249514,
1249753, 1250000]),
col_indices=tensor([ 20, 116, 133, ..., 4920, 4936, 4946]),
values=tensor([0.1564, 0.7439, 0.0267, ..., 0.8153, 0.5940, 0.0091]),
tensor(crow_indices=tensor([ 0, 257, 510, ..., 1249508,
1249742, 1250000]),
col_indices=tensor([ 10, 14, 18, ..., 4971, 4976, 4993]),
values=tensor([0.7215, 0.2673, 0.1887, ..., 0.2021, 0.2524, 0.6553]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.2737, 0.8794, 0.7768, ..., 0.6794, 0.5883, 0.1555])
tensor([0.6580, 0.5682, 0.5133, ..., 0.8598, 0.8673, 0.3117])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 0.24108004570007324 seconds
Time: 0.25171899795532227 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', '43553', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.298628091812134}
['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', '41713', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.913089752197266}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 228, 477, ..., 1249472,
1249753, 1250000]),
col_indices=tensor([ 33, 68, 106, ..., 4915, 4934, 4973]),
values=tensor([0.4796, 0.5786, 0.7704, ..., 0.3679, 0.0791, 0.9103]),
tensor(crow_indices=tensor([ 0, 230, 476, ..., 1249535,
1249754, 1250000]),
col_indices=tensor([ 26, 32, 49, ..., 4901, 4965, 4968]),
values=tensor([0.6798, 0.0159, 0.6379, ..., 0.7230, 0.8415, 0.2703]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.4175, 0.6924, 0.0772, ..., 0.0345, 0.5597, 0.1347])
tensor([0.9905, 0.3660, 0.2565, ..., 0.2843, 0.2598, 0.4388])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,16 +36,19 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 10.298628091812134 seconds
Time: 9.913089752197266 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', '44182', '-ss', '5000', '-sd', '0.05', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514305114746094}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 228, 477, ..., 1249472,
1249753, 1250000]),
col_indices=tensor([ 33, 68, 106, ..., 4915, 4934, 4973]),
values=tensor([0.4796, 0.5786, 0.7704, ..., 0.3679, 0.0791, 0.9103]),
tensor(crow_indices=tensor([ 0, 234, 486, ..., 1249518,
1249775, 1250000]),
col_indices=tensor([ 2, 39, 64, ..., 4915, 4964, 4987]),
values=tensor([0.0513, 0.5642, 0.6511, ..., 0.0332, 0.5293, 0.6294]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.4175, 0.6924, 0.0772, ..., 0.0345, 0.5597, 0.1347])
tensor([0.8655, 0.6659, 0.2976, ..., 0.0599, 0.2467, 0.0329])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -53,13 +56,30 @@ Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 10.298628091812134 seconds
Time: 10.514305114746094 seconds
[18.22, 18.23, 17.9, 17.56, 17.94, 17.95, 17.83, 17.93, 21.91, 17.95]
[89.26]
14.147054672241211
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43553, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.298628091812134, 'TIME_S_1KI': 0.23646196798870647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.7661000442506, 'W': 89.26}
[18.22, 18.23, 17.9, 17.56, 17.94, 17.95, 17.83, 17.93, 21.91, 17.95, 18.27, 17.9, 17.95, 22.05, 18.3, 22.03, 18.06, 18.09, 17.91, 17.81]
335.66499999999996
16.78325
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43553, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.298628091812134, 'TIME_S_1KI': 0.23646196798870647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.7661000442506, 'W': 89.26, 'J_1KI': 28.993779993209436, 'W_1KI': 2.0494569834454572, 'W_D': 72.47675000000001, 'J_D': 1025.3325447163584, 'W_D_1KI': 1.664104654099603, 'J_D_1KI': 0.03820872624387764}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 234, 486, ..., 1249518,
1249775, 1250000]),
col_indices=tensor([ 2, 39, 64, ..., 4915, 4964, 4987]),
values=tensor([0.0513, 0.5642, 0.6511, ..., 0.0332, 0.5293, 0.6294]),
size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
tensor([0.8655, 0.6659, 0.2976, ..., 0.0599, 0.2467, 0.0329])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 1250000
Density: 0.05
Time: 10.514305114746094 seconds
[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84]
[89.25]
14.219328165054321
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25}
[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84, 18.13, 17.61, 17.91, 17.95, 20.68, 17.51, 17.88, 18.51, 17.79, 17.64]
322.885
16.14425
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25, 'J_1KI': 28.723802424767964, 'W_1KI': 2.020053415418044, 'W_D': 73.10575, 'J_D': 1039.51465000242, 'W_D_1KI': 1.6546500837445115, 'J_D_1KI': 0.03745077370296753}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 18622, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317775011062622, "TIME_S_1KI": 0.5540637424048234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1264.8904221296311, "W": 88.65, "J_1KI": 67.9245205740324, "W_1KI": 4.760498335302331, "W_D": 72.60650000000001, "J_D": 1035.9759327056408, "W_D_1KI": 3.8989635914509724, "J_D_1KI": 0.2093740517372448}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19098, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.554213762283325, "TIME_S_1KI": 0.552634504256117, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.880751209259, "W": 88.77, "J_1KI": 68.06371092309452, "W_1KI": 4.64813069431354, "W_D": 72.8915, "J_D": 1067.3680046949385, "W_D_1KI": 3.8167085558697242, "J_D_1KI": 0.19984859963712034}

58
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
View File

@ -1,14 +1,14 @@
['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', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5638444423675537}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5497848987579346}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 491, 1004, ..., 2498969,
2499500, 2500000]),
col_indices=tensor([ 22, 38, 49, ..., 4962, 4970, 4976]),
values=tensor([0.5529, 0.3874, 0.4848, ..., 0.5629, 0.9931, 0.9487]),
tensor(crow_indices=tensor([ 0, 487, 1016, ..., 2499032,
2499521, 2500000]),
col_indices=tensor([ 8, 18, 46, ..., 4955, 4960, 4970]),
values=tensor([0.9231, 0.4693, 0.9835, ..., 0.3329, 0.5539, 0.9296]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.1713, 0.2154, 0.4105, ..., 0.0933, 0.5075, 0.3878])
tensor([0.7808, 0.5215, 0.8582, ..., 0.9627, 0.6165, 0.7692])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 0.5638444423675537 seconds
Time: 0.5497848987579346 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', '18622', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317775011062622}
['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', '19098', '-ss', '5000', '-sd', '0.1', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.554213762283325}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 525, 987, ..., 2498981,
2499483, 2500000]),
col_indices=tensor([ 8, 19, 22, ..., 4987, 4993, 4999]),
values=tensor([0.0390, 0.1537, 0.3143, ..., 0.0698, 0.3942, 0.2310]),
tensor(crow_indices=tensor([ 0, 480, 948, ..., 2499011,
2499537, 2500000]),
col_indices=tensor([ 1, 8, 11, ..., 4950, 4969, 4990]),
values=tensor([0.1158, 0.8497, 0.7920, ..., 0.6315, 0.2224, 0.2676]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.3040, 0.7679, 0.7012, ..., 0.6231, 0.1253, 0.7846])
tensor([0.4700, 0.1718, 0.6444, ..., 0.1980, 0.7458, 0.7705])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,16 +36,16 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.317775011062622 seconds
Time: 10.554213762283325 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, 525, 987, ..., 2498981,
2499483, 2500000]),
col_indices=tensor([ 8, 19, 22, ..., 4987, 4993, 4999]),
values=tensor([0.0390, 0.1537, 0.3143, ..., 0.0698, 0.3942, 0.2310]),
tensor(crow_indices=tensor([ 0, 480, 948, ..., 2499011,
2499537, 2500000]),
col_indices=tensor([ 1, 8, 11, ..., 4950, 4969, 4990]),
values=tensor([0.1158, 0.8497, 0.7920, ..., 0.6315, 0.2224, 0.2676]),
size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
tensor([0.3040, 0.7679, 0.7012, ..., 0.6231, 0.1253, 0.7846])
tensor([0.4700, 0.1718, 0.6444, ..., 0.1980, 0.7458, 0.7705])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -53,13 +53,13 @@ Rows: 5000
Size: 25000000
NNZ: 2500000
Density: 0.1
Time: 10.317775011062622 seconds
Time: 10.554213762283325 seconds
[18.14, 17.77, 17.8, 18.17, 17.99, 17.63, 17.94, 18.68, 17.91, 17.77]
[88.65]
14.26836347579956
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317775011062622, 'TIME_S_1KI': 0.5540637424048234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.8904221296311, 'W': 88.65}
[18.14, 17.77, 17.8, 18.17, 17.99, 17.63, 17.94, 18.68, 17.91, 17.77, 17.91, 17.46, 17.97, 17.49, 17.74, 17.66, 17.77, 17.61, 17.59, 17.56]
320.87
16.0435
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317775011062622, 'TIME_S_1KI': 0.5540637424048234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.8904221296311, 'W': 88.65, 'J_1KI': 67.9245205740324, 'W_1KI': 4.760498335302331, 'W_D': 72.60650000000001, 'J_D': 1035.9759327056408, 'W_D_1KI': 3.8989635914509724, 'J_D_1KI': 0.2093740517372448}
[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41]
[88.77]
14.643243789672852
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77}
[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41, 18.1, 17.72, 17.57, 17.35, 17.6, 17.54, 17.53, 17.37, 18.02, 17.43]
317.57000000000005
15.878500000000003
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77, 'J_1KI': 68.06371092309452, 'W_1KI': 4.64813069431354, 'W_D': 72.8915, 'J_D': 1067.3680046949385, 'W_D_1KI': 3.8167085558697242, 'J_D_1KI': 0.19984859963712034}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8928, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.436826467514038, "TIME_S_1KI": 1.1689993803219128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1316.4306947374344, "W": 86.99, "J_1KI": 147.4496745897664, "W_1KI": 9.743503584229389, "W_D": 70.63274999999999, "J_D": 1068.894357440114, "W_D_1KI": 7.911374327956988, "J_D_1KI": 0.8861306370919565}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252, "TIME_S_1KI": 1.175245683415114, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1354.9685973644257, "W": 87.3, "J_1KI": 149.42309190167904, "W_1KI": 9.62726069695633, "W_D": 71.26675, "J_D": 1106.11922435534, "W_D_1KI": 7.859147551830612, "J_D_1KI": 0.8666902902327539}

60
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
View File

@ -1,14 +1,14 @@
['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', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.1760613918304443}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.157815933227539}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 978, 2025, ..., 4997968,
4998985, 5000000]),
col_indices=tensor([ 2, 4, 11, ..., 4986, 4990, 4994]),
values=tensor([0.0122, 0.4949, 0.8008, ..., 0.1011, 0.5261, 0.6800]),
tensor(crow_indices=tensor([ 0, 951, 1952, ..., 4997990,
4999010, 5000000]),
col_indices=tensor([ 1, 2, 7, ..., 4975, 4984, 4994]),
values=tensor([0.2978, 0.3383, 0.4211, ..., 0.6173, 0.3619, 0.1875]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.4016, 0.7392, 0.9091, ..., 0.8685, 0.7503, 0.4791])
tensor([0.3712, 0.9173, 0.1615, ..., 0.0466, 0.6664, 0.8295])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,20 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 1.1760613918304443 seconds
Time: 1.157815933227539 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', '8928', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.436826467514038}
['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', '9068', '-ss', '5000', '-sd', '0.2', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 990, 1955, ..., 4998009,
4998983, 5000000]),
col_indices=tensor([ 11, 14, 16, ..., 4997, 4998, 4999]),
values=tensor([0.2669, 0.2498, 0.4510, ..., 0.5817, 0.0466, 0.6772]),
tensor(crow_indices=tensor([ 0, 1038, 2022, ..., 4998028,
4999030, 5000000]),
col_indices=tensor([ 5, 8, 18, ..., 4988, 4996, 4999]),
values=tensor([0.3556, 0.0118, 0.2581, ..., 0.9541, 0.9641, 0.1138]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.9945, 0.9638, 0.1643, ..., 0.2698, 0.5025, 0.0642])
tensor([2.9156e-01, 7.5281e-01, 6.6249e-01, ..., 5.6286e-01, 2.5839e-04,
4.1914e-01])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,16 +37,17 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 10.436826467514038 seconds
Time: 10.657127857208252 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, 990, 1955, ..., 4998009,
4998983, 5000000]),
col_indices=tensor([ 11, 14, 16, ..., 4997, 4998, 4999]),
values=tensor([0.2669, 0.2498, 0.4510, ..., 0.5817, 0.0466, 0.6772]),
tensor(crow_indices=tensor([ 0, 1038, 2022, ..., 4998028,
4999030, 5000000]),
col_indices=tensor([ 5, 8, 18, ..., 4988, 4996, 4999]),
values=tensor([0.3556, 0.0118, 0.2581, ..., 0.9541, 0.9641, 0.1138]),
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.9945, 0.9638, 0.1643, ..., 0.2698, 0.5025, 0.0642])
tensor([2.9156e-01, 7.5281e-01, 6.6249e-01, ..., 5.6286e-01, 2.5839e-04,
4.1914e-01])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -53,13 +55,13 @@ Rows: 5000
Size: 25000000
NNZ: 5000000
Density: 0.2
Time: 10.436826467514038 seconds
Time: 10.657127857208252 seconds
[18.18, 17.97, 22.4, 18.19, 17.69, 18.6, 17.87, 17.63, 17.75, 17.93]
[86.99]
15.133126735687256
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.436826467514038, 'TIME_S_1KI': 1.1689993803219128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.4306947374344, 'W': 86.99}
[18.18, 17.97, 22.4, 18.19, 17.69, 18.6, 17.87, 17.63, 17.75, 17.93, 18.01, 17.97, 18.36, 17.9, 17.82, 17.52, 18.18, 17.58, 17.7, 17.91]
327.145
16.35725
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.436826467514038, 'TIME_S_1KI': 1.1689993803219128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.4306947374344, 'W': 86.99, 'J_1KI': 147.4496745897664, 'W_1KI': 9.743503584229389, 'W_D': 70.63274999999999, 'J_D': 1068.894357440114, 'W_D_1KI': 7.911374327956988, 'J_D_1KI': 0.8861306370919565}
[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71]
[87.3]
15.52083158493042
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3}
[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71, 18.05, 17.78, 17.91, 17.54, 18.13, 17.46, 18.2, 17.86, 17.68, 17.62]
320.6650000000001
16.033250000000002
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3, 'J_1KI': 149.42309190167904, 'W_1KI': 9.62726069695633, 'W_D': 71.26675, 'J_D': 1106.11922435534, 'W_D_1KI': 7.859147551830612, 'J_D_1KI': 0.8666902902327539}

2
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
View File

@ -1 +1 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5478, "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": 10.325071811676025, "TIME_S_1KI": 1.8848250842782084, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1339.3279536104203, "W": 85.87, "J_1KI": 244.49214195151885, "W_1KI": 15.675428988682002, "W_D": 69.74000000000001, "J_D": 1087.7457957935335, "W_D_1KI": 12.730923694779118, "J_D_1KI": 2.3240094367979403}
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5664, "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": 10.680659770965576, "TIME_S_1KI": 1.8857097053258434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1382.186832485199, "W": 86.06, "J_1KI": 244.0301611026128, "W_1KI": 15.194209039548022, "W_D": 70.05975000000001, "J_D": 1125.2110613200666, "W_D_1KI": 12.369306144067798, "J_D_1KI": 2.1838464237407833}

58
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
View File

@ -1,14 +1,14 @@
['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": 1.9164557456970215}
{"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": 1.8537497520446777}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1507, 2972, ..., 7496967,
7498514, 7500000]),
col_indices=tensor([ 0, 1, 7, ..., 4979, 4980, 4982]),
values=tensor([0.5806, 0.6588, 0.4500, ..., 0.8030, 0.5443, 0.1304]),
tensor(crow_indices=tensor([ 0, 1463, 2978, ..., 7496989,
7498439, 7500000]),
col_indices=tensor([ 1, 2, 10, ..., 4991, 4992, 4997]),
values=tensor([0.5985, 0.8707, 0.1470, ..., 0.1515, 0.9789, 0.5190]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.5421, 0.7085, 0.6995, ..., 0.1955, 0.6605, 0.4332])
tensor([0.7595, 0.9851, 0.8459, ..., 0.8698, 0.1337, 0.7899])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -16,19 +16,19 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 1.9164557456970215 seconds
Time: 1.8537497520446777 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', '5478', '-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": 10.325071811676025}
['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', '5664', '-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": 10.680659770965576}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1469, 2909, ..., 7496983,
7498509, 7500000]),
col_indices=tensor([ 2, 8, 9, ..., 4995, 4997, 4998]),
values=tensor([0.1494, 0.3197, 0.4986, ..., 0.6801, 0.8207, 0.3086]),
tensor(crow_indices=tensor([ 0, 1531, 3042, ..., 7497046,
7498567, 7500000]),
col_indices=tensor([ 0, 1, 2, ..., 4993, 4996, 4997]),
values=tensor([0.2133, 0.5634, 0.4796, ..., 0.0244, 0.1739, 0.8367]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.0051, 0.9261, 0.2746, ..., 0.2239, 0.8025, 0.6653])
tensor([0.6801, 0.4978, 0.3141, ..., 0.6717, 0.6784, 0.5569])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -36,16 +36,16 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.325071811676025 seconds
Time: 10.680659770965576 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, 1469, 2909, ..., 7496983,
7498509, 7500000]),
col_indices=tensor([ 2, 8, 9, ..., 4995, 4997, 4998]),
values=tensor([0.1494, 0.3197, 0.4986, ..., 0.6801, 0.8207, 0.3086]),
tensor(crow_indices=tensor([ 0, 1531, 3042, ..., 7497046,
7498567, 7500000]),
col_indices=tensor([ 0, 1, 2, ..., 4993, 4996, 4997]),
values=tensor([0.2133, 0.5634, 0.4796, ..., 0.0244, 0.1739, 0.8367]),
size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
tensor([0.0051, 0.9261, 0.2746, ..., 0.2239, 0.8025, 0.6653])
tensor([0.6801, 0.4978, 0.3141, ..., 0.6717, 0.6784, 0.5569])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
@ -53,13 +53,13 @@ Rows: 5000
Size: 25000000
NNZ: 7500000
Density: 0.3
Time: 10.325071811676025 seconds
Time: 10.680659770965576 seconds
[18.13, 17.6, 17.78, 17.86, 17.7, 17.85, 17.51, 17.77, 17.69, 17.5]
[85.87]
15.597157955169678
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5478, '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': 10.325071811676025, 'TIME_S_1KI': 1.8848250842782084, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.3279536104203, 'W': 85.87}
[18.13, 17.6, 17.78, 17.86, 17.7, 17.85, 17.51, 17.77, 17.69, 17.5, 18.03, 17.39, 17.78, 17.59, 17.86, 17.49, 17.59, 17.65, 21.57, 18.18]
322.6
16.130000000000003
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5478, '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': 10.325071811676025, 'TIME_S_1KI': 1.8848250842782084, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.3279536104203, 'W': 85.87, 'J_1KI': 244.49214195151885, 'W_1KI': 15.675428988682002, 'W_D': 69.74000000000001, 'J_D': 1087.7457957935335, 'W_D_1KI': 12.730923694779118, 'J_D_1KI': 2.3240094367979403}
[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45]
[86.06]
16.060734748840332
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, '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': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06}
[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45, 18.18, 17.48, 18.72, 17.88, 18.28, 17.87, 17.95, 17.58, 17.89, 17.59]
320.00500000000005
16.00025
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, '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': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06, 'J_1KI': 244.0301611026128, 'W_1KI': 15.194209039548022, 'W_D': 70.05975000000001, 'J_D': 1125.2110613200666, 'W_D_1KI': 12.369306144067798, 'J_D_1KI': 2.1838464237407833}

1
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
View File

@ -1 +0,0 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2943, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.241667032241821, "TIME_S_1KI": 3.819798515882372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1393.030681221485, "W": 83.67, "J_1KI": 473.3369626984319, "W_1KI": 28.430173292558617, "W_D": 67.793, "J_D": 1128.6928286368848, "W_D_1KI": 23.035338090383963, "J_D_1KI": 7.82716211022221}

85
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
View File

@ -1,85 +0,0 @@
['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', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.38892388343811035}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2020, 4067, ..., 9996060,
9998056, 10000000]),
col_indices=tensor([ 1, 4, 6, ..., 4992, 4998, 4999]),
values=tensor([0.0057, 0.0348, 0.0719, ..., 0.9587, 0.3044, 0.9978]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1784, 0.7215, 0.7937, ..., 0.4492, 0.0838, 0.9744])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 0.38892388343811035 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', '2699', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.626921653747559}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1992, 4044, ..., 9995930,
9997986, 10000000]),
col_indices=tensor([ 0, 5, 10, ..., 4990, 4997, 4999]),
values=tensor([0.5930, 0.1714, 0.3969, ..., 0.4489, 0.5630, 0.4403]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.5546, 0.5609, 0.4280, ..., 0.4447, 0.9246, 0.2629])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 9.626921653747559 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', '2943', '-ss', '5000', '-sd', '0.4', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.241667032241821}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1928, 3951, ..., 9996029,
9997995, 10000000]),
col_indices=tensor([ 1, 3, 6, ..., 4994, 4996, 4997]),
values=tensor([0.0792, 0.4043, 0.0506, ..., 0.1635, 0.6350, 0.0332]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.4326, 0.5030, 0.1276, ..., 0.3674, 0.6623, 0.2020])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 11.241667032241821 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, 1928, 3951, ..., 9996029,
9997995, 10000000]),
col_indices=tensor([ 1, 3, 6, ..., 4994, 4996, 4997]),
values=tensor([0.0792, 0.4043, 0.0506, ..., 0.1635, 0.6350, 0.0332]),
size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.4326, 0.5030, 0.1276, ..., 0.3674, 0.6623, 0.2020])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 10000000
Density: 0.4
Time: 11.241667032241821 seconds
[18.06, 17.64, 17.57, 17.56, 18.11, 17.49, 17.52, 17.53, 17.66, 17.49]
[83.67]
16.64910578727722
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2943, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.241667032241821, 'TIME_S_1KI': 3.819798515882372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1393.030681221485, 'W': 83.67}
[18.06, 17.64, 17.57, 17.56, 18.11, 17.49, 17.52, 17.53, 17.66, 17.49, 17.99, 17.7, 17.78, 17.54, 17.51, 17.38, 17.97, 17.59, 17.4, 17.64]
317.53999999999996
15.876999999999999
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2943, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.241667032241821, 'TIME_S_1KI': 3.819798515882372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1393.030681221485, 'W': 83.67, 'J_1KI': 473.3369626984319, 'W_1KI': 28.430173292558617, 'W_D': 67.793, 'J_D': 1128.6928286368848, 'W_D_1KI': 23.035338090383963, 'J_D_1KI': 7.82716211022221}

1
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
View File

@ -1 +0,0 @@
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2293, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.488942384719849, "TIME_S_1KI": 4.5743316113039025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1464.8316407203674, "W": 79.5, "J_1KI": 638.827579904216, "W_1KI": 34.67073702573049, "W_D": 63.46325, "J_D": 1169.3456178987026, "W_D_1KI": 27.676951591801135, "J_D_1KI": 12.0701925825561}

85
pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
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@ -1,85 +0,0 @@
['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', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.49056053161621094}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2534, 5049, ..., 12494993,
12497525, 12500000]),
col_indices=tensor([ 2, 3, 7, ..., 4995, 4997, 4999]),
values=tensor([0.8256, 0.5002, 0.0945, ..., 0.8539, 0.0059, 0.0754]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.6389, 0.8699, 0.2648, ..., 0.6441, 0.5427, 0.0050])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 0.49056053161621094 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', '2140', '-ss', '5000', '-sd', '0.5', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.796798944473267}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2473, 4961, ..., 12495026,
12497501, 12500000]),
col_indices=tensor([ 0, 1, 2, ..., 4993, 4995, 4998]),
values=tensor([0.3292, 0.8142, 0.0983, ..., 0.8374, 0.1805, 0.7414]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.5256, 0.8051, 0.2947, ..., 0.7660, 0.3711, 0.2787])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 9.796798944473267 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', '2293', '-ss', '5000', '-sd', '0.5', '-c', '16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.488942384719849}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2483, 4907, ..., 12494946,
12497460, 12500000]),
col_indices=tensor([ 3, 6, 8, ..., 4993, 4997, 4999]),
values=tensor([0.9265, 0.6407, 0.1426, ..., 0.0639, 0.3199, 0.5517]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.6629, 0.1553, 0.8462, ..., 0.7358, 0.1133, 0.5218])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 10.488942384719849 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, 2483, 4907, ..., 12494946,
12497460, 12500000]),
col_indices=tensor([ 3, 6, 8, ..., 4993, 4997, 4999]),
values=tensor([0.9265, 0.6407, 0.1426, ..., 0.0639, 0.3199, 0.5517]),
size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
tensor([0.6629, 0.1553, 0.8462, ..., 0.7358, 0.1133, 0.5218])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([5000, 5000])
Rows: 5000
Size: 25000000
NNZ: 12500000
Density: 0.5
Time: 10.488942384719849 seconds
[18.25, 17.84, 17.64, 17.31, 17.75, 17.54, 17.53, 17.59, 17.7, 17.45]
[79.5]
18.42555522918701
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2293, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.488942384719849, 'TIME_S_1KI': 4.5743316113039025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1464.8316407203674, 'W': 79.5}
[18.25, 17.84, 17.64, 17.31, 17.75, 17.54, 17.53, 17.59, 17.7, 17.45, 18.03, 17.47, 17.56, 17.58, 17.59, 17.37, 21.25, 17.8, 17.44, 17.82]
320.735
16.03675
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2293, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.488942384719849, 'TIME_S_1KI': 4.5743316113039025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1464.8316407203674, 'W': 79.5, 'J_1KI': 638.827579904216, 'W_1KI': 34.67073702573049, 'W_D': 63.46325, 'J_D': 1169.3456178987026, 'W_D_1KI': 27.676951591801135, 'J_D_1KI': 12.0701925825561}

2
pytorch/synthetic_densities
View File

@ -7,5 +7,3 @@
0.1
0.2
0.3
0.4
0.5