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cephi 2024-12-14 15:57:07 -05:00
parent 644a6c9954
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{"CPU": "Altra", "ITERATIONS": 64501, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999950, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.937983274459839, "TIME_S_1KI": 0.1695785069139988, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 567.2887351226807, "W": 49.70430804561819, "J_1KI": 8.795037830772868, "W_1KI": 0.7705974798160989, "W_D": 31.653308045618193, "J_D": 361.2677811985016, "W_D_1KI": 0.49074135355449056, "J_D_1KI": 0.0076082751206103865}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 9, 18, ..., 999932, 999938,
999950]),
col_indices=tensor([10110, 12605, 40786, ..., 88927, 91184, 94573]),
values=tensor([-0.7796, -0.9685, -0.0839, ..., 1.2711, 1.5132,
0.5683]), size=(100000, 100000), nnz=999950,
layout=torch.sparse_csr)
tensor([0.0946, 0.2595, 0.2568, ..., 0.3660, 0.5904, 0.0609])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 999950
Density: 9.9995e-05
Time: 10.937983274459839 seconds

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pytorch/output_16core/altra_10_10_10_100000_1e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 197739, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.723783731460571, "TIME_S_1KI": 0.054232011547851317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 372.3051293182373, "W": 34.472008609034624, "J_1KI": 1.8828108229445748, "W_1KI": 0.17433085334220677, "W_D": 16.914008609034624, "J_D": 182.67494168663018, "W_D_1KI": 0.08553703927416759, "J_D_1KI": 0.0004325754619683906}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 2, ..., 99996, 99997,
100000]),
col_indices=tensor([12284, 56642, 64630, ..., 1486, 59934, 63229]),
values=tensor([ 0.6311, 0.7508, -2.1343, ..., -0.1643, 1.0472,
-3.0903]), size=(100000, 100000), nnz=100000,
layout=torch.sparse_csr)
tensor([0.1984, 0.2622, 0.0158, ..., 0.0124, 0.6459, 0.0911])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.723783731460571 seconds

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pytorch/output_16core/altra_10_10_10_100000_2e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 144710, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 199999, "MATRIX_DENSITY": 1.99999e-05, "TIME_S": 10.811187505722046, "TIME_S_1KI": 0.07470933249756095, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 419.1660452079773, "W": 40.553841840438906, "J_1KI": 2.8965934987767072, "W_1KI": 0.28024215216943477, "W_D": 23.028841840438908, "J_D": 238.02698146224026, "W_D_1KI": 0.1591378746488764, "J_D_1KI": 0.0010997019877608764}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 6, ..., 199997, 199999,
199999]),
col_indices=tensor([87835, 91834, 24596, ..., 49690, 54987, 72343]),
values=tensor([-1.1614e+00, -2.8700e-01, 1.5706e-03, ...,
-1.8033e+00, 1.7581e+00, 1.0572e+00]),
size=(100000, 100000), nnz=199999, layout=torch.sparse_csr)
tensor([0.5278, 0.3226, 0.2774, ..., 0.7173, 0.7485, 0.1274])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 199999
Density: 1.99999e-05
Time: 10.811187505722046 seconds

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pytorch/output_16core/altra_10_10_10_100000_5e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 94737, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499994, "MATRIX_DENSITY": 4.99994e-05, "TIME_S": 10.580386400222778, "TIME_S_1KI": 0.11168167031067881, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 443.9495148086548, "W": 42.52685400360466, "J_1KI": 4.68612595721476, "W_1KI": 0.4488938218816794, "W_D": 24.877854003604664, "J_D": 259.7067540769577, "W_D_1KI": 0.2625991323728286, "J_D_1KI": 0.002771875110810228}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 9, ..., 499986, 499989,
499994]),
col_indices=tensor([14307, 59019, 64362, ..., 53395, 62334, 74450]),
values=tensor([ 0.6303, -1.3333, -0.6531, ..., -0.2267, 0.7450,
0.1175]), size=(100000, 100000), nnz=499994,
layout=torch.sparse_csr)
tensor([0.6980, 0.0737, 0.9763, ..., 0.2302, 0.7366, 0.2229])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499994
Density: 4.99994e-05
Time: 10.580386400222778 seconds

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pytorch/output_16core/altra_10_10_10_100000_8e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 71869, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 799970, "MATRIX_DENSITY": 7.9997e-05, "TIME_S": 11.103689193725586, "TIME_S_1KI": 0.15449900782987916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.19529326438897, "W": 48.64164968545682, "J_1KI": 7.043305086537853, "W_1KI": 0.6768098858403041, "W_D": 30.76664968545682, "J_D": 320.1769134271144, "W_D_1KI": 0.4280934712526516, "J_D_1KI": 0.005956580323263878}

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pytorch/output_16core/altra_10_10_10_100000_8e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 11, ..., 799957, 799963,
799970]),
col_indices=tensor([ 9707, 28118, 30919, ..., 82276, 92061, 92663]),
values=tensor([ 1.2052, -1.5644, -1.5667, ..., 0.5565, -0.5405,
-0.2631]), size=(100000, 100000), nnz=799970,
layout=torch.sparse_csr)
tensor([0.5347, 0.9755, 0.4755, ..., 0.3062, 0.5404, 0.3654])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 799970
Density: 7.9997e-05
Time: 11.103689193725586 seconds

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pytorch/output_16core/altra_10_10_10_10000_0.0001.json Normal file
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{"CPU": "Altra", "ITERATIONS": 745672, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.792332410812378, "TIME_S_1KI": 0.01447329712100277, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 441.4402393722534, "W": 39.33484127375969, "J_1KI": 0.5920032391886156, "W_1KI": 0.052750862676565154, "W_D": 21.838841273759694, "J_D": 245.0891628723145, "W_D_1KI": 0.029287463219431188, "J_D_1KI": 3.927660314378331e-05}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 2, ..., 9997, 10000, 10000]),
col_indices=tensor([ 208, 3210, 3280, ..., 2267, 6277, 9749]),
values=tensor([-0.1715, -0.0528, -1.1977, ..., 0.8885, 1.1928,
0.4887]), size=(10000, 10000), nnz=10000,
layout=torch.sparse_csr)
tensor([0.4199, 0.9096, 0.7063, ..., 0.1703, 0.6234, 0.8225])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 10.792332410812378 seconds

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pytorch/output_16core/altra_10_10_10_10000_1e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 746211, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.773172378540039, "TIME_S_1KI": 0.014437166402719926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 373.8277773666382, "W": 37.833531501635065, "J_1KI": 0.5009679264532929, "W_1KI": 0.050700849359812526, "W_D": 20.80053150163506, "J_D": 205.5271118152141, "W_D_1KI": 0.027874865824324566, "J_D_1KI": 3.735520626783117e-05}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 999, 999, 1000]),
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5.7585e-01, -1.1490e+00, 1.4799e+00, 8.8461e-01,
6.0465e-01, -2.0673e-01, -4.9836e-01, -2.2241e-01,
-6.6821e-01, 1.1181e+00, 4.1180e-01, -9.3116e-02,
9.6447e-01, 8.2906e-01, -1.4702e+00, 6.0443e-01,
-6.4991e-01, 7.6962e-01, 5.0307e-01, 2.5799e-01,
1.1587e-01, -1.4561e+00, -4.9434e-01, -1.5741e+00,
3.8481e-01, -2.0851e+00, -5.7887e-01, 7.2650e-01,
1.2102e+00, -3.4302e-01, -9.5161e-01, -9.8638e-01,
2.1792e-01, -4.9377e-01, 5.6926e-01, 4.2480e-02,
1.7975e+00, 1.8717e-01, 1.2940e+00, 2.1273e-01,
9.8182e-01, -1.5887e-02, -3.3377e-01, -7.9186e-01,
1.8293e+00, -1.5921e+00, -5.1951e-01, 2.2561e-01,
1.0687e+00, 9.9938e-01, 1.9822e+00, -9.5610e-01,
-5.6567e-01, -2.9933e+00, 8.7872e-01, -1.4525e+00,
1.1245e-01, -1.5902e+00, 1.6391e+00, 1.7929e-01,
1.0651e-01, 7.6336e-01, 1.0244e-01, -6.6332e-01,
1.2278e+00, -3.1900e-01, -1.7483e-01, -9.2148e-01,
1.6418e+00, -9.3906e-01, 9.2628e-01, 6.5782e-01,
1.5584e-01, 4.7491e-01, -7.8563e-01, 2.6464e-01,
-1.7181e+00, -1.0182e+00, -5.2716e-01, -9.9400e-01,
-1.2774e+00, 2.8527e-01, 1.6023e+00, 1.0741e-01,
-1.4114e+00, -9.0080e-02, -3.7297e-01, -1.7671e+00,
-2.4728e-01, -7.0179e-01, 3.1971e-01, -5.7137e-01,
-6.7749e-01, 1.1123e+00, -4.3141e-02, -1.1673e+00,
3.0165e-01, 3.4161e-01, 2.5661e-01, -8.0741e-01]),
size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.9444, 0.3415, 0.7652, ..., 0.4554, 0.6701, 0.1187])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 10.773172378540039 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 757288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 2000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.095738172531128, "TIME_S_1KI": 0.013331438201227444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.73304656982424, "W": 34.69019436640105, "J_1KI": 0.463143541915129, "W_1KI": 0.045808456447746504, "W_D": 17.258194366401053, "J_D": 174.48789777565008, "W_D_1KI": 0.022789472917042197, "J_D_1KI": 3.0093534978822057e-05}

16
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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 1999, 1999, 2000]),
col_indices=tensor([4621, 3443, 1190, ..., 5062, 8400, 6357]),
values=tensor([-0.6728, -0.4536, -1.5291, ..., -1.1841, 0.2333,
0.5602]), size=(10000, 10000), nnz=2000,
layout=torch.sparse_csr)
tensor([0.1655, 0.4850, 0.2231, ..., 0.6018, 0.5416, 0.6623])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 2000
Density: 2e-05
Time: 10.095738172531128 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 759365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.492435455322266, "TIME_S_1KI": 0.013817380910790286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 382.43073294639584, "W": 36.54782584182964, "J_1KI": 0.503619119851976, "W_1KI": 0.04812945795741131, "W_D": 19.20582584182964, "J_D": 200.96675751161567, "W_D_1KI": 0.0252919555705486, "J_D_1KI": 3.330671754762018e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 4998, 4999, 5000]),
col_indices=tensor([7940, 1966, 9278, ..., 5521, 4680, 2043]),
values=tensor([-0.5302, -0.9728, 0.9241, ..., 0.4617, 1.3190,
-0.4573]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.3509, 0.1998, 0.1131, ..., 0.8293, 0.9176, 0.2351])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 10.492435455322266 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 705493, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.68189811706543, "TIME_S_1KI": 0.015141040544789855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 367.0330882072449, "W": 38.23569326717513, "J_1KI": 0.520250503133617, "W_1KI": 0.0541971263601129, "W_D": 20.893693267175127, "J_D": 200.56329854726792, "W_D_1KI": 0.029615734340631483, "J_D_1KI": 4.197877844377121e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 7996, 7997, 8000]),
col_indices=tensor([6513, 2704, 2290, ..., 7941, 7991, 9199]),
values=tensor([-2.1062, 0.2411, -0.6009, ..., -0.4068, 0.8394,
-1.3141]), size=(10000, 10000), nnz=8000,
layout=torch.sparse_csr)
tensor([0.6508, 0.6178, 0.0529, ..., 0.1197, 0.0890, 0.8968])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 8000
Density: 8e-05
Time: 10.68189811706543 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 28667, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 2249892, "MATRIX_DENSITY": 9.99952e-05, "TIME_S": 10.37232518196106, "TIME_S_1KI": 0.3618210898231785, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 519.7199160766602, "W": 50.88957099176079, "J_1KI": 18.129553705538083, "W_1KI": 1.775196950910831, "W_D": 33.03557099176079, "J_D": 337.38237223815923, "W_D_1KI": 1.1523902393609653, "J_D_1KI": 0.04019919208012576}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 15, 28, ..., 2249858,
2249875, 2249892]),
col_indices=tensor([ 14880, 20962, 21272, ..., 117708, 127946,
149457]),
values=tensor([ 1.3883, -0.8537, 0.3620, ..., -0.4221, 1.7007,
-0.2153]), size=(150000, 150000), nnz=2249892,
layout=torch.sparse_csr)
tensor([0.0054, 0.0896, 0.3223, ..., 0.7227, 0.0527, 0.4309])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 2249892
Density: 9.99952e-05
Time: 10.37232518196106 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 100980, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 224999, "MATRIX_DENSITY": 9.999955555555555e-06, "TIME_S": 10.421836137771606, "TIME_S_1KI": 0.10320693343010108, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 437.94505512237555, "W": 40.87157984460665, "J_1KI": 4.336948456351511, "W_1KI": 0.4047492557398163, "W_D": 22.93957984460665, "J_D": 245.80100886058815, "W_D_1KI": 0.22716953698362696, "J_D_1KI": 0.002249648811483729}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 224998, 224999,
224999]),
col_indices=tensor([ 54569, 101050, 20988, ..., 82049, 110775,
48410]),
values=tensor([-0.8716, 0.8544, -1.2720, ..., 0.2452, 0.7228,
0.1621]), size=(150000, 150000), nnz=224999,
layout=torch.sparse_csr)
tensor([0.2422, 0.3715, 0.5419, ..., 0.6661, 0.8156, 0.9122])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 224999
Density: 9.999955555555555e-06
Time: 10.421836137771606 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 72827, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 449998, "MATRIX_DENSITY": 1.999991111111111e-05, "TIME_S": 10.586020708084106, "TIME_S_1KI": 0.1453584619452141, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 442.4028211212158, "W": 43.31909206142047, "J_1KI": 6.074708845911761, "W_1KI": 0.5948218663602849, "W_D": 25.733092061420468, "J_D": 262.80311941909787, "W_D_1KI": 0.3533454908402168, "J_D_1KI": 0.004851847403301205}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 449993, 449994,
449998]),
col_indices=tensor([ 66827, 13180, 62321, ..., 29562, 45238,
134599]),
values=tensor([ 0.1032, 0.0320, 1.2243, ..., 0.9533, -1.1891,
1.2580]), size=(150000, 150000), nnz=449998,
layout=torch.sparse_csr)
tensor([0.3606, 0.8590, 0.6142, ..., 0.3221, 0.1513, 0.9214])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 449998
Density: 1.999991111111111e-05
Time: 10.586020708084106 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 42029, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1124964, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.425989151000977, "TIME_S_1KI": 0.24806655288017743, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 544.9276902675628, "W": 49.506354437696494, "J_1KI": 12.965516435498412, "W_1KI": 1.1779094063074662, "W_D": 31.889354437696497, "J_D": 351.01336899542804, "W_D_1KI": 0.7587464473981417, "J_D_1KI": 0.01805292648880872}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 8, 16, ..., 1124945,
1124952, 1124964]),
col_indices=tensor([ 21251, 34723, 46174, ..., 81420, 118474,
136795]),
values=tensor([ 0.3066, 0.1728, 0.0401, ..., -0.7916, 0.6291,
2.5231]), size=(150000, 150000), nnz=1124964,
layout=torch.sparse_csr)
tensor([0.3979, 0.8931, 0.8055, ..., 0.6413, 0.6433, 0.6850])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 1124964
Density: 4.99984e-05
Time: 10.425989151000977 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 33948, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1799946, "MATRIX_DENSITY": 7.99976e-05, "TIME_S": 10.878687620162964, "TIME_S_1KI": 0.32045150289156843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 536.6147994422912, "W": 51.36202170411531, "J_1KI": 15.80696357494672, "W_1KI": 1.512961638509347, "W_D": 33.50802170411531, "J_D": 350.08163132762905, "W_D_1KI": 0.9870396401589285, "J_D_1KI": 0.02907504536817864}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 10, 21, ..., 1799926,
1799935, 1799946]),
col_indices=tensor([ 4632, 7112, 14212, ..., 124633, 140187,
147683]),
values=tensor([-1.8172, 0.7734, 0.9656, ..., 0.4938, 0.9281,
0.7013]), size=(150000, 150000), nnz=1799946,
layout=torch.sparse_csr)
tensor([0.2540, 0.9127, 0.1735, ..., 0.5861, 0.6699, 0.8095])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 1799946
Density: 7.99976e-05
Time: 10.878687620162964 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 9139, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3999800, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.467170715332031, "TIME_S_1KI": 1.1453299830760513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 498.041587638855, "W": 46.344394921195324, "J_1KI": 54.49628927003556, "W_1KI": 5.0710575469083405, "W_D": 29.150394921195325, "J_D": 313.26569246482853, "W_D_1KI": 3.189670086573512, "J_D_1KI": 0.3490174074377407}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 22, 47, ..., 3999759,
3999781, 3999800]),
col_indices=tensor([ 1299, 3744, 6987, ..., 188420, 195819,
198823]),
values=tensor([-0.2786, -0.1175, -0.4360, ..., -1.3931, 0.0258,
-0.4577]), size=(200000, 200000), nnz=3999800,
layout=torch.sparse_csr)
tensor([0.2287, 0.5622, 0.5229, ..., 0.2820, 0.0596, 0.0899])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 3999800
Density: 9.9995e-05
Time: 10.467170715332031 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 58540, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 399997, "MATRIX_DENSITY": 9.999925e-06, "TIME_S": 10.480172872543335, "TIME_S_1KI": 0.17902584339841704, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 440.6894346141815, "W": 43.66832447332347, "J_1KI": 7.5280053743454305, "W_1KI": 0.745957028925922, "W_D": 26.138324473323472, "J_D": 263.78120921373363, "W_D_1KI": 0.44650366370555983, "J_D_1KI": 0.007627325994286981}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 7, ..., 399991, 399996,
399997]),
col_indices=tensor([160082, 160307, 162363, ..., 190956, 198013,
161884]),
values=tensor([-0.6975, 1.2342, 1.1846, ..., 0.1218, 1.0676,
0.7007]), size=(200000, 200000), nnz=399997,
layout=torch.sparse_csr)
tensor([0.5762, 0.6340, 0.7876, ..., 0.1377, 0.2839, 0.7957])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 399997
Density: 9.999925e-06
Time: 10.480172872543335 seconds

1
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{"CPU": "Altra", "ITERATIONS": 44540, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 799989, "MATRIX_DENSITY": 1.9999725e-05, "TIME_S": 10.084713459014893, "TIME_S_1KI": 0.226419251437245, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 495.6630394935608, "W": 45.56414063287463, "J_1KI": 11.12849213052449, "W_1KI": 1.0229937277250702, "W_D": 28.07714063287463, "J_D": 305.4331909496784, "W_D_1KI": 0.6303803464947155, "J_D_1KI": 0.014153128569706231}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 8, ..., 799983, 799985,
799989]),
col_indices=tensor([ 27664, 33238, 44014, ..., 124946, 138710,
170817]),
values=tensor([-0.8725, -0.7108, 0.7697, ..., -1.4556, 1.0518,
0.8270]), size=(200000, 200000), nnz=799989,
layout=torch.sparse_csr)
tensor([0.0240, 0.7234, 0.7805, ..., 0.9779, 0.3691, 0.0024])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 799989
Density: 1.9999725e-05
Time: 10.084713459014893 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 24847, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 1999945, "MATRIX_DENSITY": 4.9998625e-05, "TIME_S": 10.208934545516968, "TIME_S_1KI": 0.4108719179585853, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 502.7416627597809, "W": 49.780366353890884, "J_1KI": 20.23349550286879, "W_1KI": 2.003475926827822, "W_D": 32.31436635389089, "J_D": 326.349110335827, "W_D_1KI": 1.3005339217567873, "J_D_1KI": 0.05234168800083661}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 19, ..., 1999921,
1999935, 1999945]),
col_indices=tensor([ 3824, 96425, 103457, ..., 173104, 181406,
194614]),
values=tensor([ 0.0349, -0.0525, -0.3643, ..., 1.0650, 0.0714,
0.7456]), size=(200000, 200000), nnz=1999945,
layout=torch.sparse_csr)
tensor([0.4906, 0.6274, 0.6737, ..., 0.6970, 0.2171, 0.8266])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 1999945
Density: 4.9998625e-05
Time: 10.208934545516968 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 13587, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3199867, "MATRIX_DENSITY": 7.9996675e-05, "TIME_S": 10.251005172729492, "TIME_S_1KI": 0.7544715664038781, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 476.3702043151855, "W": 47.48744469039588, "J_1KI": 35.06073484324616, "W_1KI": 3.4950647450059527, "W_D": 28.960444690395885, "J_D": 290.51664169692987, "W_D_1KI": 2.13148190847103, "J_D_1KI": 0.15687656645845513}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If 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, 18, 36, ..., 3199838,
3199856, 3199867]),
col_indices=tensor([ 14010, 33286, 34826, ..., 171960, 177566,
181420]),
values=tensor([ 0.4827, 1.1570, -0.1634, ..., -1.2927, -0.3453,
-0.1386]), size=(200000, 200000), nnz=3199867,
layout=torch.sparse_csr)
tensor([0.5598, 0.1350, 0.8221, ..., 0.1901, 0.1582, 0.7521])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 3199867
Density: 7.9996675e-05
Time: 10.251005172729492 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 567731, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.283060550689697, "TIME_S_1KI": 0.018112557797072375, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.2048466873169, "W": 33.56132319147934, "J_1KI": 0.6256569514212134, "W_1KI": 0.05911483289001189, "W_D": 16.472323191479337, "J_D": 174.3390449903011, "W_D_1KI": 0.029014309931075344, "J_D_1KI": 5.110573481292257e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 3, ..., 39994, 39995, 39998]),
col_indices=tensor([ 1674, 5478, 7295, ..., 5282, 9148, 19746]),
values=tensor([ 1.0454, -0.2042, -0.9908, ..., -0.4352, 1.3976,
-0.2792]), size=(20000, 20000), nnz=39998,
layout=torch.sparse_csr)
tensor([0.5891, 0.6809, 0.8267, ..., 0.3289, 0.5314, 0.8509])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39998
Density: 9.9995e-05
Time: 10.283060550689697 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 742526, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.885217666625977, "TIME_S_1KI": 0.014659712476904481, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 385.8624301719666, "W": 35.598855722519104, "J_1KI": 0.519661843722599, "W_1KI": 0.0479429080227751, "W_D": 18.531855722519108, "J_D": 200.87013302969942, "W_D_1KI": 0.024957854300750554, "J_D_1KI": 3.361209479634458e-05}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 2, ..., 4000, 4000, 4000]),
col_indices=tensor([ 7067, 11731, 3926, ..., 19119, 620, 6588]),
values=tensor([ 1.0868, -0.8958, -0.4960, ..., 1.4149, 1.0552,
-0.7229]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.3996, 0.2483, 0.8086, ..., 0.3640, 0.6840, 0.9715])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.885217666625977 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 725562, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.872642755508423, "TIME_S_1KI": 0.014985132566904584, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 450.6815788841247, "W": 39.35287649094676, "J_1KI": 0.6211482669766673, "W_1KI": 0.054237786007187205, "W_D": 22.100876490946764, "J_D": 253.10622246265407, "W_D_1KI": 0.030460355546385785, "J_D_1KI": 4.198174042519562e-05}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 7999, 8000, 8000]),
col_indices=tensor([16956, 3068, 14321, ..., 16428, 14864, 8672]),
values=tensor([ 0.6797, 0.5462, -0.0183, ..., -0.4996, 1.2405,
3.8311]), size=(20000, 20000), nnz=8000,
layout=torch.sparse_csr)
tensor([0.9619, 0.9404, 0.0252, ..., 0.6566, 0.1906, 0.7994])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 8000
Density: 2e-05
Time: 10.872642755508423 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 639751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.804665803909302, "TIME_S_1KI": 0.01688886114114601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 374.2699551010132, "W": 36.07346397818204, "J_1KI": 0.585024415907147, "W_1KI": 0.0563867254262706, "W_D": 18.95946397818204, "J_D": 196.70852059412005, "W_D_1KI": 0.029635692602562623, "J_D_1KI": 4.632379254203999e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 19997, 19998, 19999]),
col_indices=tensor([ 8271, 7022, 133, ..., 16237, 2122, 4630]),
values=tensor([ 1.1453, -0.0767, 0.5090, ..., -0.5285, 0.4972,
1.3006]), size=(20000, 20000), nnz=19999,
layout=torch.sparse_csr)
tensor([0.8942, 0.9806, 0.8705, ..., 0.0714, 0.6754, 0.5950])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 19999
Density: 4.99975e-05
Time: 10.804665803909302 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 606932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 31998, "MATRIX_DENSITY": 7.9995e-05, "TIME_S": 10.414053440093994, "TIME_S_1KI": 0.017158517659464315, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.62409350395205, "W": 34.2116997670769, "J_1KI": 0.5826420315685316, "W_1KI": 0.056368258333844484, "W_D": 16.485699767076902, "J_D": 170.40195826578145, "W_D_1KI": 0.02716235058800146, "J_D_1KI": 4.4753531842119806e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 4, ..., 31996, 31997, 31998]),
col_indices=tensor([12913, 3728, 7950, ..., 936, 12780, 2993]),
values=tensor([ 1.4614, -2.3542, -0.3627, ..., -1.4645, 0.4434,
-0.9357]), size=(20000, 20000), nnz=31998,
layout=torch.sparse_csr)
tensor([0.0649, 0.7612, 0.4354, ..., 0.9877, 0.3768, 0.8444])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 31998
Density: 7.9995e-05
Time: 10.414053440093994 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 227433, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249985, "MATRIX_DENSITY": 9.9994e-05, "TIME_S": 10.572213649749756, "TIME_S_1KI": 0.046484958865906686, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 509.1497803020477, "W": 43.42214042283746, "J_1KI": 2.238680315970188, "W_1KI": 0.19092277911665176, "W_D": 26.133140422837457, "J_D": 306.4262280790805, "W_D_1KI": 0.1149047870046891, "J_D_1KI": 0.00050522477830697}

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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 13, ..., 249980, 249982,
249985]),
col_indices=tensor([ 1700, 11150, 13215, ..., 20054, 23403, 31752]),
values=tensor([ 1.2136, -0.7249, 0.7742, ..., -0.8923, -0.7123,
0.2359]), size=(50000, 50000), nnz=249985,
layout=torch.sparse_csr)
tensor([0.8666, 0.4330, 0.6051, ..., 0.7413, 0.9082, 0.0999])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249985
Density: 9.9994e-05
Time: 10.572213649749756 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 482951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677425146102905, "TIME_S_1KI": 0.02210871319471935, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 379.57143712997436, "W": 34.71486620028037, "J_1KI": 0.7859419219133501, "W_1KI": 0.07188072123316935, "W_D": 17.25286620028037, "J_D": 188.64238682270047, "W_D_1KI": 0.035723844034447325, "J_D_1KI": 7.396991420340225e-05}

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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 3, ..., 25000, 25000, 25000]),
col_indices=tensor([ 1673, 3858, 25004, ..., 30267, 19651, 6711]),
values=tensor([-0.6027, -0.6575, -0.2557, ..., -1.5398, 0.1707,
-0.6771]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.1612, 0.8476, 0.5546, ..., 0.2757, 0.8411, 0.1929])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 10.677425146102905 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 375882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 50000, "MATRIX_DENSITY": 2e-05, "TIME_S": 11.363938331604004, "TIME_S_1KI": 0.03023272817427811, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.57937900543214, "W": 34.8864729090874, "J_1KI": 0.9140618039848467, "W_1KI": 0.09281229989488031, "W_D": 17.3774729090874, "J_D": 171.14201731848715, "W_D_1KI": 0.04623119199399652, "J_D_1KI": 0.00012299389700490184}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 50000, 50000, 50000]),
col_indices=tensor([20193, 2603, 22907, ..., 39448, 46130, 37169]),
values=tensor([-0.0912, 0.5738, -1.6784, ..., 0.2528, -1.8292,
0.9168]), size=(50000, 50000), nnz=50000,
layout=torch.sparse_csr)
tensor([0.8671, 0.8813, 0.4085, ..., 0.9714, 0.5561, 0.6401])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 50000
Density: 2e-05
Time: 11.363938331604004 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 290131, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124996, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.922548770904541, "TIME_S_1KI": 0.0376469552405794, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 375.18905561447144, "W": 34.68174199802756, "J_1KI": 1.2931712075389097, "W_1KI": 0.11953821548896036, "W_D": 17.293741998027564, "J_D": 187.0846835966111, "W_D_1KI": 0.05960666732623389, "J_D_1KI": 0.00020544742659775716}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 3, ..., 124994, 124994,
124996]),
col_indices=tensor([13998, 32736, 41097, ..., 22742, 7968, 9054]),
values=tensor([0.6512, 0.4983, 2.0227, ..., 0.2925, 0.1402, 1.7352]),
size=(50000, 50000), nnz=124996, layout=torch.sparse_csr)
tensor([0.2986, 0.6337, 0.9352, ..., 0.9360, 0.6645, 0.9075])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124996
Density: 4.99984e-05
Time: 10.922548770904541 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 242304, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 199986, "MATRIX_DENSITY": 7.99944e-05, "TIME_S": 10.34809398651123, "TIME_S_1KI": 0.042707070401277865, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 370.049839553833, "W": 35.47238359008601, "J_1KI": 1.527213085850143, "W_1KI": 0.14639619482173638, "W_D": 17.97238359008601, "J_D": 187.48888545989993, "W_D_1KI": 0.0741728720536434, "J_D_1KI": 0.0003061149302266714}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 10, ..., 199981, 199982,
199986]),
col_indices=tensor([ 1840, 12534, 16220, ..., 13556, 41718, 49099]),
values=tensor([ 0.3522, -0.6832, -0.4871, ..., 0.5234, -0.4343,
1.6842]), size=(50000, 50000), nnz=199986,
layout=torch.sparse_csr)
tensor([0.5177, 0.9782, 0.1581, ..., 0.4536, 0.4967, 0.9151])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 199986
Density: 7.99944e-05
Time: 10.34809398651123 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 97450, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999942, "MATRIX_DENSITY": 9.99942e-05, "TIME_S": 10.576393604278564, "TIME_S_1KI": 0.10853148901260712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1525.2442362952233, "W": 146.27, "J_1KI": 15.651557068191105, "W_1KI": 1.500974858902001, "W_D": 110.24725000000001, "J_D": 1149.613609283507, "W_D_1KI": 1.1313211903540277, "J_D_1KI": 0.01160924772041075}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 11, 18, ..., 999917, 999929,
999942]),
col_indices=tensor([ 1892, 9410, 16896, ..., 95042, 98559, 99925]),
values=tensor([ 6.8886e-04, 2.2999e+00, -7.2309e-01, ...,
1.2011e-01, -3.2614e-01, 2.3038e+00]),
size=(100000, 100000), nnz=999942, layout=torch.sparse_csr)
tensor([0.1643, 0.6304, 0.0977, ..., 0.5343, 0.9179, 0.3813])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 999942
Density: 9.99942e-05
Time: 10.576393604278564 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 149990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.76275634765625, "TIME_S_1KI": 0.07175649275055837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1218.5091412353515, "W": 117.11999999999999, "J_1KI": 8.123935870627053, "W_1KI": 0.7808520568037869, "W_D": 81.863, "J_D": 851.6975224466324, "W_D_1KI": 0.545789719314621, "J_D_1KI": 0.0036388407181453496}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 7, ..., 100000, 100000,
100000]),
col_indices=tensor([ 6887, 13446, 39563, ..., 66177, 81567, 22565]),
values=tensor([ 1.5183, 0.2136, -1.3397, ..., -0.8325, 0.6394,
-0.8776]), size=(100000, 100000), nnz=100000,
layout=torch.sparse_csr)
tensor([0.4787, 0.6498, 0.7941, ..., 0.9578, 0.9373, 0.9290])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.76275634765625 seconds

1
pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 124803, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 199997, "MATRIX_DENSITY": 1.99997e-05, "TIME_S": 10.25207233428955, "TIME_S_1KI": 0.08214604083467185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1312.704617705345, "W": 126.02, "J_1KI": 10.518213646349407, "W_1KI": 1.0097513681562142, "W_D": 90.04275, "J_D": 937.9426576407551, "W_D_1KI": 0.7214790509843513, "J_D_1KI": 0.0057809431743175346}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 5, ..., 199993, 199995,
199997]),
col_indices=tensor([15473, 40399, 66570, ..., 49105, 11119, 13122]),
values=tensor([-0.2163, 1.4513, 0.2678, ..., -1.0774, 1.7715,
-0.9998]), size=(100000, 100000), nnz=199997,
layout=torch.sparse_csr)
tensor([0.4636, 0.9972, 0.6979, ..., 0.4218, 0.8637, 0.4224])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 199997
Density: 1.99997e-05
Time: 10.25207233428955 seconds

1
pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 131904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499990, "MATRIX_DENSITY": 4.9999e-05, "TIME_S": 10.4083731174469, "TIME_S_1KI": 0.0789086996410033, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1520.4457294797899, "W": 147.62, "J_1KI": 11.52691146197075, "W_1KI": 1.1191472586123241, "W_D": 112.009, "J_D": 1153.6621441085338, "W_D_1KI": 0.849170608927705, "J_D_1KI": 0.006437792704752737}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 8, ..., 499980, 499982,
499990]),
col_indices=tensor([39304, 58830, 2565, ..., 78956, 94237, 96571]),
values=tensor([ 0.3416, -1.0238, -2.6750, ..., 0.9475, -1.7082,
1.1162]), size=(100000, 100000), nnz=499990,
layout=torch.sparse_csr)
tensor([0.2472, 0.9751, 0.5124, ..., 0.1633, 0.7065, 0.9039])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499990
Density: 4.9999e-05
Time: 10.4083731174469 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 111056, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 799965, "MATRIX_DENSITY": 7.99965e-05, "TIME_S": 10.777923822402954, "TIME_S_1KI": 0.09704945092928752, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1669.2134556055069, "W": 152.94, "J_1KI": 15.030376167028408, "W_1KI": 1.3771430629592278, "W_D": 117.34675, "J_D": 1280.7426054111122, "W_D_1KI": 1.0566448458435385, "J_D_1KI": 0.009514522815908538}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 10, ..., 799943, 799953,
799965]),
col_indices=tensor([22485, 40332, 73808, ..., 86131, 87836, 93692]),
values=tensor([ 0.5614, 0.8180, -0.1837, ..., -1.4660, -0.5348,
-0.9075]), size=(100000, 100000), nnz=799965,
layout=torch.sparse_csr)
tensor([0.7081, 0.2887, 0.0123, ..., 0.7844, 0.3920, 0.0991])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 799965
Density: 7.99965e-05
Time: 10.777923822402954 seconds

1
pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 395708, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9998, "MATRIX_DENSITY": 9.998e-05, "TIME_S": 17.949547052383423, "TIME_S_1KI": 0.04536058672653427, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.098508644104, "W": 98.08, "J_1KI": 2.651193578709816, "W_1KI": 0.24785953278680237, "W_D": 62.85875, "J_D": 672.3595114216208, "W_D_1KI": 0.15885134998534273, "J_D_1KI": 0.00040143578089233155}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9995, 9997, 9998]),
col_indices=tensor([2642, 4658, 4879, ..., 272, 3607, 1334]),
values=tensor([-0.2834, 1.1792, 0.0754, ..., 0.1847, 0.9832,
2.2685]), size=(10000, 10000), nnz=9998,
layout=torch.sparse_csr)
tensor([0.4716, 0.6890, 0.7469, ..., 0.5460, 0.8993, 0.3038])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 9998
Density: 9.998e-05
Time: 17.949547052383423 seconds

1
pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 506768, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.661507368087769, "TIME_S_1KI": 0.02103824110458389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1001.9527449011803, "W": 95.75, "J_1KI": 1.9771428837282155, "W_1KI": 0.18894247466296213, "W_D": 60.63199999999999, "J_D": 634.4689172725676, "W_D_1KI": 0.11964449215420071, "J_D_1KI": 0.00023609322639590643}

292
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@ -0,0 +1,292 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 1000, 1000, 1000]),
col_indices=tensor([4994, 5090, 9262, 1673, 3489, 8361, 3981, 7152, 7935,
9359, 1676, 6154, 3546, 3749, 9584, 6196, 9090, 9844,
2058, 1732, 685, 4661, 3525, 4505, 9726, 6717, 7573,
4278, 2888, 8180, 6068, 9146, 100, 5234, 6345, 96,
4151, 766, 4028, 1493, 1539, 54, 8661, 7198, 4599,
7599, 8751, 2928, 6104, 9619, 5323, 3978, 3624, 4124,
6084, 8986, 9232, 9211, 9966, 3077, 5972, 1065, 9588,
8145, 8053, 7626, 2008, 634, 3618, 7156, 6292, 8087,
3639, 2557, 6251, 2782, 9366, 9693, 520, 2077, 4250,
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978, 4900, 4521, 7772, 1755, 5686, 9321, 8466, 792,
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5020, 5116, 8177, 5730, 6528, 2924, 8891, 7839, 4517,
3755, 6338, 7132, 6324, 5881, 8542, 181, 4231, 2882,
7981, 1316, 6635, 2776, 3799, 3940, 2735, 5087, 8734,
6633, 6777, 3159, 8622, 5874, 6397, 1143, 9079, 9417,
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1503, 3041, 4813, 9213, 7, 6172, 8435, 9003, 5277,
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5312, 7952, 1844, 5395, 5440, 5550, 8450, 7498, 4967,
3147, 5240, 6212, 61, 3330, 6479, 1456, 8369, 9936,
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166, 6411, 2224, 113, 5944, 8115, 2034, 6935, 2671,
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6105, 6761, 4676, 1847, 8979, 1718, 4714, 5734, 6773,
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size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.0858, 0.7811, 0.9823, ..., 0.3526, 0.9436, 0.0176])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 10.661507368087769 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 463202, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 2000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.264344692230225, "TIME_S_1KI": 0.02215954311991361, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 985.5935904884338, "W": 96.19, "J_1KI": 2.1277835382585435, "W_1KI": 0.2076631793472394, "W_D": 60.73975, "J_D": 622.3589592251777, "W_D_1KI": 0.13113015487843316, "J_D_1KI": 0.0002830949669440831}

16
pytorch/output_16core/epyc_7313p_10_10_10_10000_2e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 1999, 1999, 2000]),
col_indices=tensor([8577, 5335, 9363, ..., 493, 9156, 619]),
values=tensor([-0.1709, -0.4161, -1.7005, ..., -1.2204, 0.3457,
0.2974]), size=(10000, 10000), nnz=2000,
layout=torch.sparse_csr)
tensor([0.4540, 0.9054, 0.6007, ..., 0.1570, 0.9375, 0.1255])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 2000
Density: 2e-05
Time: 10.264344692230225 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 421140, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.421851396560669, "TIME_S_1KI": 0.024746762113692998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 988.9332138633728, "W": 96.86, "J_1KI": 2.3482291253819936, "W_1KI": 0.22999477608396257, "W_D": 61.95675, "J_D": 632.5736929385662, "W_D_1KI": 0.1471167545234364, "J_D_1KI": 0.0003493298060584043}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 5000, 5000, 5000]),
col_indices=tensor([ 158, 2525, 1382, ..., 2114, 444, 4507]),
values=tensor([ 2.0070, 1.0507, 0.6574, ..., 0.5865, 1.2696,
-0.7873]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.2973, 0.1452, 0.7931, ..., 0.5079, 0.5321, 0.0471])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 10.421851396560669 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 391437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.263977527618408, "TIME_S_1KI": 0.026221275780313073, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 952.8949096679687, "W": 97.6, "J_1KI": 2.434350635397187, "W_1KI": 0.2493376967430263, "W_D": 61.95824999999999, "J_D": 604.9149696407318, "W_D_1KI": 0.15828409169291607, "J_D_1KI": 0.0004043667095673533}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 7998, 8000, 8000]),
col_indices=tensor([7114, 497, 7750, ..., 3944, 456, 7448]),
values=tensor([ 0.4943, 2.6522, -0.5457, ..., 0.8590, -0.1148,
2.1282]), size=(10000, 10000), nnz=8000,
layout=torch.sparse_csr)
tensor([0.4457, 0.6463, 0.4549, ..., 0.7645, 0.7943, 0.2305])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 8000
Density: 8e-05
Time: 10.263977527618408 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 49707, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 2249888, "MATRIX_DENSITY": 9.999502222222223e-05, "TIME_S": 10.143399477005005, "TIME_S_1KI": 0.20406380342818928, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1627.3860547471047, "W": 161.13, "J_1KI": 32.73957500446828, "W_1KI": 3.241595751101454, "W_D": 124.94174999999998, "J_D": 1261.8907813920378, "W_D_1KI": 2.5135644878990884, "J_D_1KI": 0.050567615987669505}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 15, 35, ..., 2249850,
2249868, 2249888]),
col_indices=tensor([ 12425, 19703, 20683, ..., 137244, 148113,
149331]),
values=tensor([-0.4641, -0.4936, -0.3021, ..., 1.3227, -0.6800,
-1.8534]), size=(150000, 150000), nnz=2249888,
layout=torch.sparse_csr)
tensor([0.1935, 0.6757, 0.3773, ..., 0.0853, 0.8759, 0.6154])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 2249888
Density: 9.999502222222223e-05
Time: 10.143399477005005 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 103207, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 224997, "MATRIX_DENSITY": 9.999866666666667e-06, "TIME_S": 10.442121028900146, "TIME_S_1KI": 0.10117648055752175, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1320.9493335485458, "W": 127.7, "J_1KI": 12.799028491754878, "W_1KI": 1.237319174087029, "W_D": 92.3135, "J_D": 954.9056875687838, "W_D_1KI": 0.8944499888573451, "J_D_1KI": 0.008666563206539723}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 3, ..., 224997, 224997,
224997]),
col_indices=tensor([ 17535, 49936, 15919, ..., 120467, 18748,
140421]),
values=tensor([ 0.2086, -0.2135, 0.8987, ..., 0.0070, -1.1373,
-1.3024]), size=(150000, 150000), nnz=224997,
layout=torch.sparse_csr)
tensor([0.9820, 0.9824, 0.4146, ..., 0.0878, 0.0275, 0.3574])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 224997
Density: 9.999866666666667e-06
Time: 10.442121028900146 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 80059, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 449996, "MATRIX_DENSITY": 1.9999822222222222e-05, "TIME_S": 11.024610042572021, "TIME_S_1KI": 0.137706067307511, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1478.8713603067397, "W": 137.29, "J_1KI": 18.47226870566382, "W_1KI": 1.7148602905357297, "W_D": 101.51749999999998, "J_D": 1093.5342947041986, "W_D_1KI": 1.2680335752382614, "J_D_1KI": 0.015838738620745467}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 7, ..., 449987, 449992,
449996]),
col_indices=tensor([ 26830, 38805, 39964, ..., 35924, 100960,
149008]),
values=tensor([-1.6227, 0.6078, 0.1377, ..., -0.4547, -0.3313,
1.8573]), size=(150000, 150000), nnz=449996,
layout=torch.sparse_csr)
tensor([0.6636, 0.3846, 0.0412, ..., 0.1798, 0.6708, 0.5140])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 449996
Density: 1.9999822222222222e-05
Time: 11.024610042572021 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 76302, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1124967, "MATRIX_DENSITY": 4.999853333333333e-05, "TIME_S": 10.245508193969727, "TIME_S_1KI": 0.13427574891837338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1582.9392757558821, "W": 158.07, "J_1KI": 20.745711459147625, "W_1KI": 2.071636392230872, "W_D": 121.98974999999999, "J_D": 1221.6256501210926, "W_D_1KI": 1.5987752614610362, "J_D_1KI": 0.020953254979699566}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 6, 14, ..., 1124950,
1124960, 1124967]),
col_indices=tensor([ 30457, 53538, 58099, ..., 43595, 68704,
127802]),
values=tensor([ 0.9993, 1.3551, -0.8257, ..., -0.5831, -0.3575,
0.8935]), size=(150000, 150000), nnz=1124967,
layout=torch.sparse_csr)
tensor([0.3516, 0.5198, 0.0988, ..., 0.3923, 0.0390, 0.6416])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 1124967
Density: 4.999853333333333e-05
Time: 10.245508193969727 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 61488, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1799911, "MATRIX_DENSITY": 7.999604444444445e-05, "TIME_S": 10.417873620986938, "TIME_S_1KI": 0.16942937843135147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1688.2730289936067, "W": 160.55, "J_1KI": 27.456951421311583, "W_1KI": 2.61107858443924, "W_D": 125.08400000000002, "J_D": 1315.3282065315248, "W_D_1KI": 2.034283112151965, "J_D_1KI": 0.03308422964077486}

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@ -0,0 +1,18 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 13, 22, ..., 1799885,
1799902, 1799911]),
col_indices=tensor([ 8148, 18288, 20984, ..., 90221, 145590,
147539]),
values=tensor([ 1.1715, -0.3998, -1.8516, ..., 0.6898, -0.1280,
-0.8730]), size=(150000, 150000), nnz=1799911,
layout=torch.sparse_csr)
tensor([0.6125, 0.0901, 0.3657, ..., 0.4636, 0.0050, 0.1440])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([150000, 150000])
Size: 22500000000
NNZ: 1799911
Density: 7.999604444444445e-05
Time: 10.417873620986938 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 32983, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3999807, "MATRIX_DENSITY": 9.9995175e-05, "TIME_S": 10.48969292640686, "TIME_S_1KI": 0.3180333179640075, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1717.8176005411146, "W": 159.42, "J_1KI": 52.08190887854697, "W_1KI": 4.833399023739502, "W_D": 124.50374999999998, "J_D": 1341.580310396254, "W_D_1KI": 3.7747854955583175, "J_D_1KI": 0.11444639649390043}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 25, 44, ..., 3999753,
3999777, 3999807]),
col_indices=tensor([ 11440, 17199, 18924, ..., 189529, 194020,
195373]),
values=tensor([ 2.0210, -0.2315, -1.7881, ..., -0.7783, 1.1984,
-1.3241]), size=(200000, 200000), nnz=3999807,
layout=torch.sparse_csr)
tensor([0.2301, 0.8723, 0.5820, ..., 0.6894, 0.3744, 0.2923])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 3999807
Density: 9.9995175e-05
Time: 10.48969292640686 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 77881, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 400000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.578283071517944, "TIME_S_1KI": 0.13582623581512748, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1463.2235719990729, "W": 136.67, "J_1KI": 18.78794021647222, "W_1KI": 1.7548567686598784, "W_D": 101.13924999999999, "J_D": 1082.82237985152, "W_D_1KI": 1.2986383071609249, "J_D_1KI": 0.016674648594149084}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 399999, 400000,
400000]),
col_indices=tensor([ 34673, 146068, 158534, ..., 145873, 173490,
70375]),
values=tensor([ 1.0796, -0.5052, 1.4130, ..., 1.5574, 0.2912,
1.0338]), size=(200000, 200000), nnz=400000,
layout=torch.sparse_csr)
tensor([0.3430, 0.0135, 0.1563, ..., 0.0368, 0.8801, 0.7194])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 400000
Density: 1e-05
Time: 10.578283071517944 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 73404, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 799992, "MATRIX_DENSITY": 1.99998e-05, "TIME_S": 10.732025861740112, "TIME_S_1KI": 0.14620491882922065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1703.6127026557924, "W": 159.46, "J_1KI": 23.208717544763125, "W_1KI": 2.1723611792272903, "W_D": 123.84325000000001, "J_D": 1323.096286455393, "W_D_1KI": 1.6871457958694351, "J_D_1KI": 0.022984384990864738}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 3, 7, ..., 799981, 799987,
799992]),
col_indices=tensor([ 27009, 174256, 194418, ..., 140709, 156996,
173843]),
values=tensor([ 0.0935, 1.3801, -0.5551, ..., 0.6888, 0.8476,
-0.0900]), size=(200000, 200000), nnz=799992,
layout=torch.sparse_csr)
tensor([0.8622, 0.0079, 0.1360, ..., 0.5227, 0.4709, 0.3275])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 799992
Density: 1.99998e-05
Time: 10.732025861740112 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 48787, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 1999955, "MATRIX_DENSITY": 4.9998875e-05, "TIME_S": 10.624053478240967, "TIME_S_1KI": 0.21776402480662815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1748.5560211658478, "W": 162.38, "J_1KI": 35.84061371196933, "W_1KI": 3.3283456658536084, "W_D": 126.24249999999999, "J_D": 1359.4166984975336, "W_D_1KI": 2.5876258019554386, "J_D_1KI": 0.053039248200451736}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 17, 32, ..., 1999929,
1999945, 1999955]),
col_indices=tensor([ 10339, 23771, 43985, ..., 142873, 182508,
198479]),
values=tensor([ 0.9127, -1.4974, 0.0924, ..., 1.0221, -2.4782,
-0.5277]), size=(200000, 200000), nnz=1999955,
layout=torch.sparse_csr)
tensor([0.9521, 0.4634, 0.1847, ..., 0.7016, 0.1879, 0.0658])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 1999955
Density: 4.9998875e-05
Time: 10.624053478240967 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 36618, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3199867, "MATRIX_DENSITY": 7.9996675e-05, "TIME_S": 10.282621145248413, "TIME_S_1KI": 0.2808078307184558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1651.7103869795799, "W": 160.09, "J_1KI": 45.10651556555737, "W_1KI": 4.371893604238353, "W_D": 124.06425, "J_D": 1280.0188042840362, "W_D_1KI": 3.3880673439292153, "J_D_1KI": 0.09252464208665725}

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pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 15, 31, ..., 3199841,
3199852, 3199867]),
col_indices=tensor([ 3804, 8724, 12172, ..., 120964, 148272,
152836]),
values=tensor([-0.6224, -0.8097, 1.2416, ..., -1.2954, -0.4499,
-0.0195]), size=(200000, 200000), nnz=3199867,
layout=torch.sparse_csr)
tensor([0.5490, 0.9304, 0.8028, ..., 0.1619, 0.9607, 0.8253])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([200000, 200000])
Size: 40000000000
NNZ: 3199867
Density: 7.9996675e-05
Time: 10.282621145248413 seconds

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