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cephi 2024-12-12 18:01:49 -05:00
parent b3f8fbe5c3
commit 89d3365313
72 changed files with 1650 additions and 55 deletions
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pytorch/output_test2/altra_10_2_10_100000_0.0001.json
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{"CPU": "Altra", "ITERATIONS": 82249, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999948, "MATRIX_DENSITY": 9.99948e-05, "TIME_S": 12.20802617073059, "TIME_S_1KI": 0.14842765469161437, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1033.954319496155, "W": 84.9319356344559, "J_1KI": 12.57102602458577, "W_1KI": 1.032619674822258, "W_D": 74.4169356344559, "J_D": 905.9455841684344, "W_D_1KI": 0.9047761752052413, "J_D_1KI": 0.011000451983674468} {"CPU": "Altra", "ITERATIONS": 68726, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999952, "MATRIX_DENSITY": 9.99952e-05, "TIME_S": 14.76737093925476, "TIME_S_1KI": 0.21487313301013825, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1048.773065109253, "W": 84.94573931101591, "J_1KI": 15.260208146978625, "W_1KI": 1.2360058683906516, "W_D": 74.56573931101592, "J_D": 920.6175566148759, "W_D_1KI": 1.0849713254229245, "J_D_1KI": 0.015786912164579992}

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pytorch/output_test2/altra_10_2_10_100000_0.0001.output
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 12, 20, ..., 999931, 999940, tensor(crow_indices=tensor([ 0, 9, 14, ..., 999938, 999944,
999948]), 999952]),
col_indices=tensor([20217, 25552, 38877, ..., 63581, 75717, 96314]), col_indices=tensor([ 7267, 12169, 14263, ..., 65124, 80624, 88608]),
values=tensor([-1.5899, -0.7194, -0.7547, ..., 0.5402, -0.1912, values=tensor([-0.9381, -0.7021, 0.3838, ..., 0.4652, -2.0655,
-0.1167]), size=(100000, 100000), nnz=999948, 0.0178]), size=(100000, 100000), nnz=999952,
layout=torch.sparse_csr) layout=torch.sparse_csr)
tensor([0.7377, 0.7528, 0.7695, ..., 0.6702, 0.9924, 0.8686]) tensor([0.6322, 0.2482, 0.5736, ..., 0.5609, 0.3437, 0.0062])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 999948 NNZ: 999952
Density: 9.99948e-05 Density: 9.99952e-05
Time: 12.20802617073059 seconds Time: 14.76737093925476 seconds

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pytorch/output_test2/altra_10_2_10_100000_1e-05.json
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{"CPU": "Altra", "ITERATIONS": 104724, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 10.740116596221924, "TIME_S_1KI": 0.10255640155286203, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 720.857314195633, "W": 76.49734010579499, "J_1KI": 6.883401266143702, "W_1KI": 0.7304661787727262, "W_D": 65.89234010579499, "J_D": 620.9232275140286, "W_D_1KI": 0.6291999933710991, "J_D_1KI": 0.0060081738032456665} {"CPU": "Altra", "ITERATIONS": 109027, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 11.089369058609009, "TIME_S_1KI": 0.10171213606362652, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1338.7191013622285, "W": 84.31423687535087, "J_1KI": 12.278785084082186, "W_1KI": 0.7733335492616588, "W_D": 73.66423687535087, "J_D": 1169.6212246823309, "W_D_1KI": 0.6756513237578844, "J_D_1KI": 0.006197100936078993}

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pytorch/output_test2/altra_10_2_10_100000_1e-05.output
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 99990, 99994, 99999]), tensor(crow_indices=tensor([ 0, 1, 3, ..., 99996, 99998, 99999]),
col_indices=tensor([77500, 30298, 91629, ..., 67143, 70964, 98118]), col_indices=tensor([51912, 73273, 76981, ..., 56282, 97323, 82299]),
values=tensor([ 0.0300, -1.0927, 1.5365, ..., -1.2655, 1.0213, values=tensor([-1.0009, -1.0395, 1.0694, ..., -0.2809, -0.4591,
0.2378]), size=(100000, 100000), nnz=99999, -1.3247]), size=(100000, 100000), nnz=99999,
layout=torch.sparse_csr) layout=torch.sparse_csr)
tensor([0.9139, 0.4903, 0.5737, ..., 0.7094, 0.3230, 0.9275]) tensor([0.9591, 0.5528, 0.0037, ..., 0.1141, 0.8131, 0.2616])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 99999 NNZ: 99999
Density: 9.9999e-06 Density: 9.9999e-06
Time: 10.740116596221924 seconds Time: 11.089369058609009 seconds

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{"CPU": "Altra", "ITERATIONS": 86604, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499984, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 11.186031818389893, "TIME_S_1KI": 0.129162992683824, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 703.3605092048646, "W": 72.85834958159226, "J_1KI": 8.121570703487883, "W_1KI": 0.8412815756961833, "W_D": 62.373349581592265, "J_D": 602.1403335988522, "W_D_1KI": 0.720213264763663, "J_D_1KI": 0.008316166282892973}

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pytorch/output_test2/altra_10_2_10_100000_5e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 10, ..., 499969, 499975,
499984]),
col_indices=tensor([50217, 56812, 62796, ..., 67972, 79752, 87971]),
values=tensor([ 2.8176, 1.0569, 0.3735, ..., -0.3011, 0.4006,
0.5158]), size=(100000, 100000), nnz=499984,
layout=torch.sparse_csr)
tensor([0.2516, 0.6501, 0.8377, ..., 0.5649, 0.1553, 0.1858])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499984
Density: 4.99984e-05
Time: 11.186031818389893 seconds

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{"CPU": "Altra", "ITERATIONS": 124752, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.144775629043579, "TIME_S_1KI": 0.08933544655832033, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 657.3852064323426, "W": 65.4524638588437, "J_1KI": 5.269536411699552, "W_1KI": 0.5246606375756997, "W_D": 54.9824638588437, "J_D": 552.227620215416, "W_D_1KI": 0.4407341273794705, "J_D_1KI": 0.003532882257434514}

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pytorch/output_test2/altra_10_2_10_10000_0.0001.output Normal file
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 3, ..., 9998, 10000, 10000]),
col_indices=tensor([6543, 1100, 5224, ..., 5370, 1002, 7590]),
values=tensor([-0.6024, 0.2491, 0.9340, ..., 0.1715, -0.8476,
1.0921]), size=(10000, 10000), nnz=10000,
layout=torch.sparse_csr)
tensor([0.4076, 0.0059, 0.6456, ..., 0.1126, 0.9287, 0.3305])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 11.144775629043579 seconds

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pytorch/output_test2/altra_10_2_10_10000_1e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 143288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.683725357055664, "TIME_S_1KI": 0.10247700684673988, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1078.6493862438201, "W": 74.05715122890027, "J_1KI": 7.527841733039892, "W_1KI": 0.5168412653460183, "W_D": 63.45215122890026, "J_D": 924.1865619075296, "W_D_1KI": 0.44282948487591606, "J_D_1KI": 0.0030904854898938924}

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@ -0,0 +1,375 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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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-9.8339e-01, 3.4486e-01, -1.4062e+00, -9.7044e-01,
-5.2972e-03, 9.1333e-01, 1.0024e+00, 9.3248e-01,
-1.4035e-01, 8.8494e-01, 7.4568e-01, -4.6183e-01,
-3.7966e-01, 6.5651e-02, 1.2309e+00, 1.1560e+00,
2.0723e+00, 2.3916e+00, 1.3140e+00, 2.1369e-01,
2.0539e-01, -1.9116e-01, -1.2841e+00, 1.0293e-01,
-1.1464e+00, -1.1779e+00, 1.3296e+00, -3.9741e-01,
1.0123e+00, 3.8692e-01, 9.6798e-01, -5.0365e-01,
6.9614e-01, 6.5680e-01, 6.3927e-01, 1.2289e-01,
-7.1378e-01, 1.4048e+00, -2.9717e-01, 4.0848e-01,
-1.7374e+00, 8.1578e-01, 8.9790e-01, 7.8613e-01,
-9.3854e-01, -1.2153e+00, 1.7158e+00, -2.4091e-01,
1.8530e-01, -9.8432e-01, -7.6705e-02, 1.1269e+00,
1.4949e+00, -3.2681e-01, 8.3171e-01, 8.4933e-01,
-4.8143e-01, 7.3843e-01, 1.2397e-01, 4.5028e-01,
-5.2425e-01, -1.6772e+00, 2.3094e+00, -5.5873e-01,
-7.8440e-01, 1.9962e-01, 8.1310e-01, -2.1801e-01]),
size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.7505, 0.4073, 0.0835, ..., 0.6948, 0.5731, 0.2916])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 14.683725357055664 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 120060, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.660146236419678, "TIME_S_1KI": 0.10544849438963584, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 870.1460137557983, "W": 72.72296358512563, "J_1KI": 7.247592984805916, "W_1KI": 0.6057218356249011, "W_D": 61.982963585125624, "J_D": 741.6395870780945, "W_D_1KI": 0.516266563261083, "J_D_1KI": 0.004300071324846602}

16
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 5000, 5000, 5000]),
col_indices=tensor([8592, 9144, 2169, ..., 9134, 8894, 359]),
values=tensor([-0.0060, -0.0063, -0.4176, ..., 1.0189, 0.1882,
1.7586]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.3551, 0.3905, 0.3120, ..., 0.2483, 0.7672, 0.2531])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 12.660146236419678 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 133794, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39999, "MATRIX_DENSITY": 9.99975e-05, "TIME_S": 16.90324330329895, "TIME_S_1KI": 0.12633782758045167, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 805.8708676052095, "W": 75.0263270228942, "J_1KI": 6.023221277525221, "W_1KI": 0.5607600267791844, "W_D": 64.4763270228942, "J_D": 692.551477057934, "W_D_1KI": 0.48190746238915194, "J_D_1KI": 0.003601861536310686}

16
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 3, ..., 39995, 39997, 39999]),
col_indices=tensor([17006, 4621, 18341, ..., 19319, 4727, 18723]),
values=tensor([ 0.2379, -0.9389, 0.6425, ..., 0.1001, 1.7488,
-0.7276]), size=(20000, 20000), nnz=39999,
layout=torch.sparse_csr)
tensor([0.8179, 0.8239, 0.9268, ..., 0.9263, 0.5883, 0.5053])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39999
Density: 9.99975e-05
Time: 16.90324330329895 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 111302, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.277668476104736, "TIME_S_1KI": 0.09234037551979961, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 837.560459136963, "W": 71.64058174307, "J_1KI": 7.525115982973918, "W_1KI": 0.6436594287889705, "W_D": 60.82558174307, "J_D": 711.1207214188578, "W_D_1KI": 0.5464913635250939, "J_D_1KI": 0.004909986914207237}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 3999, 3999, 4000]),
col_indices=tensor([10253, 7068, 18490, ..., 5661, 16756, 17692]),
values=tensor([ 1.4469, 0.6569, -0.3333, ..., -0.9449, -0.0864,
-1.4279]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.2516, 0.4285, 0.8673, ..., 0.0632, 0.3777, 0.0594])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.277668476104736 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 125814, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.677078485488892, "TIME_S_1KI": 0.08486399355786232, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 799.5182011795044, "W": 75.37915658808171, "J_1KI": 6.3547633902388005, "W_1KI": 0.5991317070284842, "W_D": 64.78415658808171, "J_D": 687.1410438203812, "W_D_1KI": 0.5149200930586557, "J_D_1KI": 0.004092709023309454}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 19996, 19998, 19999]),
col_indices=tensor([ 2667, 4661, 5883, ..., 13481, 17827, 19838]),
values=tensor([-1.5771, -0.6983, -0.6117, ..., -1.0094, -0.7733,
0.5763]), size=(20000, 20000), nnz=19999,
layout=torch.sparse_csr)
tensor([0.3110, 0.3755, 0.4763, ..., 0.0556, 0.0235, 0.3706])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 19999
Density: 4.99975e-05
Time: 10.677078485488892 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 118605, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249986, "MATRIX_DENSITY": 9.99944e-05, "TIME_S": 11.267115592956543, "TIME_S_1KI": 0.09499696971423248, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 878.7403219223023, "W": 76.94861601833053, "J_1KI": 7.408965236898127, "W_1KI": 0.6487805406039419, "W_D": 66.37861601833052, "J_D": 758.0326902151107, "W_D_1KI": 0.5596611948765273, "J_D_1KI": 0.004718698156709476}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 15, ..., 249976, 249982,
249986]),
col_indices=tensor([17442, 19726, 35075, ..., 45436, 48088, 48654]),
values=tensor([-0.4134, -0.5604, 2.5859, ..., 0.1425, -0.4736,
-0.0671]), size=(50000, 50000), nnz=249986,
layout=torch.sparse_csr)
tensor([0.7503, 0.4656, 0.2507, ..., 0.7766, 0.1600, 0.0359])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249986
Density: 9.99944e-05
Time: 11.267115592956543 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 115338, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.82823133468628, "TIME_S_1KI": 0.11989310838306784, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 894.4880589389801, "W": 76.29270117988719, "J_1KI": 7.755363010794188, "W_1KI": 0.6614706443660129, "W_D": 65.81270117988718, "J_D": 771.6160841274261, "W_D_1KI": 0.570607268895656, "J_D_1KI": 0.004947261690818777}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 25000, 25000, 25000]),
col_indices=tensor([13862, 4916, 38787, ..., 5088, 30567, 42215]),
values=tensor([-0.2260, 1.4145, 1.1308, ..., 0.9407, 0.6943,
-0.2440]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.8792, 0.8161, 0.1487, ..., 0.5739, 0.7719, 0.6015])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 13.82823133468628 seconds

1
pytorch/output_test2/altra_10_2_10_50000_5e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 132458, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124998, "MATRIX_DENSITY": 4.99992e-05, "TIME_S": 12.935593843460083, "TIME_S_1KI": 0.09765807911534286, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 887.2351137542726, "W": 76.34686817805957, "J_1KI": 6.698237280906193, "W_1KI": 0.5763854820249404, "W_D": 65.79186817805956, "J_D": 764.5743308150768, "W_D_1KI": 0.49669984582327653, "J_D_1KI": 0.003749866718682726}

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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 4, ..., 124992, 124995,
124998]),
col_indices=tensor([19401, 47685, 26750, ..., 932, 14818, 47901]),
values=tensor([ 1.5329, 2.1967, 0.7519, ..., 1.6488, -0.2402,
-1.2661]), size=(50000, 50000), nnz=124998,
layout=torch.sparse_csr)
tensor([0.3628, 0.0633, 0.4692, ..., 0.2124, 0.4450, 0.4631])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124998
Density: 4.99992e-05
Time: 12.935593843460083 seconds

2
pytorch/output_test2/epyc_7313p_10_2_10_100000_0.0001.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 101034, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999955, "MATRIX_DENSITY": 9.99955e-05, "TIME_S": 10.361092805862427, "TIME_S_1KI": 0.10255055531665011, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1568.5116010475158, "W": 147.64, "J_1KI": 15.524591731966623, "W_1KI": 1.4612902587247856, "W_D": 127.57249999999999, "J_D": 1355.316623033285, "W_D_1KI": 1.2626690025140053, "J_D_1KI": 0.012497466224379963} {"CPU": "Epyc 7313P", "ITERATIONS": 98312, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999949, "MATRIX_DENSITY": 9.99949e-05, "TIME_S": 10.41502332687378, "TIME_S_1KI": 0.10593847472204593, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1474.0722945690156, "W": 147.65, "J_1KI": 14.993818603720966, "W_1KI": 1.5018512490845473, "W_D": 128.1775, "J_D": 1279.6674672341348, "W_D_1KI": 1.3037828545854016, "J_D_1KI": 0.013261685802195068}

18
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@ -1,17 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 12, 24, ..., 999933, 999940, tensor(crow_indices=tensor([ 0, 11, 23, ..., 999932, 999944,
999955]), 999949]),
col_indices=tensor([ 5967, 15636, 19622, ..., 82825, 87847, 97213]), col_indices=tensor([ 9003, 14694, 14961, ..., 25088, 62580, 64370]),
values=tensor([-1.5657, 1.3165, 0.1051, ..., -0.5017, 0.1827, values=tensor([-0.5626, -0.3545, 0.8913, ..., -1.4062, -1.3465,
-1.1977]), size=(100000, 100000), nnz=999955, 0.0257]), size=(100000, 100000), nnz=999949,
layout=torch.sparse_csr) layout=torch.sparse_csr)
tensor([0.4289, 0.2254, 0.8435, ..., 0.1753, 0.8896, 0.3058]) tensor([0.5947, 0.3012, 0.0547, ..., 0.1233, 0.4957, 0.0854])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 999955 NNZ: 999949
Density: 9.99955e-05 Density: 9.99949e-05
Time: 10.361092805862427 seconds Time: 10.41502332687378 seconds

2
pytorch/output_test2/epyc_7313p_10_2_10_100000_1e-05.json
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@ -1 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 150582, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.027292013168335, "TIME_S_1KI": 0.06659024327720667, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1239.2374660491944, "W": 116.83, "J_1KI": 8.229652057013418, "W_1KI": 0.7758563440517459, "W_D": 97.04249999999999, "J_D": 1029.3477856636046, "W_D_1KI": 0.6444495358010917, "J_D_1KI": 0.0042797249060385146} {"CPU": "Epyc 7313P", "ITERATIONS": 156417, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 11.098344087600708, "TIME_S_1KI": 0.07095356698824748, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1239.9076641368865, "W": 118.41, "J_1KI": 7.92693674048784, "W_1KI": 0.7570149024722377, "W_D": 97.90375, "J_D": 1025.1803899395466, "W_D_1KI": 0.6259150220244603, "J_D_1KI": 0.004001579253050885}

19
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@ -1,17 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 99996, 99997, tensor(crow_indices=tensor([ 0, 0, 2, ..., 99996, 99998, 99999]),
100000]), col_indices=tensor([18767, 44455, 31476, ..., 37701, 96003, 93517]),
col_indices=tensor([98366, 86469, 784, ..., 24883, 35225, 74645]), values=tensor([ 1.1909e+00, -1.8994e-01, 2.6436e-04, ...,
values=tensor([ 0.5652, 0.5870, -0.9667, ..., -0.8134, 0.3649, -4.3065e-01, -4.1931e-01, -1.6576e+00]),
-0.5054]), size=(100000, 100000), nnz=100000, size=(100000, 100000), nnz=99999, layout=torch.sparse_csr)
layout=torch.sparse_csr) tensor([0.3502, 0.7895, 0.7161, ..., 0.4208, 0.4096, 0.7887])
tensor([0.7832, 0.0968, 0.2513, ..., 0.3975, 0.2140, 0.9668])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 100000 NNZ: 99999
Density: 1e-05 Density: 9.9999e-06
Time: 10.027292013168335 seconds Time: 11.098344087600708 seconds

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{"CPU": "Epyc 7313P", "ITERATIONS": 135631, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499989, "MATRIX_DENSITY": 4.99989e-05, "TIME_S": 10.57732343673706, "TIME_S_1KI": 0.0779860314879125, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1524.0668136215209, "W": 146.54, "J_1KI": 11.236861879817452, "W_1KI": 1.0804314647831248, "W_D": 126.76624999999999, "J_D": 1318.4129569554327, "W_D_1KI": 0.9346406794906768, "J_D_1KI": 0.006891054991046861}

17
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 499973, 499982,
499989]),
col_indices=tensor([ 6400, 26862, 36191, ..., 26915, 49846, 61682]),
values=tensor([-0.6810, -0.8696, 1.1051, ..., -0.5859, 0.5431,
-0.8420]), size=(100000, 100000), nnz=499989,
layout=torch.sparse_csr)
tensor([0.2037, 0.9965, 0.5140, ..., 0.7576, 0.1956, 0.2524])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499989
Density: 4.99989e-05
Time: 10.57732343673706 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 402279, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9999, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 11.058919668197632, "TIME_S_1KI": 0.02749067107206101, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1068.2311151790618, "W": 98.58, "J_1KI": 2.65544837085471, "W_1KI": 0.24505380594065315, "W_D": 78.6075, "J_D": 851.8054106962682, "W_D_1KI": 0.19540542757638357, "J_D_1KI": 0.0004857460309297368}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 9996, 9999, 9999]),
col_indices=tensor([6722, 8655, 672, ..., 2263, 3918, 5766]),
values=tensor([ 0.4549, 0.3188, 1.2914, ..., 0.2944, -0.2287,
0.9296]), size=(10000, 10000), nnz=9999,
layout=torch.sparse_csr)
tensor([0.6875, 0.3376, 0.2808, ..., 0.9144, 0.8469, 0.4162])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 9999
Density: 9.999e-05
Time: 11.058919668197632 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 523085, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.953115224838257, "TIME_S_1KI": 0.020939455776476587, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1012.5659449434281, "W": 96.26, "J_1KI": 1.9357579455412182, "W_1KI": 0.18402362904690442, "W_D": 76.33250000000001, "J_D": 802.9471222978832, "W_D_1KI": 0.14592752611908202, "J_D_1KI": 0.00027897478635227933}

375
pytorch/output_test2/epyc_7313p_10_2_10_10000_1e-05.output Normal file
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@ -0,0 +1,375 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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)
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Time: 10.953115224838257 seconds

1
pytorch/output_test2/epyc_7313p_10_2_10_10000_5e-05.json Normal file
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15
pytorch/output_test2/epyc_7313p_10_2_10_10000_5e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 4997, 4997, 4999]),
col_indices=tensor([7534, 4703, 7615, ..., 690, 3311, 9850]),
values=tensor([0.9319, 1.5848, 0.2665, ..., 1.2538, 0.8654, 0.6652]),
size=(10000, 10000), nnz=4999, layout=torch.sparse_csr)
tensor([0.4609, 0.8821, 0.6641, ..., 0.4010, 0.9569, 0.0599])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 4999
Density: 4.999e-05
Time: 10.101872205734253 seconds

1
pytorch/output_test2/epyc_7313p_10_2_10_20000_0.0001.json Normal file
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16
pytorch/output_test2/epyc_7313p_10_2_10_20000_0.0001.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 39996, 39998, 40000]),
col_indices=tensor([ 3017, 12065, 12288, ..., 14488, 300, 17624]),
values=tensor([ 0.9413, 1.2109, -0.1435, ..., -0.9306, 1.4038,
-1.7362]), size=(20000, 20000), nnz=40000,
layout=torch.sparse_csr)
tensor([0.4349, 0.6098, 0.4010, ..., 0.6451, 0.4277, 0.0573])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 40000
Density: 0.0001
Time: 10.829553365707397 seconds

1
pytorch/output_test2/epyc_7313p_10_2_10_20000_1e-05.json Normal file
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{"CPU": "Epyc 7313P", "ITERATIONS": 376559, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.13594126701355, "TIME_S_1KI": 0.029572898980009903, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1044.873656282425, "W": 99.08, "J_1KI": 2.774794006470234, "W_1KI": 0.26311945803977593, "W_D": 79.34625, "J_D": 836.7663135829567, "W_D_1KI": 0.21071399169851207, "J_D_1KI": 0.0005595776271407988}

16
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 3999, 3999, 4000]),
col_indices=tensor([ 8934, 212, 12203, ..., 17644, 4637, 9395]),
values=tensor([ 1.2371, 0.0694, 2.3960, ..., -1.0433, -0.2651,
0.6109]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.2961, 0.5849, 0.9840, ..., 0.2712, 0.9792, 0.9610])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 11.13594126701355 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 247171, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.491997718811035, "TIME_S_1KI": 0.0424483362482291, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1038.4885951280594, "W": 99.49, "J_1KI": 4.201498537967882, "W_1KI": 0.4025148581346517, "W_D": 79.61874999999999, "J_D": 831.070095822215, "W_D_1KI": 0.32212011117809125, "J_D_1KI": 0.0013032277701594897}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1, ..., 19995, 19995, 19999]),
col_indices=tensor([ 6664, 8664, 844, ..., 13486, 13952, 14311]),
values=tensor([ 0.0765, 0.5519, 0.0090, ..., 1.4320, -0.8858,
-0.6986]), size=(20000, 20000), nnz=19999,
layout=torch.sparse_csr)
tensor([0.3197, 0.2721, 0.5326, ..., 0.8808, 0.5799, 0.4532])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 19999
Density: 4.99975e-05
Time: 10.491997718811035 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 126709, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249991, "MATRIX_DENSITY": 9.99964e-05, "TIME_S": 10.611387491226196, "TIME_S_1KI": 0.08374612293701471, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1311.9159492492677, "W": 120.65, "J_1KI": 10.35377083908221, "W_1KI": 0.952181770829223, "W_D": 100.78750000000001, "J_D": 1095.9364171981813, "W_D_1KI": 0.7954249500824725, "J_D_1KI": 0.006277572627693948}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 5, ..., 249979, 249987,
249991]),
col_indices=tensor([ 6923, 14624, 14826, ..., 16653, 24983, 26510]),
values=tensor([-0.3339, -0.1256, 1.3717, ..., 0.6008, -2.5611,
-0.3793]), size=(50000, 50000), nnz=249991,
layout=torch.sparse_csr)
tensor([0.2532, 0.9095, 0.3546, ..., 0.7075, 0.6638, 0.8293])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249991
Density: 9.99964e-05
Time: 10.611387491226196 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 210368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.059137105941772, "TIME_S_1KI": 0.05257043421975668, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1123.8110211277008, "W": 103.99, "J_1KI": 5.342119624314062, "W_1KI": 0.49432423182233043, "W_D": 83.88749999999999, "J_D": 906.5650258183479, "W_D_1KI": 0.3987654966534834, "J_D_1KI": 0.001895561571405743}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 24999, 24999, 25000]),
col_indices=tensor([17991, 40249, 32851, ..., 16381, 40475, 35032]),
values=tensor([-2.1931, -0.1057, -0.3336, ..., 1.6608, 0.0622,
-1.3985]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.4030, 0.6455, 0.3588, ..., 0.8411, 0.5744, 0.8801])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 11.059137105941772 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 161422, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124998, "MATRIX_DENSITY": 4.99992e-05, "TIME_S": 10.579585790634155, "TIME_S_1KI": 0.06553992510707435, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1212.8924506664275, "W": 114.44, "J_1KI": 7.513798928686471, "W_1KI": 0.7089492138618032, "W_D": 94.58875, "J_D": 1002.4989583447576, "W_D_1KI": 0.5859718625713967, "J_D_1KI": 0.0036300619653541442}

17
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 5, ..., 124996, 124997,
124998]),
col_indices=tensor([11989, 44778, 46590, ..., 12645, 23142, 31661]),
values=tensor([-0.0866, 1.8120, -0.0219, ..., 1.2549, -1.2066,
-0.6973]), size=(50000, 50000), nnz=124998,
layout=torch.sparse_csr)
tensor([0.3195, 0.2357, 0.1472, ..., 0.2809, 0.6862, 0.5275])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124998
Density: 4.99992e-05
Time: 10.579585790634155 seconds

2
pytorch/output_test2/xeon_4216_10_2_10_100000_0.0001.json
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@ -1 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 41245, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999957, "MATRIX_DENSITY": 9.99957e-05, "TIME_S": 10.48258900642395, "TIME_S_1KI": 0.2541541764195406, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 907.7721267700196, "W": 86.4, "J_1KI": 22.00926480227954, "W_1KI": 2.0947993696205605, "W_D": 77.29, "J_D": 812.0568018293382, "W_D_1KI": 1.8739241120135777, "J_D_1KI": 0.045433970469476975} {"CPU": "Xeon 4216", "ITERATIONS": 41417, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999936, "MATRIX_DENSITY": 9.99936e-05, "TIME_S": 10.603114604949951, "TIME_S_1KI": 0.2560087549786308, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 911.4264350938797, "W": 86.86, "J_1KI": 22.006094963273046, "W_1KI": 2.097206461115001, "W_D": 77.6825, "J_D": 815.1264568752051, "W_D_1KI": 1.8756187072941064, "J_D_1KI": 0.045286203908880565}

18
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@ -1,17 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 10, 20, ..., 999933, 999947, tensor(crow_indices=tensor([ 0, 15, 22, ..., 999917, 999929,
999957]), 999936]),
col_indices=tensor([10614, 12000, 12630, ..., 76477, 82289, 92989]), col_indices=tensor([ 6790, 16501, 25658, ..., 50375, 68639, 74275]),
values=tensor([ 0.5650, 0.5553, -0.5300, ..., 0.3637, -1.1395, values=tensor([ 0.5666, 1.0499, -2.0495, ..., 1.5720, 1.2770,
0.7341]), size=(100000, 100000), nnz=999957, -0.7894]), size=(100000, 100000), nnz=999936,
layout=torch.sparse_csr) layout=torch.sparse_csr)
tensor([0.5083, 0.7251, 0.1206, ..., 0.9177, 0.3147, 0.5521]) tensor([0.9755, 0.4770, 0.7031, ..., 0.5604, 0.7471, 0.7018])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 999957 NNZ: 999936
Density: 9.99957e-05 Density: 9.99936e-05
Time: 10.48258900642395 seconds Time: 10.603114604949951 seconds

2
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@ -1 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 118541, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99998, "MATRIX_DENSITY": 9.9998e-06, "TIME_S": 10.91425085067749, "TIME_S_1KI": 0.09207152673486381, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 868.9650503587723, "W": 82.07, "J_1KI": 7.3305021077835715, "W_1KI": 0.6923342978378788, "W_D": 72.5, "J_D": 767.636970281601, "W_D_1KI": 0.6116027366059, "J_D_1KI": 0.005159419412742426} {"CPU": "Xeon 4216", "ITERATIONS": 117123, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99998, "MATRIX_DENSITY": 9.9998e-06, "TIME_S": 10.340282917022705, "TIME_S_1KI": 0.08828567332652601, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 875.3140355348587, "W": 81.91, "J_1KI": 7.473459828853929, "W_1KI": 0.6993502557140784, "W_D": 72.77, "J_D": 777.6413425207138, "W_D_1KI": 0.6213126371421497, "J_D_1KI": 0.005304787592037001}

12
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@ -1,16 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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) matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 2, 3, ..., 99997, 99997, 99998]), tensor(crow_indices=tensor([ 0, 0, 0, ..., 99997, 99997, 99998]),
col_indices=tensor([90305, 96230, 86891, ..., 66888, 39495, 21203]), col_indices=tensor([58363, 95270, 20035, ..., 59508, 69423, 51805]),
values=tensor([-0.5290, 1.7137, 0.7615, ..., 1.2465, -0.3855, values=tensor([ 1.0401, 0.8766, 1.7073, ..., -0.0072, 1.9232,
-0.4542]), size=(100000, 100000), nnz=99998, 0.0142]), size=(100000, 100000), nnz=99998,
layout=torch.sparse_csr) layout=torch.sparse_csr)
tensor([0.2048, 0.5046, 0.8421, ..., 0.4453, 0.3792, 0.7036]) tensor([0.2867, 0.2295, 0.4526, ..., 0.7869, 0.6646, 0.3034])
Matrix: synthetic Matrix: synthetic
Matrix: csr Matrix: csr
Shape: torch.Size([100000, 100000]) Shape: torch.Size([100000, 100000])
Size: 10000000000 Size: 10000000000
NNZ: 99998 NNZ: 99998
Density: 9.9998e-06 Density: 9.9998e-06
Time: 10.91425085067749 seconds Time: 10.340282917022705 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 69686, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499986, "MATRIX_DENSITY": 4.99986e-05, "TIME_S": 10.165817260742188, "TIME_S_1KI": 0.14588033838564687, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 899.6590386629105, "W": 88.05, "J_1KI": 12.910183374894677, "W_1KI": 1.2635249547972334, "W_D": 78.845, "J_D": 805.6060977101325, "W_D_1KI": 1.1314324254513102, "J_D_1KI": 0.016236151098517785}

17
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 9, ..., 499980, 499983,
499986]),
col_indices=tensor([58761, 63539, 72385, ..., 33504, 41124, 68298]),
values=tensor([-0.1835, 1.2708, -2.4180, ..., -0.2638, 0.8943,
0.8332]), size=(100000, 100000), nnz=499986,
layout=torch.sparse_csr)
tensor([0.6028, 0.3331, 0.7633, ..., 0.8876, 0.4663, 0.2198])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499986
Density: 4.99986e-05
Time: 10.165817260742188 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 395356, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9999, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.566214323043823, "TIME_S_1KI": 0.026725822608089478, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 788.1841578483582, "W": 74.55, "J_1KI": 1.9936061621636147, "W_1KI": 0.18856423071864345, "W_D": 65.39375, "J_D": 691.3791787028313, "W_D_1KI": 0.16540472384382682, "J_D_1KI": 0.0004183690745652698}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2, ..., 9997, 9997, 9999]),
col_indices=tensor([4106, 8016, 7128, ..., 5609, 1233, 8666]),
values=tensor([ 0.6536, -2.5242, 0.4276, ..., 0.4750, -1.7889,
1.5433]), size=(10000, 10000), nnz=9999,
layout=torch.sparse_csr)
tensor([0.4100, 0.4025, 0.7291, ..., 0.6304, 0.5931, 0.7594])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 9999
Density: 9.999e-05
Time: 10.566214323043823 seconds

1
pytorch/output_test2/xeon_4216_10_2_10_10000_1e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 476678, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.187696933746338, "TIME_S_1KI": 0.023470134836821373, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 768.1570157098771, "W": 73.37, "J_1KI": 1.6114798998692559, "W_1KI": 0.15391941730056768, "W_D": 64.16125000000001, "J_D": 671.7447774869205, "W_D_1KI": 0.13460082067978807, "J_D_1KI": 0.0002823726303286245}

375
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@ -0,0 +1,375 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 1000, 1000, 1000]),
col_indices=tensor([1654, 1104, 4675, 5854, 9970, 6100, 7133, 1211, 5315,
478, 5135, 2953, 9948, 210, 8574, 5480, 706, 6269,
5530, 5031, 109, 9703, 1681, 7878, 4767, 73, 2990,
1980, 7102, 7980, 8316, 5886, 6974, 5917, 9033, 2097,
3756, 1865, 5092, 506, 4936, 8829, 4251, 7526, 735,
119, 6076, 5655, 3122, 634, 2728, 1562, 3695, 1648,
778, 8113, 2206, 8081, 4091, 8173, 782, 4321, 1750,
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size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.2908, 0.6605, 0.7225, ..., 0.3770, 0.3334, 0.5813])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 11.187696933746338 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 440665, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.798494100570679, "TIME_S_1KI": 0.024504996086756787, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 793.5612908649445, "W": 74.13, "J_1KI": 1.8008266843632794, "W_1KI": 0.1682230265621277, "W_D": 64.96625, "J_D": 695.4633915102482, "W_D_1KI": 0.14742775123960378, "J_D_1KI": 0.00033455743306049667}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 5000, 5000, 5000]),
col_indices=tensor([8266, 4110, 1127, ..., 2062, 4056, 905]),
values=tensor([-0.6934, -1.5934, 0.4073, ..., -1.3866, -0.1937,
-1.3300]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.9293, 0.8596, 0.7145, ..., 0.3618, 0.3352, 0.4743])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 10.798494100570679 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 259904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39996, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.36286997795105, "TIME_S_1KI": 0.039871914160424814, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 801.7571017837524, "W": 77.24, "J_1KI": 3.0848201712315024, "W_1KI": 0.2971866535336124, "W_D": 68.00375, "J_D": 705.884121056795, "W_D_1KI": 0.2616494936591972, "J_D_1KI": 0.0010067159168739121}

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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 6, ..., 39993, 39994, 39996]),
col_indices=tensor([12767, 8765, 9580, ..., 6145, 8828, 14685]),
values=tensor([ 0.1350, 0.9538, -0.5970, ..., 0.4847, 1.1186,
-0.3204]), size=(20000, 20000), nnz=39996,
layout=torch.sparse_csr)
tensor([0.2590, 0.5571, 0.5001, ..., 0.9763, 0.4254, 0.2867])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39996
Density: 9.999e-05
Time: 10.36286997795105 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 368614, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.64458179473877, "TIME_S_1KI": 0.028877312838738546, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 785.2860484743118, "W": 74.57, "J_1KI": 2.1303749951827977, "W_1KI": 0.20229833918407872, "W_D": 65.41749999999999, "J_D": 688.9023746287821, "W_D_1KI": 0.1774688427460704, "J_D_1KI": 0.0004814490028758278}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 3999, 3999, 4000]),
col_indices=tensor([ 4783, 9851, 5325, ..., 15405, 3201, 3555]),
values=tensor([ 0.3085, 1.2735, -0.3529, ..., 1.1219, 1.0957,
-0.5773]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.7769, 0.4542, 0.8807, ..., 0.1799, 0.8487, 0.1514])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.64458179473877 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 283636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 20000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.500569581985474, "TIME_S_1KI": 0.03702128637403388, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 810.0879921865463, "W": 76.03, "J_1KI": 2.8560831212770816, "W_1KI": 0.26805483083952675, "W_D": 66.86375, "J_D": 712.4230039137601, "W_D_1KI": 0.23573788235625942, "J_D_1KI": 0.0008311282148819593}

16
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@ -0,0 +1,16 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1, ..., 19998, 19999, 20000]),
col_indices=tensor([ 8922, 1520, 2083, ..., 15757, 10768, 6287]),
values=tensor([-1.1442, 0.4868, 0.6733, ..., 0.9094, -1.4024,
-0.9844]), size=(20000, 20000), nnz=20000,
layout=torch.sparse_csr)
tensor([0.7870, 0.5320, 0.2324, ..., 0.3230, 0.6000, 0.7397])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 20000
Density: 5e-05
Time: 10.500569581985474 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 136743, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249989, "MATRIX_DENSITY": 9.99956e-05, "TIME_S": 10.19824481010437, "TIME_S_1KI": 0.07457964802662198, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 912.4124284863472, "W": 84.05, "J_1KI": 6.672461687152887, "W_1KI": 0.6146566917502175, "W_D": 74.32124999999999, "J_D": 806.8010969737171, "W_D_1KI": 0.5435104539172023, "J_D_1KI": 0.0039746857529614115}

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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, 9, ..., 249977, 249981,
249989]),
col_indices=tensor([ 1763, 15898, 31944, ..., 25767, 45730, 48074]),
values=tensor([ 0.6468, 0.2817, -0.4433, ..., 0.7389, -0.5082,
-0.4977]), size=(50000, 50000), nnz=249989,
layout=torch.sparse_csr)
tensor([0.3477, 0.0927, 0.9187, ..., 0.0955, 0.4581, 0.4152])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249989
Density: 9.99956e-05
Time: 10.19824481010437 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 213899, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.812883138656616, "TIME_S_1KI": 0.050551349649398156, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 809.0901684951782, "W": 74.49, "J_1KI": 3.7825804164356924, "W_1KI": 0.34824847240987566, "W_D": 65.00625, "J_D": 706.0802492380142, "W_D_1KI": 0.3039109579754931, "J_D_1KI": 0.0014208152351132689}

17
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@ -0,0 +1,17 @@
/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 25000, 25000, 25000]),
col_indices=tensor([14105, 49794, 12275, ..., 28034, 32175, 49067]),
values=tensor([-0.2580, 2.2414, -0.4406, ..., 0.7293, 1.3018,
0.4178]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([6.5155e-01, 7.4521e-01, 5.7246e-01, ..., 2.1695e-01, 2.5153e-01,
5.8967e-04])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 10.812883138656616 seconds

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pytorch/output_test2/xeon_4216_10_2_10_50000_5e-05.json Normal file
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{"CPU": "Xeon 4216", "ITERATIONS": 167105, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124998, "MATRIX_DENSITY": 4.99992e-05, "TIME_S": 10.385007858276367, "TIME_S_1KI": 0.06214660158748313, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 840.060476000309, "W": 80.17, "J_1KI": 5.02714147392543, "W_1KI": 0.47975823583974153, "W_D": 70.9425, "J_D": 743.3702172714471, "W_D_1KI": 0.4245384638401005, "J_D_1KI": 0.0025405491388055448}

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pytorch/output_test2/xeon_4216_10_2_10_50000_5e-05.output Normal file
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/nfshomes/vut/ampere_research/pytorch/spmv.py:57: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 124995, 124996,
124998]),
col_indices=tensor([ 4431, 12161, 21642, ..., 46785, 7280, 21912]),
values=tensor([-0.0038, 0.8141, -1.0870, ..., -0.4364, -1.6532,
-0.2924]), size=(50000, 50000), nnz=124998,
layout=torch.sparse_csr)
tensor([0.7830, 0.7286, 0.1161, ..., 0.7505, 0.0868, 0.3751])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124998
Density: 4.99992e-05
Time: 10.385007858276367 seconds