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cephi 2024-12-12 22:10:49 -05:00
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{"CPU": "Altra", "ITERATIONS": 33323, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999952, "MATRIX_DENSITY": 9.99952e-05, "TIME_S": 10.71526312828064, "TIME_S_1KI": 0.3215575766971953, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 412.07791979789727, "W": 38.7935930425703, "J_1KI": 12.366171106980081, "W_1KI": 1.1641686835690153, "W_D": 29.0535930425703, "J_D": 308.6165329027175, "W_D_1KI": 0.8718780734798878, "J_D_1KI": 0.026164453184883946}

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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, 17, ..., 999937, 999941,
999952]),
col_indices=tensor([19687, 28948, 45848, ..., 46436, 60409, 68965]),
values=tensor([-0.4272, 0.9264, 0.9856, ..., -0.1190, 0.6751,
0.0598]), size=(100000, 100000), nnz=999952,
layout=torch.sparse_csr)
tensor([0.2980, 0.3512, 0.0619, ..., 0.2729, 0.3128, 0.6493])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 999952
Density: 9.99952e-05
Time: 10.71526312828064 seconds

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pytorch/output_8core/altra_10_2_10_100000_1e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 101767, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99998, "MATRIX_DENSITY": 9.9998e-06, "TIME_S": 10.474453926086426, "TIME_S_1KI": 0.10292583967382772, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 291.813862285614, "W": 26.46627795822632, "J_1KI": 2.8674704205254553, "W_1KI": 0.2600673888217823, "W_D": 16.77127795822632, "J_D": 184.91800789594646, "W_D_1KI": 0.16480075032403746, "J_D_1KI": 0.0016193928319006893}

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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, ..., 99996, 99996, 99998]),
col_indices=tensor([ 109, 74194, 61918, ..., 25487, 43401, 98161]),
values=tensor([ 0.2524, -0.1849, -2.0293, ..., 0.7242, 1.0483,
0.0862]), size=(100000, 100000), nnz=99998,
layout=torch.sparse_csr)
tensor([0.5112, 0.5546, 0.5214, ..., 0.6604, 0.9362, 0.9569])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 99998
Density: 9.9998e-06
Time: 10.474453926086426 seconds

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pytorch/output_8core/altra_10_2_10_100000_5e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 50359, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499980, "MATRIX_DENSITY": 4.9998e-05, "TIME_S": 10.462877750396729, "TIME_S_1KI": 0.20776579658842964, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 399.8736016845703, "W": 38.582577789425095, "J_1KI": 7.940459534235593, "W_1KI": 0.7661505945198495, "W_D": 29.062577789425095, "J_D": 301.20739257812494, "W_D_1KI": 0.5771079209163227, "J_D_1KI": 0.011459876505020408}

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pytorch/output_8core/altra_10_2_10_100000_5e-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, 9, 12, ..., 499970, 499978,
499980]),
col_indices=tensor([19403, 23038, 25846, ..., 91095, 29503, 92227]),
values=tensor([-0.0894, 0.5989, 0.0398, ..., 0.1235, -0.7698,
1.7527]), size=(100000, 100000), nnz=499980,
layout=torch.sparse_csr)
tensor([0.3186, 0.0161, 0.2456, ..., 0.9108, 0.3200, 0.6989])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499980
Density: 4.9998e-05
Time: 10.462877750396729 seconds

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pytorch/output_8core/altra_10_2_10_10000_0.0001.json Normal file
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{"CPU": "Altra", "ITERATIONS": 674430, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.27655839920044, "TIME_S_1KI": 0.015237398097950031, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 269.338226776123, "W": 26.38869527856713, "J_1KI": 0.39935682988022925, "W_1KI": 0.03912740429483731, "W_D": 16.88369527856713, "J_D": 172.32472086071965, "W_D_1KI": 0.02503402173474954, "J_D_1KI": 3.711878435827223e-05}

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pytorch/output_8core/altra_10_2_10_10000_0.0001.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, 0, 1, ..., 9998, 9999, 10000]),
col_indices=tensor([3164, 9580, 1349, ..., 981, 2279, 6382]),
values=tensor([-1.1170, -1.0404, -2.0258, ..., -0.0529, -1.2276,
0.7453]), size=(10000, 10000), nnz=10000,
layout=torch.sparse_csr)
tensor([0.2669, 0.8530, 0.1049, ..., 0.3093, 0.0150, 0.6871])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 10.27655839920044 seconds

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pytorch/output_8core/altra_10_2_10_10000_1e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 781565, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.538879156112671, "TIME_S_1KI": 0.013484328438597775, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 283.76676887512207, "W": 26.443221115401535, "J_1KI": 0.3630750722910085, "W_1KI": 0.03383368128741888, "W_D": 17.003221115401534, "J_D": 182.46449989318847, "W_D_1KI": 0.021755351270081866, "J_D_1KI": 2.783562630118015e-05}

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pytorch/output_8core/altra_10_2_10_10000_1e-05.output Normal file
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@ -0,0 +1,375 @@
/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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size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.7863, 0.2047, 0.8532, ..., 0.8457, 0.0751, 0.8874])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 10.538879156112671 seconds

1
pytorch/output_8core/altra_10_2_10_10000_5e-05.json Normal file
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{"CPU": "Altra", "ITERATIONS": 722194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.27481746673584, "TIME_S_1KI": 0.014227226294784836, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 275.34924281120294, "W": 26.588815836963942, "J_1KI": 0.3812676965070368, "W_1KI": 0.036816722150784895, "W_D": 16.973815836963944, "J_D": 175.7779423868656, "W_D_1KI": 0.023503124973295188, "J_D_1KI": 3.2544060146297514e-05}

16
pytorch/output_8core/altra_10_2_10_10000_5e-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, 0, 0, ..., 5000, 5000, 5000]),
col_indices=tensor([ 730, 1184, 6226, ..., 4450, 393, 9426]),
values=tensor([-0.6520, -0.7895, -0.9804, ..., -1.4162, 0.4906,
0.1947]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.0482, 0.3073, 0.6996, ..., 0.3017, 0.8775, 0.4557])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 10.27481746673584 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 383298, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.178744077682495, "TIME_S_1KI": 0.026555693162193635, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 286.18491299629216, "W": 27.046677974472292, "J_1KI": 0.7466381588119222, "W_1KI": 0.0705630553106781, "W_D": 17.451677974472293, "J_D": 184.65879423260694, "W_D_1KI": 0.04553031316227137, "J_D_1KI": 0.00011878567892937447}

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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, 5, ..., 39996, 39998, 39998]),
col_indices=tensor([ 4867, 15484, 7914, ..., 19697, 6735, 16833]),
values=tensor([-0.3077, -0.3059, 0.7735, ..., -2.0428, -1.4562,
-1.2098]), size=(20000, 20000), nnz=39998,
layout=torch.sparse_csr)
tensor([0.4051, 0.2592, 0.6408, ..., 0.6275, 0.0899, 0.2928])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39998
Density: 9.9995e-05
Time: 10.178744077682495 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 636816, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.367442607879639, "TIME_S_1KI": 0.01628012268517066, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 282.81412154197693, "W": 26.878661134925416, "J_1KI": 0.4441064947205738, "W_1KI": 0.04220789228745103, "W_D": 17.233661134925416, "J_D": 181.3305622017384, "W_D_1KI": 0.027062230118158805, "J_D_1KI": 4.249615292040213e-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, ..., 3999, 3999, 4000]),
col_indices=tensor([10060, 11713, 8237, ..., 6932, 14069, 757]),
values=tensor([ 0.8321, 0.1946, -0.2913, ..., -0.3362, -0.6210,
1.2498]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.6647, 0.8949, 0.3166, ..., 0.4160, 0.2645, 0.7939])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.367442607879639 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 487532, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 20000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.782274961471558, "TIME_S_1KI": 0.022116035381208942, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 299.7260921096802, "W": 27.648575396143613, "J_1KI": 0.6147823980983407, "W_1KI": 0.05671130386547675, "W_D": 17.963575396143614, "J_D": 194.7352504301072, "W_D_1KI": 0.036845941181591395, "J_D_1KI": 7.557645689224788e-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, ..., 19998, 19998, 20000]),
col_indices=tensor([ 2697, 3286, 9997, ..., 10197, 6648, 8484]),
values=tensor([ 1.6901, -0.6402, -0.1805, ..., 0.2027, -0.1868,
-0.5533]), size=(20000, 20000), nnz=20000,
layout=torch.sparse_csr)
tensor([0.7474, 0.1459, 0.7655, ..., 0.6283, 0.2273, 0.4477])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 20000
Density: 5e-05
Time: 10.782274961471558 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 112982, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249989, "MATRIX_DENSITY": 9.99956e-05, "TIME_S": 10.606346607208252, "TIME_S_1KI": 0.09387642816739172, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 295.4847658538818, "W": 28.4523730731245, "J_1KI": 2.6153260329422547, "W_1KI": 0.2518310268283842, "W_D": 18.9023730731245, "J_D": 196.3057094478607, "W_D_1KI": 0.16730428805583633, "J_D_1KI": 0.001480804801258929}

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, 2, 7, ..., 249977, 249984,
249989]),
col_indices=tensor([19392, 19516, 3969, ..., 9397, 13799, 18598]),
values=tensor([ 0.7235, -0.3416, 0.2990, ..., -1.3366, -2.0730,
1.1817]), size=(50000, 50000), nnz=249989,
layout=torch.sparse_csr)
tensor([0.4876, 0.3319, 0.9142, ..., 0.7843, 0.0179, 0.5206])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249989
Density: 9.99956e-05
Time: 10.606346607208252 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 284902, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.685346841812134, "TIME_S_1KI": 0.03750534163260396, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 285.9664697265625, "W": 26.488718415254077, "J_1KI": 1.0037362662479115, "W_1KI": 0.0929748419289934, "W_D": 16.893718415254078, "J_D": 182.38092685461044, "W_D_1KI": 0.05929659467204189, "J_D_1KI": 0.000208129794357505}

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, 1, ..., 25000, 25000, 25000]),
col_indices=tensor([12058, 39801, 6345, ..., 49764, 27098, 10943]),
values=tensor([ 1.6480, 0.4412, 1.3099, ..., 1.0150, 0.4807,
-0.5419]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.9117, 0.0733, 0.3474, ..., 0.2793, 0.5305, 0.3928])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 10.685346841812134 seconds

1
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@ -0,0 +1 @@
{"CPU": "Altra", "ITERATIONS": 144418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124998, "MATRIX_DENSITY": 4.99992e-05, "TIME_S": 10.171265840530396, "TIME_S_1KI": 0.07042934980771369, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 274.5023854827881, "W": 26.936643314017235, "J_1KI": 1.9007491135647085, "W_1KI": 0.18651860096398812, "W_D": 17.421643314017235, "J_D": 177.5381807219982, "W_D_1KI": 0.12063346199239176, "J_D_1KI": 0.0008353076624270641}

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, 2, 5, ..., 124993, 124996,
124998]),
col_indices=tensor([ 3460, 36222, 32292, ..., 37574, 12800, 47090]),
values=tensor([-0.2525, 0.9634, -1.4586, ..., -0.7907, 0.4392,
0.8238]), size=(50000, 50000), nnz=124998,
layout=torch.sparse_csr)
tensor([0.4631, 0.6797, 0.0082, ..., 0.6902, 0.2330, 0.3617])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124998
Density: 4.99992e-05
Time: 10.171265840530396 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 56154, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999940, "MATRIX_DENSITY": 9.9994e-05, "TIME_S": 10.565309524536133, "TIME_S_1KI": 0.18814883222096615, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1172.9214494276046, "W": 110.49, "J_1KI": 20.88758502382029, "W_1KI": 1.9676247462335716, "W_D": 90.96375, "J_D": 965.6379174166918, "W_D_1KI": 1.6198979591836735, "J_D_1KI": 0.0288474188692466}

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, 12, 27, ..., 999918, 999926,
999940]),
col_indices=tensor([10635, 12710, 47896, ..., 84196, 86725, 88253]),
values=tensor([-0.7477, 0.4189, -1.0168, ..., 0.1393, -1.2249,
-1.4894]), size=(100000, 100000), nnz=999940,
layout=torch.sparse_csr)
tensor([0.0132, 0.2560, 0.3006, ..., 0.6334, 0.2940, 0.6020])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 999940
Density: 9.9994e-05
Time: 10.565309524536133 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 140304, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 10.418423652648926, "TIME_S_1KI": 0.07425607005252113, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1049.3959311294554, "W": 102.85999999999999, "J_1KI": 7.479444143641346, "W_1KI": 0.7331223628691982, "W_D": 83.23624999999998, "J_D": 849.1909592890738, "W_D_1KI": 0.5932564288972516, "J_D_1KI": 0.004228364329578997}

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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, 0, 0, ..., 99996, 99996, 99999]),
col_indices=tensor([ 5909, 46249, 49873, ..., 51099, 64641, 73499]),
values=tensor([-1.0005, 1.1647, -0.5534, ..., -0.2931, 2.1579,
-0.4823]), size=(100000, 100000), nnz=99999,
layout=torch.sparse_csr)
tensor([0.9840, 0.9205, 0.1724, ..., 0.6954, 0.2921, 0.6740])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 99999
Density: 9.9999e-06
Time: 10.418423652648926 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 82055, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499987, "MATRIX_DENSITY": 4.99987e-05, "TIME_S": 10.827465534210205, "TIME_S_1KI": 0.13195375704357085, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1135.1707736134529, "W": 104.89, "J_1KI": 13.83426693819332, "W_1KI": 1.2782889525318384, "W_D": 85.5125, "J_D": 925.458011046052, "W_D_1KI": 1.0421363719456462, "J_D_1KI": 0.012700461543423877}

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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, 10, ..., 499977, 499983,
499987]),
col_indices=tensor([30997, 34024, 68742, ..., 39238, 86756, 88625]),
values=tensor([-0.3461, 1.0096, 1.0312, ..., -0.4452, -1.3530,
-0.7355]), size=(100000, 100000), nnz=499987,
layout=torch.sparse_csr)
tensor([0.7998, 0.1424, 0.0356, ..., 0.9066, 0.1670, 0.4921])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499987
Density: 4.99987e-05
Time: 10.827465534210205 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 478747, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9998, "MATRIX_DENSITY": 9.998e-05, "TIME_S": 11.3640615940094, "TIME_S_1KI": 0.023737092021483996, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1010.4165006351471, "W": 88.62, "J_1KI": 2.1105437749691323, "W_1KI": 0.18510820955536014, "W_D": 68.94625, "J_D": 786.1027833098174, "W_D_1KI": 0.14401395726761734, "J_D_1KI": 0.00030081432837723756}

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, 2, 4, ..., 9994, 9995, 9998]),
col_indices=tensor([3352, 5347, 7872, ..., 1075, 3060, 3215]),
values=tensor([-0.4642, -0.0165, 0.4848, ..., 0.2407, -0.0620,
1.1649]), size=(10000, 10000), nnz=9998,
layout=torch.sparse_csr)
tensor([0.2161, 0.2804, 0.3926, ..., 0.6876, 0.5385, 0.4336])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 9998
Density: 9.998e-05
Time: 11.3640615940094 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 543763, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.077085971832275, "TIME_S_1KI": 0.020371165327233143, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 933.9396756601333, "W": 85.66, "J_1KI": 1.7175491448666667, "W_1KI": 0.15753186590481513, "W_D": 65.82374999999999, "J_D": 717.6676596513389, "W_D_1KI": 0.1210522782903581, "J_D_1KI": 0.00022261955721584236}

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@ -0,0 +1,375 @@
/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, ..., 1000, 1000, 1000]),
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size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.9412, 0.2377, 0.9263, ..., 0.7791, 0.9612, 0.7836])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 11.077085971832275 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 557815, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.01402235031128, "TIME_S_1KI": 0.019744937569465288, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 990.0257138228417, "W": 87.87, "J_1KI": 1.774828059164493, "W_1KI": 0.1575253444242267, "W_D": 68.075, "J_D": 766.9967050015927, "W_D_1KI": 0.12203866873425777, "J_D_1KI": 0.00021877982616863613}

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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, ..., 4998, 4999, 5000]),
col_indices=tensor([ 962, 6463, 1315, ..., 3384, 7308, 7375]),
values=tensor([-1.3704, 0.8257, 0.1787, ..., -0.8045, 0.3437,
-0.5795]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.6135, 0.2745, 0.2257, ..., 0.0222, 0.8530, 0.9395])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 11.01402235031128 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 271123, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39996, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.34571099281311, "TIME_S_1KI": 0.03815873604531194, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 970.767766571045, "W": 93.06, "J_1KI": 3.5805437626872116, "W_1KI": 0.343239046484437, "W_D": 73.38625, "J_D": 765.5384269237519, "W_D_1KI": 0.270675117935402, "J_D_1KI": 0.0009983480484333754}

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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, 6, ..., 39990, 39994, 39996]),
col_indices=tensor([14238, 15850, 19198, ..., 14995, 9694, 19303]),
values=tensor([ 0.8573, -0.9595, -0.2158, ..., -1.3697, -0.3251,
-1.1161]), size=(20000, 20000), nnz=39996,
layout=torch.sparse_csr)
tensor([0.8621, 0.0100, 0.5156, ..., 0.4557, 0.2844, 0.5730])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39996
Density: 9.999e-05
Time: 10.34571099281311 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 421253, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.67978286743164, "TIME_S_1KI": 0.025352419727412364, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 931.7455441474915, "W": 88.4, "J_1KI": 2.211843106511981, "W_1KI": 0.20985013756578588, "W_D": 68.82125, "J_D": 725.3834053185583, "W_D_1KI": 0.16337272375508305, "J_D_1KI": 0.00038782566238123657}

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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, ..., 4000, 4000, 4000]),
col_indices=tensor([10299, 8742, 17691, ..., 732, 6544, 3957]),
values=tensor([ 1.1417, 0.5466, 0.4330, ..., -0.6116, -0.3136,
-0.8636]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.4727, 0.7162, 0.3857, ..., 0.9756, 0.0358, 0.6437])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.67978286743164 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 296049, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 20000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.487282514572144, "TIME_S_1KI": 0.035424144363170096, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 910.7233948850632, "W": 90.38, "J_1KI": 3.076258980388595, "W_1KI": 0.30528730041310725, "W_D": 70.03375, "J_D": 705.7023075518011, "W_D_1KI": 0.23656134626362527, "J_D_1KI": 0.0007990614603110474}

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, 3, ..., 19996, 19996, 20000]),
col_indices=tensor([ 9594, 17407, 19927, ..., 12343, 19403, 19542]),
values=tensor([-1.2753, 1.5696, -0.3609, ..., -0.9557, 0.3541,
1.4035]), size=(20000, 20000), nnz=20000,
layout=torch.sparse_csr)
tensor([0.4746, 0.1303, 0.6771, ..., 0.0166, 0.2071, 0.5823])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 20000
Density: 5e-05
Time: 10.487282514572144 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 164977, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249983, "MATRIX_DENSITY": 9.99932e-05, "TIME_S": 10.256412982940674, "TIME_S_1KI": 0.06216874463071018, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1082.1654234600067, "W": 102.49, "J_1KI": 6.559492677524786, "W_1KI": 0.6212381119792455, "W_D": 82.77374999999999, "J_D": 873.9866349899768, "W_D_1KI": 0.5017290288949368, "J_D_1KI": 0.003041205918976201}

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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, 6, 9, ..., 249973, 249980,
249983]),
col_indices=tensor([31108, 35040, 37902, ..., 16206, 40410, 49581]),
values=tensor([ 0.2289, -0.6532, 0.4374, ..., -0.3465, -0.2721,
-1.2308]), size=(50000, 50000), nnz=249983,
layout=torch.sparse_csr)
tensor([0.5939, 0.2881, 0.4014, ..., 0.1135, 0.5678, 0.4858])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249983
Density: 9.99932e-05
Time: 10.256412982940674 seconds

1
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "ITERATIONS": 219932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.079042673110962, "TIME_S_1KI": 0.045827995349066813, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1025.4395825886727, "W": 97.19000000000001, "J_1KI": 4.662530157451724, "W_1KI": 0.4419093174253861, "W_D": 69.5575, "J_D": 733.8925173979998, "W_D_1KI": 0.3162682101740538, "J_D_1KI": 0.00143802725466987}

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 ../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([37623, 15843, 14123, ..., 30324, 41135, 7403]),
values=tensor([ 0.7582, -0.5492, -3.0150, ..., -0.6906, 0.6477,
-0.0377]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.6487, 0.2049, 0.1238, ..., 0.0813, 0.3528, 0.4600])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 10.079042673110962 seconds

1
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{"CPU": "Epyc 7313P", "ITERATIONS": 159209, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124995, "MATRIX_DENSITY": 4.9998e-05, "TIME_S": 10.519323825836182, "TIME_S_1KI": 0.0660724194350582, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 1054.267113995552, "W": 101.45, "J_1KI": 6.621906512794831, "W_1KI": 0.6372127203864103, "W_D": 81.7225, "J_D": 849.2591840660572, "W_D_1KI": 0.5133032680313299, "J_D_1KI": 0.0032240844929076235}

17
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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, 2, 5, ..., 124993, 124993,
124995]),
col_indices=tensor([ 2905, 27517, 17092, ..., 5202, 31385, 39133]),
values=tensor([ 0.3277, 1.7264, -0.7414, ..., -1.2741, 1.4153,
-1.1079]), size=(50000, 50000), nnz=124995,
layout=torch.sparse_csr)
tensor([0.7367, 0.2293, 0.5341, ..., 0.2055, 0.1490, 0.3781])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124995
Density: 4.9998e-05
Time: 10.519323825836182 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 23675, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999960, "MATRIX_DENSITY": 9.9996e-05, "TIME_S": 10.017890453338623, "TIME_S_1KI": 0.42314215220015305, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 782.8562486886979, "W": 78.54, "J_1KI": 33.06678980733677, "W_1KI": 3.317423442449842, "W_D": 68.92125000000001, "J_D": 686.9802804932, "W_D_1KI": 2.9111404435058086, "J_D_1KI": 0.12296263752928442}

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, 9, 23, ..., 999933, 999947,
999960]),
col_indices=tensor([ 424, 4792, 5050, ..., 79901, 81580, 95361]),
values=tensor([ 0.4958, -0.1718, -2.5022, ..., 0.1973, 0.9139,
0.2644]), size=(100000, 100000), nnz=999960,
layout=torch.sparse_csr)
tensor([0.5419, 0.1775, 0.7763, ..., 0.2696, 0.9298, 0.5219])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 999960
Density: 9.9996e-05
Time: 10.017890453338623 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 70568, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.367664813995361, "TIME_S_1KI": 0.14691736784371615, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 789.4405226111413, "W": 75.93, "J_1KI": 11.186947661987604, "W_1KI": 1.0759834485885955, "W_D": 66.35125000000001, "J_D": 689.8507240340115, "W_D_1KI": 0.9402455787325702, "J_D_1KI": 0.01332396523541223}

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, 3, ..., 99999, 100000,
100000]),
col_indices=tensor([91400, 94838, 77872, ..., 72812, 5846, 19852]),
values=tensor([ 0.2160, -0.4558, 1.5341, ..., 1.0751, -0.7979,
0.0744]), size=(100000, 100000), nnz=100000,
layout=torch.sparse_csr)
tensor([0.4788, 0.1878, 0.5643, ..., 0.6576, 0.4190, 0.4714])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.367664813995361 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 39760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499991, "MATRIX_DENSITY": 4.99991e-05, "TIME_S": 10.557223320007324, "TIME_S_1KI": 0.2655237253522969, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 874.8044866871833, "W": 83.21, "J_1KI": 22.002124916679662, "W_1KI": 2.0928068410462775, "W_D": 73.28125, "J_D": 770.4214191809297, "W_D_1KI": 1.8430897887323943, "J_D_1KI": 0.046355376980191}

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, 3, 7, ..., 499977, 499981,
499991]),
col_indices=tensor([ 6493, 51721, 52310, ..., 87324, 94948, 96733]),
values=tensor([ 0.2000, 0.1551, 1.4811, ..., -1.0655, 0.9511,
-2.2930]), size=(100000, 100000), nnz=499991,
layout=torch.sparse_csr)
tensor([0.2649, 0.2615, 0.0417, ..., 0.6463, 0.6188, 0.8502])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([100000, 100000])
Size: 10000000000
NNZ: 499991
Density: 4.99991e-05
Time: 10.557223320007324 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 443098, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.363812923431396, "TIME_S_1KI": 0.023389437378258073, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 728.5208771610261, "W": 69.26, "J_1KI": 1.6441529349286752, "W_1KI": 0.15630853671196893, "W_D": 59.79750000000001, "J_D": 628.9882638180256, "W_D_1KI": 0.13495321576716662, "J_D_1KI": 0.00030456742248253577}

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, 3, ..., 10000, 10000, 10000]),
col_indices=tensor([9325, 7184, 9788, ..., 8868, 9325, 5370]),
values=tensor([-0.8970, 0.5991, 0.0641, ..., -0.0698, 0.9688,
-3.4153]), size=(10000, 10000), nnz=10000,
layout=torch.sparse_csr)
tensor([0.8166, 0.2262, 0.2530, ..., 0.9896, 0.2502, 0.0838])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 10.363812923431396 seconds

1
pytorch/output_8core/xeon_4216_10_2_10_10000_1e-05.json Normal file
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 542659, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.456323862075806, "TIME_S_1KI": 0.019268682288648684, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 688.9364667081833, "W": 67.82, "J_1KI": 1.2695568795655896, "W_1KI": 0.12497719562377108, "W_D": 58.177499999999995, "J_D": 590.984979237914, "W_D_1KI": 0.10720820994399798, "J_D_1KI": 0.00019756091752647236}

375
pytorch/output_8core/xeon_4216_10_2_10_10000_1e-05.output Normal file
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@ -0,0 +1,375 @@
/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, ..., 1000, 1000, 1000]),
col_indices=tensor([1852, 2505, 1291, 995, 9669, 4473, 2992, 9344, 8383,
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-9.2248e-02, -4.8814e-01, -5.9692e-01, 6.4085e-01,
7.4396e-01, 1.8791e-01, 5.2013e-01, 1.1617e+00,
7.5987e-01, -1.0077e+00, -2.1012e+00, 1.7359e-01,
2.9332e-01, -8.0365e-01, -3.8712e-01, 2.6136e+00,
-2.9833e-01, -1.4024e+00, -4.3362e-01, -1.6925e-01,
-6.6657e-01, 6.7348e-01, 3.7334e-02, 1.2098e-01]),
size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
tensor([0.8051, 0.7193, 0.7310, ..., 0.3503, 0.5164, 0.5572])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 10.456323862075806 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 487645, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.432088851928711, "TIME_S_1KI": 0.021392793634567586, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 707.2562955856323, "W": 68.32, "J_1KI": 1.4503507584116155, "W_1KI": 0.1401019184037568, "W_D": 58.7225, "J_D": 607.901900139451, "W_D_1KI": 0.12042059284930635, "J_D_1KI": 0.00024694315095880477}

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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, ..., 5000, 5000, 5000]),
col_indices=tensor([3725, 7553, 8222, ..., 6344, 7639, 6260]),
values=tensor([ 0.8432, -1.0427, -0.2190, ..., 0.4959, -0.1674,
-0.0937]), size=(10000, 10000), nnz=5000,
layout=torch.sparse_csr)
tensor([0.7163, 0.8365, 0.1620, ..., 0.0528, 0.7355, 0.5159])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([10000, 10000])
Size: 100000000
NNZ: 5000
Density: 5e-05
Time: 10.432088851928711 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 271193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39996, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.380186557769775, "TIME_S_1KI": 0.03827601213073263, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 734.2139717435838, "W": 70.93, "J_1KI": 2.707348536811731, "W_1KI": 0.2615480488065695, "W_D": 60.87500000000001, "J_D": 630.1321800351144, "W_D_1KI": 0.22447113310446806, "J_D_1KI": 0.0008277172829109456}

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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, 2, ..., 39992, 39996, 39996]),
col_indices=tensor([ 7037, 18637, 10167, ..., 8957, 15554, 19711]),
values=tensor([ 0.7638, 0.4331, 0.5708, ..., 0.9268, -0.8672,
-0.7994]), size=(20000, 20000), nnz=39996,
layout=torch.sparse_csr)
tensor([0.7033, 0.4906, 0.3942, ..., 0.3649, 0.3820, 0.3964])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 39996
Density: 9.999e-05
Time: 10.380186557769775 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 425538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.612646102905273, "TIME_S_1KI": 0.024939361708954957, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 733.1615182805061, "W": 69.61, "J_1KI": 1.7229049304186845, "W_1KI": 0.16358116078940071, "W_D": 60.0125, "J_D": 632.076650133729, "W_D_1KI": 0.1410273583087762, "J_D_1KI": 0.000331409552869018}

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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, ..., 4000, 4000, 4000]),
col_indices=tensor([ 4428, 1606, 8949, ..., 7037, 7619, 15417]),
values=tensor([ 0.1462, 0.3661, 0.8472, ..., 0.9410, 1.1193,
-1.0240]), size=(20000, 20000), nnz=4000,
layout=torch.sparse_csr)
tensor([0.1186, 0.0648, 0.1914, ..., 0.9609, 0.8460, 0.4234])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 4000
Density: 1e-05
Time: 10.612646102905273 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 327790, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.633241653442383, "TIME_S_1KI": 0.03243918866787389, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 758.1797456002236, "W": 71.09, "J_1KI": 2.313004501663332, "W_1KI": 0.2168766588364502, "W_D": 61.556250000000006, "J_D": 656.5016453102231, "W_D_1KI": 0.18779172641020167, "J_D_1KI": 0.0005729025486140568}

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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, 1, ..., 19998, 19998, 19999]),
col_indices=tensor([17332, 19069, 8431, ..., 11082, 18916, 417]),
values=tensor([-2.0256, -0.3508, -0.5357, ..., 1.0581, 0.1092,
1.0328]), size=(20000, 20000), nnz=19999,
layout=torch.sparse_csr)
tensor([0.6263, 0.4138, 0.5906, ..., 0.1013, 0.0614, 0.9531])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([20000, 20000])
Size: 400000000
NNZ: 19999
Density: 4.99975e-05
Time: 10.633241653442383 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 86156, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249983, "MATRIX_DENSITY": 9.99932e-05, "TIME_S": 10.060947179794312, "TIME_S_1KI": 0.11677593179574623, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 789.3654530024528, "W": 78.13, "J_1KI": 9.162048528279549, "W_1KI": 0.9068434003435628, "W_D": 68.57124999999999, "J_D": 692.7911918494104, "W_D_1KI": 0.7958963972329263, "J_D_1KI": 0.009237852235861998}

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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, 5, 15, ..., 249974, 249977,
249983]),
col_indices=tensor([ 9971, 12071, 16397, ..., 21429, 22054, 49364]),
values=tensor([-0.1463, 0.4247, 0.1497, ..., -0.0494, -0.8218,
-0.6361]), size=(50000, 50000), nnz=249983,
layout=torch.sparse_csr)
tensor([0.8616, 0.6800, 0.3985, ..., 0.2192, 0.2669, 0.8530])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 249983
Density: 9.99932e-05
Time: 10.060947179794312 seconds

1
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@ -0,0 +1 @@
{"CPU": "Xeon 4216", "ITERATIONS": 159448, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.552724838256836, "TIME_S_1KI": 0.0661828611099345, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 729.3027632951736, "W": 71.05, "J_1KI": 4.573922302538594, "W_1KI": 0.4455998193768501, "W_D": 61.05499999999999, "J_D": 626.707673652172, "W_D_1KI": 0.38291480608097933, "J_D_1KI": 0.0024015027223983952}

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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, 0, 0, ..., 24996, 24997, 25000]),
col_indices=tensor([11148, 16126, 18025, ..., 26243, 35537, 45208]),
values=tensor([ 1.2117, 2.1451, 1.4317, ..., -1.1109, 2.1282,
-0.5315]), size=(50000, 50000), nnz=25000,
layout=torch.sparse_csr)
tensor([0.0712, 0.6707, 0.1386, ..., 0.8073, 0.6140, 0.3720])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 25000
Density: 1e-05
Time: 10.552724838256836 seconds

1
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{"CPU": "Xeon 4216", "ITERATIONS": 106035, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124996, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.573558330535889, "TIME_S_1KI": 0.09971762465729135, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 803.3346718859673, "W": 75.61, "J_1KI": 7.57612742854687, "W_1KI": 0.7130664403263074, "W_D": 65.60875, "J_D": 697.0742448630929, "W_D_1KI": 0.6187461687178761, "J_D_1KI": 0.005835301256357581}

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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, 2, 9, ..., 124991, 124993,
124996]),
col_indices=tensor([10594, 40246, 1820, ..., 31632, 33399, 36136]),
values=tensor([-0.5712, 0.9018, -1.2338, ..., 0.9174, 0.3466,
-0.5163]), size=(50000, 50000), nnz=124996,
layout=torch.sparse_csr)
tensor([0.0416, 0.6695, 0.0944, ..., 0.0814, 0.2859, 0.7324])
Matrix: synthetic
Matrix: csr
Shape: torch.Size([50000, 50000])
Size: 2500000000
NNZ: 124996
Density: 4.99984e-05
Time: 10.573558330535889 seconds