From 0cbb446bb5368aa790c62ef60a84403913fd8068 Mon Sep 17 00:00:00 2001 From: cephi Date: Thu, 12 Dec 2024 21:56:03 -0500 Subject: [PATCH] 1core runs --- .../altra_10_2_10_100000_0.0001.json | 1 + .../altra_10_2_10_100000_0.0001.output | 17 + .../altra_10_2_10_100000_1e-05.json | 1 + .../altra_10_2_10_100000_1e-05.output | 16 + .../altra_10_2_10_100000_5e-05.json | 1 + .../altra_10_2_10_100000_5e-05.output | 17 + .../altra_10_2_10_10000_0.0001.json | 1 + .../altra_10_2_10_10000_0.0001.output | 16 + .../altra_10_2_10_10000_1e-05.json | 1 + .../altra_10_2_10_10000_1e-05.output | 375 ++++++++++++++++++ .../altra_10_2_10_10000_5e-05.json | 1 + .../altra_10_2_10_10000_5e-05.output | 16 + .../altra_10_2_10_20000_0.0001.json | 1 + .../altra_10_2_10_20000_0.0001.output | 16 + .../altra_10_2_10_20000_1e-05.json | 1 + .../altra_10_2_10_20000_1e-05.output | 16 + .../altra_10_2_10_20000_5e-05.json | 1 + .../altra_10_2_10_20000_5e-05.output | 16 + .../altra_10_2_10_50000_0.0001.json | 1 + 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16 + .../epyc_7313p_10_2_10_20000_5e-05.json | 1 + .../epyc_7313p_10_2_10_20000_5e-05.output | 16 + .../epyc_7313p_10_2_10_50000_0.0001.json | 1 + .../epyc_7313p_10_2_10_50000_0.0001.output | 17 + .../epyc_7313p_10_2_10_50000_1e-05.json | 1 + .../epyc_7313p_10_2_10_50000_1e-05.output | 16 + .../epyc_7313p_10_2_10_50000_5e-05.json | 1 + .../epyc_7313p_10_2_10_50000_5e-05.output | 17 + .../xeon_4216_10_2_10_100000_0.0001.json | 1 + .../xeon_4216_10_2_10_100000_0.0001.output | 17 + .../xeon_4216_10_2_10_100000_1e-05.json | 1 + .../xeon_4216_10_2_10_100000_1e-05.output | 16 + .../xeon_4216_10_2_10_100000_5e-05.json | 1 + .../xeon_4216_10_2_10_100000_5e-05.output | 17 + .../xeon_4216_10_2_10_10000_0.0001.json | 1 + .../xeon_4216_10_2_10_10000_0.0001.output | 16 + .../xeon_4216_10_2_10_10000_1e-05.json | 1 + .../xeon_4216_10_2_10_10000_1e-05.output | 375 ++++++++++++++++++ .../xeon_4216_10_2_10_10000_5e-05.json | 1 + .../xeon_4216_10_2_10_10000_5e-05.output | 16 + 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mode 100644 pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.output diff --git a/pytorch/output_1core/altra_10_2_10_100000_0.0001.json b/pytorch/output_1core/altra_10_2_10_100000_0.0001.json new file mode 100644 index 0000000..562646f --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 4591, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999953, "MATRIX_DENSITY": 9.99953e-05, "TIME_S": 10.515824556350708, "TIME_S_1KI": 2.290530288902354, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 273.044737739563, "W": 26.087881391815785, "J_1KI": 59.473913687554564, "W_1KI": 5.682396295320363, "W_D": 16.547881391815785, "J_D": 173.19581712722783, "W_D_1KI": 3.604417641432321, "J_D_1KI": 0.7851051277352038} diff --git a/pytorch/output_1core/altra_10_2_10_100000_0.0001.output b/pytorch/output_1core/altra_10_2_10_100000_0.0001.output new file mode 100644 index 0000000..cc0ebe7 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_0.0001.output @@ -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, 5, 11, ..., 999928, 999936, + 999953]), + col_indices=tensor([70868, 74790, 90634, ..., 84755, 88122, 91648]), + values=tensor([ 0.4036, 1.0126, 0.6579, ..., 0.6347, -1.2417, + 0.8926]), size=(100000, 100000), nnz=999953, + layout=torch.sparse_csr) +tensor([0.1903, 0.5682, 0.1772, ..., 0.5583, 0.7604, 0.7111]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999953 +Density: 9.99953e-05 +Time: 10.515824556350708 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_100000_1e-05.json b/pytorch/output_1core/altra_10_2_10_100000_1e-05.json new file mode 100644 index 0000000..2b40efd --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 14108, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 10.44682264328003, "TIME_S_1KI": 0.7404892715679068, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 262.4202471351623, "W": 25.1855074061394, "J_1KI": 18.60081139319268, "W_1KI": 1.7851933233725121, "W_D": 15.715507406139402, "J_D": 163.747637515068, "W_D_1KI": 1.1139429689636662, "J_D_1KI": 0.07895824843802567} diff --git a/pytorch/output_1core/altra_10_2_10_100000_1e-05.output b/pytorch/output_1core/altra_10_2_10_100000_1e-05.output new file mode 100644 index 0000000..99a5539 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_1e-05.output @@ -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, 0, ..., 99999, 99999, 99999]), + col_indices=tensor([54792, 14606, 48431, ..., 1584, 20255, 32244]), + values=tensor([ 0.2168, 2.2061, -2.0429, ..., -0.0096, -0.3108, + -0.6421]), size=(100000, 100000), nnz=99999, + layout=torch.sparse_csr) +tensor([0.3823, 0.7563, 0.1745, ..., 0.6521, 0.2295, 0.6307]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 99999 +Density: 9.9999e-06 +Time: 10.44682264328003 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_100000_5e-05.json b/pytorch/output_1core/altra_10_2_10_100000_5e-05.json new file mode 100644 index 0000000..302def7 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 7167, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499988, "MATRIX_DENSITY": 4.99988e-05, "TIME_S": 10.57473087310791, "TIME_S_1KI": 1.4754752160050104, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 266.5949284362793, "W": 25.47428412267614, "J_1KI": 37.19756222077289, "W_1KI": 3.5543859526546866, "W_D": 16.09928412267614, "J_D": 168.48314472317693, "W_D_1KI": 2.2463072586404547, "J_D_1KI": 0.3134236442919568} diff --git a/pytorch/output_1core/altra_10_2_10_100000_5e-05.output b/pytorch/output_1core/altra_10_2_10_100000_5e-05.output new file mode 100644 index 0000000..5d15d9c --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_100000_5e-05.output @@ -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, 5, 11, ..., 499974, 499983, + 499988]), + col_indices=tensor([15958, 18715, 52510, ..., 53360, 79192, 95801]), + values=tensor([ 0.1102, -0.4713, 0.0240, ..., 1.5482, 1.0325, + 1.6719]), size=(100000, 100000), nnz=499988, + layout=torch.sparse_csr) +tensor([0.0554, 0.4470, 0.9893, ..., 0.6567, 0.6732, 0.5744]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499988 +Density: 4.99988e-05 +Time: 10.57473087310791 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_10000_0.0001.json b/pytorch/output_1core/altra_10_2_10_10000_0.0001.json new file mode 100644 index 0000000..d5afbfd --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 175804, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.573810815811157, "TIME_S_1KI": 0.0601454507053944, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 214.99873767852785, "W": 20.46577514143255, "J_1KI": 1.222945653560373, "W_1KI": 0.11641245444604532, "W_D": 10.405775141432551, "J_D": 109.31560151100159, "W_D_1KI": 0.059189638127872805, "J_D_1KI": 0.0003366797008479489} diff --git a/pytorch/output_1core/altra_10_2_10_10000_0.0001.output b/pytorch/output_1core/altra_10_2_10_10000_0.0001.output new file mode 100644 index 0000000..169d61b --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_10000_0.0001.output @@ -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, ..., 9998, 9998, 10000]), + col_indices=tensor([5607, 1451, 5668, ..., 3387, 9491, 9524]), + values=tensor([-1.8541, 0.1963, 1.5424, ..., -0.3045, -0.4980, + 0.1016]), size=(10000, 10000), nnz=10000, + layout=torch.sparse_csr) +tensor([0.8961, 0.6841, 0.9747, ..., 0.8377, 0.2079, 0.9912]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.573810815811157 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_10000_1e-05.json b/pytorch/output_1core/altra_10_2_10_10000_1e-05.json new file mode 100644 index 0000000..0e72603 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 416649, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.149910688400269, "TIME_S_1KI": 0.024360818550867202, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 217.88464315414433, "W": 21.251971491655738, "J_1KI": 0.522945316451364, "W_1KI": 0.051006894272290916, "W_D": 11.821971491655738, "J_D": 121.20409821033482, "W_D_1KI": 0.028373934634802287, "J_D_1KI": 6.810033057754197e-05} diff --git a/pytorch/output_1core/altra_10_2_10_10000_1e-05.output b/pytorch/output_1core/altra_10_2_10_10000_1e-05.output new file mode 100644 index 0000000..d95b29c --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_10000_1e-05.output @@ -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, ..., 1000, 1000, 1000]), + col_indices=tensor([ 838, 471, 8264, 1933, 3490, 7261, 7284, 6998, 2605, + 6095, 4691, 5234, 4206, 6889, 2260, 4619, 4582, 7441, + 8584, 5173, 5710, 7393, 3829, 1411, 8599, 9358, 3880, + 5288, 7826, 470, 6578, 3784, 8496, 5718, 9937, 7145, + 7947, 5701, 2585, 8298, 7820, 7940, 6905, 8342, 1771, + 6193, 9824, 2790, 987, 326, 5223, 5114, 7951, 9055, + 5808, 4607, 1220, 238, 576, 4862, 2157, 2085, 1520, + 8969, 5790, 1872, 6800, 1026, 4390, 1668, 7994, 4809, + 2257, 5557, 3183, 537, 4913, 5571, 4112, 1179, 7062, + 4471, 6404, 8277, 3053, 1499, 3552, 7119, 4976, 7335, + 390, 2533, 2953, 1440, 2039, 6469, 3145, 8189, 224, + 3491, 6602, 2839, 6424, 4100, 2799, 1949, 7285, 8640, + 5367, 4180, 8857, 9367, 9062, 9855, 7430, 5434, 7498, + 752, 4909, 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"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.347587585449219, "TIME_S_1KI": 0.04448776660382133, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 211.02262233734132, "W": 20.325620076607365, "J_1KI": 0.90725737696304, "W_1KI": 0.08738669130161296, "W_D": 10.790620076607365, "J_D": 112.02929783344271, "W_D_1KI": 0.046392512603968136, "J_D_1KI": 0.00019945704792027368} diff --git a/pytorch/output_1core/altra_10_2_10_10000_5e-05.output b/pytorch/output_1core/altra_10_2_10_10000_5e-05.output new file mode 100644 index 0000000..1efc0de --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_10000_5e-05.output @@ -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, ..., 5000, 5000, 5000]), + col_indices=tensor([2868, 7932, 249, ..., 7975, 3408, 5305]), + values=tensor([ 2.4798, 0.2185, 0.9681, ..., 0.4257, -1.2815, + -1.0756]), size=(10000, 10000), nnz=5000, + layout=torch.sparse_csr) +tensor([0.4898, 0.4327, 0.8103, ..., 0.9373, 0.0546, 0.0972]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 5000 +Density: 5e-05 +Time: 10.347587585449219 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_20000_0.0001.json b/pytorch/output_1core/altra_10_2_10_20000_0.0001.json new file mode 100644 index 0000000..8e1083c --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 59076, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.14804720878601, "TIME_S_1KI": 0.171779524828797, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 218.31626580238344, "W": 21.429018605550787, "J_1KI": 3.695515366686699, "W_1KI": 0.36273645144476246, "W_D": 11.904018605550787, "J_D": 121.27671069979668, "W_D_1KI": 0.2015034634293247, "J_D_1KI": 0.0034109192130361687} diff --git a/pytorch/output_1core/altra_10_2_10_20000_0.0001.output b/pytorch/output_1core/altra_10_2_10_20000_0.0001.output new file mode 100644 index 0000000..dd01570 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_0.0001.output @@ -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, 3, ..., 39998, 39998, 39998]), + col_indices=tensor([ 3923, 4463, 7742, ..., 473, 4406, 15797]), + values=tensor([-1.0869, 0.4083, -0.1118, ..., -0.2764, 0.8806, + -0.5343]), size=(20000, 20000), nnz=39998, + layout=torch.sparse_csr) +tensor([0.4042, 0.1033, 0.9915, ..., 0.8010, 0.0420, 0.3163]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39998 +Density: 9.9995e-05 +Time: 10.14804720878601 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_20000_1e-05.json b/pytorch/output_1core/altra_10_2_10_20000_1e-05.json new file mode 100644 index 0000000..2400c0e --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 167541, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.27948784828186, "TIME_S_1KI": 0.06135505845304648, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 206.614094543457, "W": 20.665713760266534, "J_1KI": 1.2332151207373538, "W_1KI": 0.12334720313395846, "W_D": 11.170713760266535, "J_D": 111.6838709640503, "W_D_1KI": 0.0666745080921478, "J_D_1KI": 0.0003979593537829415} diff --git a/pytorch/output_1core/altra_10_2_10_20000_1e-05.output b/pytorch/output_1core/altra_10_2_10_20000_1e-05.output new file mode 100644 index 0000000..791257c --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_1e-05.output @@ -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, 0, ..., 4000, 4000, 4000]), + col_indices=tensor([ 1080, 8729, 18175, ..., 17993, 9127, 87]), + values=tensor([ 0.4773, 1.2327, -0.1343, ..., 0.2605, 1.0707, + 0.2212]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.1046, 0.3432, 0.0078, ..., 0.4521, 0.7459, 0.6533]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 10.27948784828186 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_20000_5e-05.json b/pytorch/output_1core/altra_10_2_10_20000_5e-05.json new file mode 100644 index 0000000..e3d51b0 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 77166, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.135813474655151, "TIME_S_1KI": 0.1313507694406235, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 212.44719322204588, "W": 20.92446157139477, "J_1KI": 2.753119161574345, "W_1KI": 0.27116167186837165, "W_D": 11.469461571394769, "J_D": 116.45006540775297, "W_D_1KI": 0.1486336154704762, "J_D_1KI": 0.0019261542061332223} diff --git a/pytorch/output_1core/altra_10_2_10_20000_5e-05.output b/pytorch/output_1core/altra_10_2_10_20000_5e-05.output new file mode 100644 index 0000000..0d020d3 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_20000_5e-05.output @@ -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, 3, ..., 19998, 19998, 19999]), + col_indices=tensor([15328, 1557, 7791, ..., 7811, 7304, 544]), + values=tensor([-0.0434, 0.6146, 0.0336, ..., 2.3152, 2.2408, + -0.3543]), size=(20000, 20000), nnz=19999, + layout=torch.sparse_csr) +tensor([0.6138, 0.3544, 0.9837, ..., 0.2956, 0.2755, 0.2504]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 19999 +Density: 4.99975e-05 +Time: 10.135813474655151 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_50000_0.0001.json b/pytorch/output_1core/altra_10_2_10_50000_0.0001.json new file mode 100644 index 0000000..b234f92 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 16259, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249989, "MATRIX_DENSITY": 9.99956e-05, "TIME_S": 10.448587656021118, "TIME_S_1KI": 0.6426340891826754, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 265.63406348228455, "W": 25.74416236713474, "J_1KI": 16.337663047068364, "W_1KI": 1.5833791971913858, "W_D": 16.45916236713474, "J_D": 169.82934300780298, "W_D_1KI": 1.0123108658056916, "J_D_1KI": 0.06226156994930141} diff --git a/pytorch/output_1core/altra_10_2_10_50000_0.0001.output b/pytorch/output_1core/altra_10_2_10_50000_0.0001.output new file mode 100644 index 0000000..250c7f5 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_0.0001.output @@ -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, 4, 9, ..., 249983, 249986, + 249989]), + col_indices=tensor([ 2109, 4915, 9069, ..., 21226, 23887, 48661]), + values=tensor([-0.5327, -2.0009, -0.4522, ..., 0.2326, -0.6829, + 0.9097]), size=(50000, 50000), nnz=249989, + layout=torch.sparse_csr) +tensor([0.3317, 0.4011, 0.1693, ..., 0.4876, 0.5926, 0.5720]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249989 +Density: 9.99956e-05 +Time: 10.448587656021118 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_50000_1e-05.json b/pytorch/output_1core/altra_10_2_10_50000_1e-05.json new file mode 100644 index 0000000..e210087 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 38882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.414772033691406, "TIME_S_1KI": 0.2678558724780466, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 230.91071128845212, "W": 22.283717353358732, "J_1KI": 5.938756012768174, "W_1KI": 0.5731113973910481, "W_D": 12.918717353358732, "J_D": 133.86771002769467, "W_D_1KI": 0.33225444558815725, "J_D_1KI": 0.008545199464743513} diff --git a/pytorch/output_1core/altra_10_2_10_50000_1e-05.output b/pytorch/output_1core/altra_10_2_10_50000_1e-05.output new file mode 100644 index 0000000..4abd07f --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_1e-05.output @@ -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, 0, ..., 24999, 25000, 25000]), + col_indices=tensor([22311, 35881, 31128, ..., 24299, 37246, 44423]), + values=tensor([ 1.1649, -0.3542, -0.8853, ..., 0.1551, 0.1696, + -2.7590]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.4524, 0.0084, 0.2512, ..., 0.5914, 0.1409, 0.6936]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.414772033691406 seconds + diff --git a/pytorch/output_1core/altra_10_2_10_50000_5e-05.json b/pytorch/output_1core/altra_10_2_10_50000_5e-05.json new file mode 100644 index 0000000..00d9524 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 21272, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124999, "MATRIX_DENSITY": 4.99996e-05, "TIME_S": 10.406851530075073, "TIME_S_1KI": 0.48922769509566916, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 269.6182607269287, "W": 25.94493503699868, "J_1KI": 12.674796010103833, "W_1KI": 1.2196753966246088, "W_D": 16.629935036998678, "J_D": 172.81732077121734, "W_D_1KI": 0.7817758103139656, "J_D_1KI": 0.03675140138745608} diff --git a/pytorch/output_1core/altra_10_2_10_50000_5e-05.output b/pytorch/output_1core/altra_10_2_10_50000_5e-05.output new file mode 100644 index 0000000..19eab64 --- /dev/null +++ b/pytorch/output_1core/altra_10_2_10_50000_5e-05.output @@ -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, ..., 124997, 124998, + 124999]), + col_indices=tensor([34758, 37051, 4283, ..., 36902, 8930, 21854]), + values=tensor([ 0.2027, -0.2421, -0.0122, ..., 2.0567, 0.6418, + -1.9333]), size=(50000, 50000), nnz=124999, + layout=torch.sparse_csr) +tensor([0.7323, 0.4458, 0.6264, ..., 0.0699, 0.5750, 0.3790]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124999 +Density: 4.99996e-05 +Time: 10.406851530075073 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.json b/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.json new file mode 100644 index 0000000..104f48f --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 7096, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999948, "MATRIX_DENSITY": 9.99948e-05, "TIME_S": 10.168954849243164, "TIME_S_1KI": 1.43305451652243, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 672.870105085373, "W": 66.03, "J_1KI": 94.82385922849113, "W_1KI": 9.305242390078917, "W_D": 46.44625, "J_D": 473.3044543135166, "W_D_1KI": 6.545412908680947, "J_D_1KI": 0.9224088090023883} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.output b/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.output new file mode 100644 index 0000000..00e70da --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_0.0001.output @@ -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, 8, 17, ..., 999931, 999938, + 999948]), + col_indices=tensor([ 4524, 19684, 22420, ..., 85904, 94042, 95795]), + values=tensor([-1.3594, -0.6760, 1.5198, ..., -0.4902, -0.6733, + -0.9072]), size=(100000, 100000), nnz=999948, + layout=torch.sparse_csr) +tensor([0.5407, 0.5422, 0.8589, ..., 0.3802, 0.1065, 0.2777]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999948 +Density: 9.99948e-05 +Time: 10.168954849243164 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.json new file mode 100644 index 0000000..0a16760 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 15524, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.355973243713379, "TIME_S_1KI": 0.6670943857068654, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 670.6344871044159, "W": 64.68, "J_1KI": 43.199851011621746, "W_1KI": 4.166451945374904, "W_D": 45.22375000000001, "J_D": 468.90238692313443, "W_D_1KI": 2.9131506055140433, "J_D_1KI": 0.18765463833509685} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.output new file mode 100644 index 0000000..8642229 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_1e-05.output @@ -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, 4, ..., 99998, 99999, + 100000]), + col_indices=tensor([52367, 87862, 40992, ..., 72156, 81616, 61830]), + values=tensor([-1.2130, 0.3871, 0.5809, ..., 0.0845, 0.0061, + 0.2524]), size=(100000, 100000), nnz=100000, + layout=torch.sparse_csr) +tensor([0.1658, 0.3689, 0.6232, ..., 0.0281, 0.1230, 0.4103]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.355973243713379 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.json new file mode 100644 index 0000000..ea9783b --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 9636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499985, "MATRIX_DENSITY": 4.99985e-05, "TIME_S": 10.401328325271606, "TIME_S_1KI": 1.0794238610701128, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 675.1464500951768, "W": 65.51, "J_1KI": 70.06501142540232, "W_1KI": 6.798464092984641, "W_D": 45.37875000000001, "J_D": 467.67366771876823, "W_D_1KI": 4.709293275217934, "J_D_1KI": 0.48871868775611604} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.output new file mode 100644 index 0000000..a80f1ba --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_100000_5e-05.output @@ -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, 8, 17, ..., 499979, 499983, + 499985]), + col_indices=tensor([ 5105, 20642, 52866, ..., 97652, 88458, 89695]), + values=tensor([ 0.4983, 1.2371, 1.2262, ..., 0.2227, -0.8154, + -1.7583]), size=(100000, 100000), nnz=499985, + layout=torch.sparse_csr) +tensor([0.6479, 0.3012, 0.7174, ..., 0.8126, 0.2989, 0.7422]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499985 +Density: 4.99985e-05 +Time: 10.401328325271606 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.json b/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.json new file mode 100644 index 0000000..5f34a52 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 377024, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9999, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.560901880264282, "TIME_S_1KI": 0.028011219127334817, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 693.101114051342, "W": 65.39, "J_1KI": 1.8383474634276387, "W_1KI": 0.1734372347648956, "W_D": 46.042500000000004, "J_D": 488.02734429895884, "W_D_1KI": 0.1221208729417756, "J_D_1KI": 0.0003239074248370809} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.output b/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.output new file mode 100644 index 0000000..9a9ca16 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_10000_0.0001.output @@ -0,0 +1,16 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9998, 9999, 9999]), + col_indices=tensor([8074, 8881, 2785, ..., 4144, 527, 3612]), + values=tensor([-1.2557, -1.0833, 0.1504, ..., -2.2932, 0.0716, + -0.7273]), size=(10000, 10000), nnz=9999, + layout=torch.sparse_csr) +tensor([0.6353, 0.6903, 0.7256, ..., 0.9307, 0.7404, 0.5188]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 9999 +Density: 9.999e-05 +Time: 10.560901880264282 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.json new file mode 100644 index 0000000..845190e --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 634293, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.566636562347412, "TIME_S_1KI": 0.016658920344931147, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 669.9189710187911, "W": 64.99, "J_1KI": 1.0561664262711257, "W_1KI": 0.10246053480016332, "W_D": 45.294999999999995, "J_D": 466.90228946447365, "W_D_1KI": 0.07141021578355743, "J_D_1KI": 0.000112582380356645} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.output new file mode 100644 index 0000000..6611550 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_10000_1e-05.output @@ -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([1698, 2743, 1039, 6916, 2774, 2597, 923, 7219, 396, + 4866, 834, 6999, 7087, 2820, 3930, 2118, 9590, 8266, + 7993, 357, 479, 8961, 9987, 3231, 9668, 9626, 907, + 8007, 8206, 2305, 2321, 492, 5876, 7706, 1004, 5699, + 2751, 8337, 5455, 4515, 2835, 8478, 2625, 102, 7988, + 4049, 5405, 3458, 1977, 9297, 2105, 2993, 1344, 9348, + 6100, 2703, 8225, 7647, 3119, 1193, 238, 8458, 5019, + 1718, 4649, 207, 1628, 7502, 4336, 5724, 4510, 6851, + 468, 9661, 1612, 6178, 2594, 6565, 4936, 9321, 5927, + 9620, 3848, 7114, 5578, 2034, 1680, 951, 3358, 475, + 3085, 7183, 4423, 9677, 9327, 7724, 1601, 6116, 2386, + 3393, 870, 1429, 9229, 5322, 3465, 9681, 5015, 9277, + 7854, 7184, 8452, 303, 918, 808, 37, 8062, 2242, + 9309, 3842, 7109, 9542, 4679, 3670, 3474, 5566, 2045, + 8284, 2694, 5176, 9956, 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"MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.733664512634277, "TIME_S_1KI": 0.02271213576211876, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 687.49480676651, "W": 65.37, "J_1KI": 1.4547199019173036, "W_1KI": 0.1383211030139908, "W_D": 45.042500000000004, "J_D": 473.7109504938126, "W_D_1KI": 0.09530867802520547, "J_D_1KI": 0.0002016705135574687} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_10000_5e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_10000_5e-05.output new file mode 100644 index 0000000..4ac088a --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_10000_5e-05.output @@ -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, ..., 4999, 4999, 5000]), + col_indices=tensor([2720, 7565, 9569, ..., 3581, 5033, 9559]), + values=tensor([-0.2858, 1.1812, -0.9537, ..., -0.6696, -0.1596, + 1.4045]), size=(10000, 10000), nnz=5000, + layout=torch.sparse_csr) +tensor([0.5283, 0.9547, 0.2607, ..., 0.6208, 0.8258, 0.5120]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 5000 +Density: 5e-05 +Time: 10.733664512634277 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.json b/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.json new file mode 100644 index 0000000..11e8df9 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 77665, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.419386148452759, "TIME_S_1KI": 0.13415806538920697, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 715.0278479003906, "W": 64.64, "J_1KI": 9.206564706114603, "W_1KI": 0.8322925384664907, "W_D": 44.5275, "J_D": 492.5495435857773, "W_D_1KI": 0.5733277538144597, "J_D_1KI": 0.007382060822950617} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.output b/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.output new file mode 100644 index 0000000..680e9ff --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_0.0001.output @@ -0,0 +1,16 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 39991, 39995, 39998]), + col_indices=tensor([11652, 13274, 207, ..., 7083, 12745, 14601]), + values=tensor([-0.6317, 1.0881, 0.8065, ..., -2.7134, -1.0640, + 1.4791]), size=(20000, 20000), nnz=39998, + layout=torch.sparse_csr) +tensor([0.8018, 0.5866, 0.4831, ..., 0.3300, 0.2526, 0.0357]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39998 +Density: 9.9995e-05 +Time: 10.419386148452759 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.json new file mode 100644 index 0000000..3e142f8 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 343961, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.51590633392334, "TIME_S_1KI": 0.030572961277363826, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 682.592255680561, "W": 65.41, "J_1KI": 1.9845048004877328, "W_1KI": 0.19016690845764492, "W_D": 44.973749999999995, "J_D": 469.32783150762316, "W_D_1KI": 0.13075246902991908, "J_D_1KI": 0.00038013748369704434} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.output new file mode 100644 index 0000000..c23dabb --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_1e-05.output @@ -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, 1, ..., 4000, 4000, 4000]), + col_indices=tensor([15853, 14861, 12215, ..., 3910, 15182, 195]), + values=tensor([-0.0688, 0.2364, -0.1682, ..., 0.7875, 0.1623, + -0.0174]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.0870, 0.4697, 0.0475, ..., 0.6658, 0.3278, 0.5072]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 10.51590633392334 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.json new file mode 100644 index 0000000..5e81a0d --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 94898, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 20000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.925030946731567, "TIME_S_1KI": 0.11512393250365201, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 615.267878255844, "W": 65.02, "J_1KI": 6.483465175829249, "W_1KI": 0.6851566945562604, "W_D": 45.30499999999999, "J_D": 428.70980043649666, "W_D_1KI": 0.47740732154523796, "J_D_1KI": 0.005030741654673839} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.output new file mode 100644 index 0000000..1c9e1c4 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_20000_5e-05.output @@ -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, 3, ..., 19999, 20000, 20000]), + col_indices=tensor([ 5331, 11927, 15794, ..., 14979, 9049, 4624]), + values=tensor([ 0.0049, -2.8806, 0.2868, ..., -0.5517, 0.2551, + 0.4274]), size=(20000, 20000), nnz=20000, + layout=torch.sparse_csr) +tensor([0.4157, 0.0530, 0.1068, ..., 0.7666, 0.0297, 0.3868]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 20000 +Density: 5e-05 +Time: 10.925030946731567 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.json b/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.json new file mode 100644 index 0000000..b2cc413 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 19780, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249992, "MATRIX_DENSITY": 9.99968e-05, "TIME_S": 10.32542872428894, "TIME_S_1KI": 0.5220135856566704, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 671.4097343873977, "W": 65.31, "J_1KI": 33.943869281466014, "W_1KI": 3.301820020222447, "W_D": 45.71, "J_D": 469.91485161304473, "W_D_1KI": 2.3109201213346817, "J_D_1KI": 0.11683114870246115} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.output b/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.output new file mode 100644 index 0000000..2ffad82 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_0.0001.output @@ -0,0 +1,17 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 10, ..., 249979, 249986, + 249992]), + col_indices=tensor([ 7217, 18019, 22940, ..., 41080, 45621, 49188]), + values=tensor([ 1.3418, -1.2707, -0.5433, ..., -1.2530, 1.9430, + 0.1875]), size=(50000, 50000), nnz=249992, + layout=torch.sparse_csr) +tensor([0.8358, 0.3669, 0.4208, ..., 0.9554, 0.6487, 0.8610]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249992 +Density: 9.99968e-05 +Time: 10.32542872428894 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.json new file mode 100644 index 0000000..4513681 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 43359, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.284875392913818, "TIME_S_1KI": 0.23720278126603053, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 638.9146102428437, "W": 64.12, "J_1KI": 14.735455389719403, "W_1KI": 1.4788163933670058, "W_D": 44.045, "J_D": 438.8801311314106, "W_D_1KI": 1.0158213980949746, "J_D_1KI": 0.023428155586959445} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.output new file mode 100644 index 0000000..c692540 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_1e-05.output @@ -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, ..., 24997, 25000, 25000]), + col_indices=tensor([19994, 22049, 48468, ..., 8495, 16023, 32837]), + values=tensor([-1.8163, -0.0299, 0.1597, ..., -2.9027, 0.0447, + 0.9203]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.2325, 0.1458, 0.0013, ..., 0.5389, 0.4890, 0.3122]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.284875392913818 seconds + diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.json b/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.json new file mode 100644 index 0000000..8f95134 --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 23583, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124997, "MATRIX_DENSITY": 4.99988e-05, "TIME_S": 10.195623636245728, "TIME_S_1KI": 0.4323293743902696, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 656.70900785923, "W": 64.5, "J_1KI": 27.846711947556717, "W_1KI": 2.7350209896959674, "W_D": 44.925, "J_D": 457.40546012520787, "W_D_1KI": 1.9049739218928887, "J_D_1KI": 0.08077742110388367} diff --git a/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.output b/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.output new file mode 100644 index 0000000..b9f1efe --- /dev/null +++ b/pytorch/output_1core/epyc_7313p_10_2_10_50000_5e-05.output @@ -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, 0, 5, ..., 124989, 124993, + 124997]), + col_indices=tensor([ 7870, 10492, 13171, ..., 12371, 39744, 40067]), + values=tensor([-0.1686, 0.9622, 0.3859, ..., -0.4609, -1.3867, + -0.0631]), size=(50000, 50000), nnz=124997, + layout=torch.sparse_csr) +tensor([0.9114, 0.0077, 0.0075, ..., 0.9886, 0.9267, 0.5022]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124997 +Density: 4.99988e-05 +Time: 10.195623636245728 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.json b/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.json new file mode 100644 index 0000000..95fade4 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 3828, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999959, "MATRIX_DENSITY": 9.99959e-05, "TIME_S": 10.317766427993774, "TIME_S_1KI": 2.695341282130035, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 535.4964678955079, "W": 51.84, "J_1KI": 139.88935942933853, "W_1KI": 13.542319749216302, "W_D": 42.282500000000006, "J_D": 436.7694715237618, "W_D_1KI": 11.045585161964473, "J_D_1KI": 2.8854715679113045} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.output b/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.output new file mode 100644 index 0000000..8dfa29e --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_0.0001.output @@ -0,0 +1,17 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 23, ..., 999934, 999944, + 999959]), + col_indices=tensor([ 9492, 26542, 45572, ..., 85669, 93860, 99637]), + values=tensor([-0.5032, -0.4425, 1.2901, ..., 0.6217, -1.3072, + 1.2570]), size=(100000, 100000), nnz=999959, + layout=torch.sparse_csr) +tensor([0.5683, 0.9148, 0.7465, ..., 0.9189, 0.9836, 0.0134]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999959 +Density: 9.99959e-05 +Time: 10.317766427993774 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.json new file mode 100644 index 0000000..0cb9f4e --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 10284, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99999, "MATRIX_DENSITY": 9.9999e-06, "TIME_S": 10.279484272003174, "TIME_S_1KI": 0.9995608977054816, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 527.2581393003464, "W": 51.17000000000001, "J_1KI": 51.269752946358075, "W_1KI": 4.975690392843252, "W_D": 41.602500000000006, "J_D": 428.67415947318085, "W_D_1KI": 4.045361726954493, "J_D_1KI": 0.39336461755683516} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.output new file mode 100644 index 0000000..7631528 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_1e-05.output @@ -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, 2, ..., 99996, 99997, 99999]), + col_indices=tensor([53446, 60239, 57002, ..., 87293, 8614, 73293]), + values=tensor([-1.5600, -1.7444, 1.1495, ..., -0.1715, -1.3206, + -1.4367]), size=(100000, 100000), nnz=99999, + layout=torch.sparse_csr) +tensor([0.8858, 0.7738, 0.0915, ..., 0.6061, 0.1212, 0.2608]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 99999 +Density: 9.9999e-06 +Time: 10.279484272003174 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.json new file mode 100644 index 0000000..236c683 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 5951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499988, "MATRIX_DENSITY": 4.99988e-05, "TIME_S": 10.432047128677368, "TIME_S_1KI": 1.7529906114396518, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 542.8448672485351, "W": 52.16, "J_1KI": 91.21910052907666, "W_1KI": 8.7649134599227, "W_D": 42.12625, "J_D": 438.42060178160665, "W_D_1KI": 7.078852293732146, "J_D_1KI": 1.1895231547188954} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.output new file mode 100644 index 0000000..b530832 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_100000_5e-05.output @@ -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, 11, ..., 499969, 499979, + 499988]), + col_indices=tensor([22455, 23859, 29197, ..., 41507, 47228, 83381]), + values=tensor([ 0.8086, -1.3111, 0.7144, ..., -0.9283, 0.6276, + 0.0423]), size=(100000, 100000), nnz=499988, + layout=torch.sparse_csr) +tensor([0.2048, 0.7838, 0.9595, ..., 0.6479, 0.2872, 0.9019]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499988 +Density: 4.99988e-05 +Time: 10.432047128677368 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.json b/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.json new file mode 100644 index 0000000..11addfa --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 123377, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433231830596924, "TIME_S_1KI": 0.08456383143208963, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 530.0696156978607, "W": 50.84, "J_1KI": 4.296340612090265, "W_1KI": 0.41207032104849367, "W_D": 41.282500000000006, "J_D": 430.4209069639445, "W_D_1KI": 0.33460450489151145, "J_D_1KI": 0.00271204928707548} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.output b/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.output new file mode 100644 index 0000000..3da390a --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_10000_0.0001.output @@ -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, 4, ..., 9998, 10000, 10000]), + col_indices=tensor([9176, 176, 6730, ..., 9592, 5827, 9675]), + values=tensor([-0.0157, -0.6431, -0.6454, ..., 0.0062, -1.2344, + 0.4342]), size=(10000, 10000), nnz=10000, + layout=torch.sparse_csr) +tensor([0.7658, 0.4719, 0.0181, ..., 0.6359, 0.9564, 0.9840]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.433231830596924 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.json new file mode 100644 index 0000000..0925a71 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 356710, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.643239498138428, "TIME_S_1KI": 0.029837233321573346, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 540.0778507947922, "W": 50.71, "J_1KI": 1.5140530144789666, "W_1KI": 0.1421602982815172, "W_D": 41.230000000000004, "J_D": 439.1127940893174, "W_D_1KI": 0.11558408791455245, "J_D_1KI": 0.0003240281683007273} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.output new file mode 100644 index 0000000..c9d1d95 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_10000_1e-05.output @@ -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([3764, 7829, 5913, 9241, 2345, 6075, 4450, 6634, 8729, + 4007, 3849, 5461, 1244, 8426, 5722, 9818, 3851, 4820, + 8955, 4220, 698, 4092, 5428, 631, 7097, 3582, 7936, + 6223, 7975, 9145, 6326, 8587, 2642, 8193, 4524, 5178, + 2495, 6920, 2580, 7419, 3189, 3756, 4022, 8147, 9518, + 5609, 3664, 4255, 1311, 9267, 4467, 5274, 9296, 8452, + 3572, 4174, 4359, 3960, 5997, 9129, 5697, 3838, 1404, + 3088, 5778, 3281, 8945, 6617, 3929, 9796, 5842, 3109, + 8559, 8895, 6359, 9120, 4308, 1343, 6943, 5789, 8328, + 9886, 3202, 2572, 8626, 6452, 2516, 1848, 1451, 7459, + 6651, 4759, 2648, 9393, 6515, 9253, 267, 1581, 7821, + 2207, 671, 5337, 2470, 7840, 7915, 4010, 2589, 6195, + 2640, 1421, 4183, 7620, 5400, 604, 3940, 4042, 624, + 5720, 5203, 7094, 6961, 499, 1406, 6808, 8312, 6060, + 7588, 7742, 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"synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 4999, "MATRIX_DENSITY": 4.999e-05, "TIME_S": 10.489216327667236, "TIME_S_1KI": 0.06435575826237046, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 532.5132316040992, "W": 50.29, "J_1KI": 3.2671928706659337, "W_1KI": 0.30855032272314525, "W_D": 40.7775, "J_D": 431.7868025797606, "W_D_1KI": 0.250187130340884, "J_D_1KI": 0.0015350033765730241} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_10000_5e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_10000_5e-05.output new file mode 100644 index 0000000..9b1449c --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_10000_5e-05.output @@ -0,0 +1,16 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 4998, 4998, 4999]), + col_indices=tensor([ 135, 4852, 7675, ..., 8242, 9510, 5080]), + values=tensor([-0.5492, 1.2472, 0.2842, ..., 0.5096, 1.1862, + -0.3033]), size=(10000, 10000), nnz=4999, + layout=torch.sparse_csr) +tensor([0.1525, 0.9434, 0.8321, ..., 0.0657, 0.5857, 0.7418]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 4999 +Density: 4.999e-05 +Time: 10.489216327667236 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.json b/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.json new file mode 100644 index 0000000..4f79224 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 44363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.488017559051514, "TIME_S_1KI": 0.23641362304288516, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 526.0746799468994, "W": 49.760000000000005, "J_1KI": 11.858410836663422, "W_1KI": 1.1216554335820392, "W_D": 40.27875, "J_D": 425.8366261035204, "W_D_1KI": 0.9079356671099791, "J_D_1KI": 0.020466056558618197} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.output b/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.output new file mode 100644 index 0000000..92c2cfb --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_0.0001.output @@ -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, 2, ..., 39993, 39996, 39998]), + col_indices=tensor([12560, 17978, 12094, ..., 7541, 14037, 17712]), + values=tensor([ 0.0578, 0.0703, -0.7576, ..., 0.5104, -1.0726, + -0.6932]), size=(20000, 20000), nnz=39998, + layout=torch.sparse_csr) +tensor([0.2932, 0.3582, 0.9907, ..., 0.9728, 0.2484, 0.8066]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39998 +Density: 9.9995e-05 +Time: 10.488017559051514 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.json new file mode 100644 index 0000000..ae80b37 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 144303, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.480739116668701, "TIME_S_1KI": 0.0726300847291373, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 531.7523377656936, "W": 50.89999999999999, "J_1KI": 3.6849707751446164, "W_1KI": 0.3527300194729146, "W_D": 41.34374999999999, "J_D": 431.91818692535156, "W_D_1KI": 0.2865065175360179, "J_D_1KI": 0.001985450874451799} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.output new file mode 100644 index 0000000..2dba38b --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_1e-05.output @@ -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, ..., 4000, 4000, 4000]), + col_indices=tensor([ 9066, 345, 11126, ..., 9719, 6530, 16548]), + values=tensor([-1.5343, 0.4773, -0.7801, ..., 2.3084, 1.9114, + 0.7099]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.8886, 0.9459, 0.9102, ..., 0.1910, 0.7609, 0.2804]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 10.480739116668701 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.json new file mode 100644 index 0000000..4d1797b --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 58112, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 20000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.547230005264282, "TIME_S_1KI": 0.18149831369191013, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 535.8703639006615, "W": 50.89, "J_1KI": 9.221337484524048, "W_1KI": 0.875722742290749, "W_D": 41.3525, "J_D": 435.44073930442335, "W_D_1KI": 0.7116000137665198, "J_D_1KI": 0.012245319620156247} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.output new file mode 100644 index 0000000..c563bf9 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_20000_5e-05.output @@ -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, 2, ..., 19997, 19999, 20000]), + col_indices=tensor([ 4689, 12751, 5485, ..., 4694, 6467, 17055]), + values=tensor([-1.2647, 1.0456, -0.0576, ..., -1.0665, 0.0821, + -2.2501]), size=(20000, 20000), nnz=20000, + layout=torch.sparse_csr) +tensor([0.6014, 0.6440, 0.5127, ..., 0.6380, 0.5224, 0.6478]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 20000 +Density: 5e-05 +Time: 10.547230005264282 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.json b/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.json new file mode 100644 index 0000000..b715396 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 11345, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249975, "MATRIX_DENSITY": 9.999e-05, "TIME_S": 10.375968217849731, "TIME_S_1KI": 0.9145851227721227, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 541.7785520744324, "W": 52.04, "J_1KI": 47.754830504577555, "W_1KI": 4.587042750110181, "W_D": 42.4325, "J_D": 441.75669505953783, "W_D_1KI": 3.7401939180255614, "J_D_1KI": 0.3296777362737383} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.output b/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.output new file mode 100644 index 0000000..c4b282b --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_0.0001.output @@ -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, 11, ..., 249969, 249974, + 249975]), + col_indices=tensor([ 2591, 11540, 17691, ..., 44394, 47863, 9441]), + values=tensor([ 0.8221, 0.8133, 1.6367, ..., -0.4455, -0.0184, + -0.1335]), size=(50000, 50000), nnz=249975, + layout=torch.sparse_csr) +tensor([0.6937, 0.1230, 0.4662, ..., 0.1698, 0.0658, 0.6889]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249975 +Density: 9.999e-05 +Time: 10.375968217849731 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.json new file mode 100644 index 0000000..7660cc7 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 26674, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.05628752708435, "TIME_S_1KI": 0.37700710531170245, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 509.4300830769539, "W": 51.03, "J_1KI": 19.09837606196873, "W_1KI": 1.9130988978031043, "W_D": 41.565, "J_D": 414.94143451094624, "W_D_1KI": 1.5582589787808352, "J_D_1KI": 0.05841864657647279} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.output new file mode 100644 index 0000000..c23c633 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_1e-05.output @@ -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, 1, ..., 24998, 25000, 25000]), + col_indices=tensor([18289, 816, 22022, ..., 17006, 7802, 41272]), + values=tensor([-1.3405, 1.6567, -0.4947, ..., 0.7138, 1.3141, + -1.5291]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.1595, 0.4181, 0.5988, ..., 0.7340, 0.2989, 0.4476]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.05628752708435 seconds + diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.json b/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.json new file mode 100644 index 0000000..df03843 --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 14264, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124996, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.336601495742798, "TIME_S_1KI": 0.724663593363909, "BASELINE_TIME_S": 2, "BASELINE_DELAY_S": 10, "J": 533.7157925939559, "W": 51.46999999999999, "J_1KI": 37.41697929009786, "W_1KI": 3.608384744812114, "W_D": 41.99499999999999, "J_D": 435.46521682500827, "W_D_1KI": 2.944125070106561, "J_D_1KI": 0.20640248668722386} diff --git a/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.output b/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.output new file mode 100644 index 0000000..3d1427c --- /dev/null +++ b/pytorch/output_1core/xeon_4216_10_2_10_50000_5e-05.output @@ -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, 9, ..., 124991, 124994, + 124996]), + col_indices=tensor([ 2395, 14429, 23023, ..., 39947, 32304, 46209]), + values=tensor([-0.0601, -1.0010, -0.0844, ..., 0.6539, -0.7041, + 2.0265]), size=(50000, 50000), nnz=124996, + layout=torch.sparse_csr) +tensor([0.0956, 0.8159, 0.2718, ..., 0.1492, 0.0329, 0.4158]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124996 +Density: 4.99984e-05 +Time: 10.336601495742798 seconds +