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create mode 100644 pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.output create mode 100644 pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.json create mode 100644 pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.output diff --git a/pytorch/output_16core/altra_10_10_10_100000_0.0001.json b/pytorch/output_16core/altra_10_10_10_100000_0.0001.json new file mode 100644 index 0000000..e2bba01 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 64501, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999950, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.937983274459839, "TIME_S_1KI": 0.1695785069139988, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 567.2887351226807, "W": 49.70430804561819, "J_1KI": 8.795037830772868, "W_1KI": 0.7705974798160989, "W_D": 31.653308045618193, "J_D": 361.2677811985016, "W_D_1KI": 0.49074135355449056, "J_D_1KI": 0.0076082751206103865} diff --git a/pytorch/output_16core/altra_10_10_10_100000_0.0001.output b/pytorch/output_16core/altra_10_10_10_100000_0.0001.output new file mode 100644 index 0000000..a86c1c8 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 9, 18, ..., 999932, 999938, + 999950]), + col_indices=tensor([10110, 12605, 40786, ..., 88927, 91184, 94573]), + values=tensor([-0.7796, -0.9685, -0.0839, ..., 1.2711, 1.5132, + 0.5683]), size=(100000, 100000), nnz=999950, + layout=torch.sparse_csr) +tensor([0.0946, 0.2595, 0.2568, ..., 0.3660, 0.5904, 0.0609]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999950 +Density: 9.9995e-05 +Time: 10.937983274459839 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_100000_1e-05.json b/pytorch/output_16core/altra_10_10_10_100000_1e-05.json new file mode 100644 index 0000000..48c9808 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 197739, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.723783731460571, "TIME_S_1KI": 0.054232011547851317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 372.3051293182373, "W": 34.472008609034624, "J_1KI": 1.8828108229445748, "W_1KI": 0.17433085334220677, "W_D": 16.914008609034624, "J_D": 182.67494168663018, "W_D_1KI": 0.08553703927416759, "J_D_1KI": 0.0004325754619683906} diff --git a/pytorch/output_16core/altra_10_10_10_100000_1e-05.output b/pytorch/output_16core/altra_10_10_10_100000_1e-05.output new file mode 100644 index 0000000..569a2f7 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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 /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 99996, 99997, + 100000]), + col_indices=tensor([12284, 56642, 64630, ..., 1486, 59934, 63229]), + values=tensor([ 0.6311, 0.7508, -2.1343, ..., -0.1643, 1.0472, + -3.0903]), size=(100000, 100000), nnz=100000, + layout=torch.sparse_csr) +tensor([0.1984, 0.2622, 0.0158, ..., 0.0124, 0.6459, 0.0911]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.723783731460571 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_100000_2e-05.json b/pytorch/output_16core/altra_10_10_10_100000_2e-05.json new file mode 100644 index 0000000..db2c45e --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 144710, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 199999, "MATRIX_DENSITY": 1.99999e-05, "TIME_S": 10.811187505722046, "TIME_S_1KI": 0.07470933249756095, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 419.1660452079773, "W": 40.553841840438906, "J_1KI": 2.8965934987767072, "W_1KI": 0.28024215216943477, "W_D": 23.028841840438908, "J_D": 238.02698146224026, "W_D_1KI": 0.1591378746488764, "J_D_1KI": 0.0010997019877608764} diff --git a/pytorch/output_16core/altra_10_10_10_100000_2e-05.output b/pytorch/output_16core/altra_10_10_10_100000_2e-05.output new file mode 100644 index 0000000..ebc5e07 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_2e-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, 6, ..., 199997, 199999, + 199999]), + col_indices=tensor([87835, 91834, 24596, ..., 49690, 54987, 72343]), + values=tensor([-1.1614e+00, -2.8700e-01, 1.5706e-03, ..., + -1.8033e+00, 1.7581e+00, 1.0572e+00]), + size=(100000, 100000), nnz=199999, layout=torch.sparse_csr) +tensor([0.5278, 0.3226, 0.2774, ..., 0.7173, 0.7485, 0.1274]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 199999 +Density: 1.99999e-05 +Time: 10.811187505722046 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_100000_5e-05.json b/pytorch/output_16core/altra_10_10_10_100000_5e-05.json new file mode 100644 index 0000000..e69e8c6 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 94737, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499994, "MATRIX_DENSITY": 4.99994e-05, "TIME_S": 10.580386400222778, "TIME_S_1KI": 0.11168167031067881, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 443.9495148086548, "W": 42.52685400360466, "J_1KI": 4.68612595721476, "W_1KI": 0.4488938218816794, "W_D": 24.877854003604664, "J_D": 259.7067540769577, "W_D_1KI": 0.2625991323728286, "J_D_1KI": 0.002771875110810228} diff --git a/pytorch/output_16core/altra_10_10_10_100000_5e-05.output b/pytorch/output_16core/altra_10_10_10_100000_5e-05.output new file mode 100644 index 0000000..1c1c8ee --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 3, 9, ..., 499986, 499989, + 499994]), + col_indices=tensor([14307, 59019, 64362, ..., 53395, 62334, 74450]), + values=tensor([ 0.6303, -1.3333, -0.6531, ..., -0.2267, 0.7450, + 0.1175]), size=(100000, 100000), nnz=499994, + layout=torch.sparse_csr) +tensor([0.6980, 0.0737, 0.9763, ..., 0.2302, 0.7366, 0.2229]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499994 +Density: 4.99994e-05 +Time: 10.580386400222778 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_100000_8e-05.json b/pytorch/output_16core/altra_10_10_10_100000_8e-05.json new file mode 100644 index 0000000..67fa37f --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 71869, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 799970, "MATRIX_DENSITY": 7.9997e-05, "TIME_S": 11.103689193725586, "TIME_S_1KI": 0.15449900782987916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.19529326438897, "W": 48.64164968545682, "J_1KI": 7.043305086537853, "W_1KI": 0.6768098858403041, "W_D": 30.76664968545682, "J_D": 320.1769134271144, "W_D_1KI": 0.4280934712526516, "J_D_1KI": 0.005956580323263878} diff --git a/pytorch/output_16core/altra_10_10_10_100000_8e-05.output b/pytorch/output_16core/altra_10_10_10_100000_8e-05.output new file mode 100644 index 0000000..dd24834 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_100000_8e-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, 4, 11, ..., 799957, 799963, + 799970]), + col_indices=tensor([ 9707, 28118, 30919, ..., 82276, 92061, 92663]), + values=tensor([ 1.2052, -1.5644, -1.5667, ..., 0.5565, -0.5405, + -0.2631]), size=(100000, 100000), nnz=799970, + layout=torch.sparse_csr) +tensor([0.5347, 0.9755, 0.4755, ..., 0.3062, 0.5404, 0.3654]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 799970 +Density: 7.9997e-05 +Time: 11.103689193725586 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_10000_0.0001.json b/pytorch/output_16core/altra_10_10_10_10000_0.0001.json new file mode 100644 index 0000000..800ed4d --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 745672, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.792332410812378, "TIME_S_1KI": 0.01447329712100277, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 441.4402393722534, "W": 39.33484127375969, "J_1KI": 0.5920032391886156, "W_1KI": 0.052750862676565154, "W_D": 21.838841273759694, "J_D": 245.0891628723145, "W_D_1KI": 0.029287463219431188, "J_D_1KI": 3.927660314378331e-05} diff --git a/pytorch/output_16core/altra_10_10_10_10000_0.0001.output b/pytorch/output_16core/altra_10_10_10_10000_0.0001.output new file mode 100644 index 0000000..ae6f740 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 2, 2, ..., 9997, 10000, 10000]), + col_indices=tensor([ 208, 3210, 3280, ..., 2267, 6277, 9749]), + values=tensor([-0.1715, -0.0528, -1.1977, ..., 0.8885, 1.1928, + 0.4887]), size=(10000, 10000), nnz=10000, + layout=torch.sparse_csr) +tensor([0.4199, 0.9096, 0.7063, ..., 0.1703, 0.6234, 0.8225]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.792332410812378 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_10000_1e-05.json b/pytorch/output_16core/altra_10_10_10_10000_1e-05.json new file mode 100644 index 0000000..eee9300 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 746211, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.773172378540039, "TIME_S_1KI": 0.014437166402719926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 373.8277773666382, "W": 37.833531501635065, "J_1KI": 0.5009679264532929, "W_1KI": 0.050700849359812526, "W_D": 20.80053150163506, "J_D": 205.5271118152141, "W_D_1KI": 0.027874865824324566, "J_D_1KI": 3.735520626783117e-05} diff --git a/pytorch/output_16core/altra_10_10_10_10000_1e-05.output b/pytorch/output_16core/altra_10_10_10_10000_1e-05.output new file mode 100644 index 0000000..23e95ac --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, ..., 999, 999, 1000]), + col_indices=tensor([9902, 7111, 5421, 8317, 2274, 6510, 3368, 8326, 8666, + 4378, 835, 969, 134, 7273, 5432, 882, 2682, 9181, + 2436, 162, 5466, 2267, 2378, 8682, 4925, 6470, 3561, + 8797, 7800, 1487, 5058, 6873, 6105, 2868, 697, 2588, + 4704, 7636, 4395, 8053, 6435, 2478, 4332, 3225, 1152, + 9872, 4463, 9547, 9210, 2098, 2358, 6315, 606, 3503, + 3308, 3293, 324, 7139, 3295, 2979, 2986, 253, 6525, + 2903, 6368, 968, 6911, 4898, 6976, 4805, 7710, 9392, + 3109, 2952, 253, 7810, 5011, 8956, 6103, 6828, 8288, + 7626, 8831, 7668, 8813, 1776, 2842, 3870, 7192, 681, + 107, 2994, 2651, 4184, 8595, 5092, 6800, 3268, 4112, + 7523, 7482, 1183, 6437, 1740, 1412, 3809, 8142, 5330, + 1831, 4066, 14, 127, 7302, 4921, 1449, 3806, 3675, + 4943, 5096, 4847, 6093, 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+{"CPU": "Altra", "ITERATIONS": 757288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 2000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.095738172531128, "TIME_S_1KI": 0.013331438201227444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.73304656982424, "W": 34.69019436640105, "J_1KI": 0.463143541915129, "W_1KI": 0.045808456447746504, "W_D": 17.258194366401053, "J_D": 174.48789777565008, "W_D_1KI": 0.022789472917042197, "J_D_1KI": 3.0093534978822057e-05} diff --git a/pytorch/output_16core/altra_10_10_10_10000_2e-05.output b/pytorch/output_16core/altra_10_10_10_10000_2e-05.output new file mode 100644 index 0000000..66f38c9 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_2e-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, ..., 1999, 1999, 2000]), + col_indices=tensor([4621, 3443, 1190, ..., 5062, 8400, 6357]), + values=tensor([-0.6728, -0.4536, -1.5291, ..., -1.1841, 0.2333, + 0.5602]), size=(10000, 10000), nnz=2000, + layout=torch.sparse_csr) +tensor([0.1655, 0.4850, 0.2231, ..., 0.6018, 0.5416, 0.6623]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 2000 +Density: 2e-05 +Time: 10.095738172531128 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_10000_5e-05.json b/pytorch/output_16core/altra_10_10_10_10000_5e-05.json new file mode 100644 index 0000000..23cb02a --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 759365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.492435455322266, "TIME_S_1KI": 0.013817380910790286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 382.43073294639584, "W": 36.54782584182964, "J_1KI": 0.503619119851976, "W_1KI": 0.04812945795741131, "W_D": 19.20582584182964, "J_D": 200.96675751161567, "W_D_1KI": 0.0252919555705486, "J_D_1KI": 3.330671754762018e-05} diff --git a/pytorch/output_16core/altra_10_10_10_10000_5e-05.output b/pytorch/output_16core/altra_10_10_10_10000_5e-05.output new file mode 100644 index 0000000..7af82ca --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 0, 1, ..., 4998, 4999, 5000]), + col_indices=tensor([7940, 1966, 9278, ..., 5521, 4680, 2043]), + values=tensor([-0.5302, -0.9728, 0.9241, ..., 0.4617, 1.3190, + -0.4573]), size=(10000, 10000), nnz=5000, + layout=torch.sparse_csr) +tensor([0.3509, 0.1998, 0.1131, ..., 0.8293, 0.9176, 0.2351]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 5000 +Density: 5e-05 +Time: 10.492435455322266 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_10000_8e-05.json b/pytorch/output_16core/altra_10_10_10_10000_8e-05.json new file mode 100644 index 0000000..cb620ab --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 705493, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.68189811706543, "TIME_S_1KI": 0.015141040544789855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 367.0330882072449, "W": 38.23569326717513, "J_1KI": 0.520250503133617, "W_1KI": 0.0541971263601129, "W_D": 20.893693267175127, "J_D": 200.56329854726792, "W_D_1KI": 0.029615734340631483, "J_D_1KI": 4.197877844377121e-05} diff --git a/pytorch/output_16core/altra_10_10_10_10000_8e-05.output b/pytorch/output_16core/altra_10_10_10_10000_8e-05.output new file mode 100644 index 0000000..615d554 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_10000_8e-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, 1, ..., 7996, 7997, 8000]), + col_indices=tensor([6513, 2704, 2290, ..., 7941, 7991, 9199]), + values=tensor([-2.1062, 0.2411, -0.6009, ..., -0.4068, 0.8394, + -1.3141]), size=(10000, 10000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.6508, 0.6178, 0.0529, ..., 0.1197, 0.0890, 0.8968]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 8000 +Density: 8e-05 +Time: 10.68189811706543 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_150000_0.0001.json b/pytorch/output_16core/altra_10_10_10_150000_0.0001.json new file mode 100644 index 0000000..5d54619 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 28667, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 2249892, "MATRIX_DENSITY": 9.99952e-05, "TIME_S": 10.37232518196106, "TIME_S_1KI": 0.3618210898231785, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 519.7199160766602, "W": 50.88957099176079, "J_1KI": 18.129553705538083, "W_1KI": 1.775196950910831, "W_D": 33.03557099176079, "J_D": 337.38237223815923, "W_D_1KI": 1.1523902393609653, "J_D_1KI": 0.04019919208012576} diff --git a/pytorch/output_16core/altra_10_10_10_150000_0.0001.output b/pytorch/output_16core/altra_10_10_10_150000_0.0001.output new file mode 100644 index 0000000..ce3e5db --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 28, ..., 2249858, + 2249875, 2249892]), + col_indices=tensor([ 14880, 20962, 21272, ..., 117708, 127946, + 149457]), + values=tensor([ 1.3883, -0.8537, 0.3620, ..., -0.4221, 1.7007, + -0.2153]), size=(150000, 150000), nnz=2249892, + layout=torch.sparse_csr) +tensor([0.0054, 0.0896, 0.3223, ..., 0.7227, 0.0527, 0.4309]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 2249892 +Density: 9.99952e-05 +Time: 10.37232518196106 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_150000_1e-05.json b/pytorch/output_16core/altra_10_10_10_150000_1e-05.json new file mode 100644 index 0000000..1b9cc45 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 100980, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 224999, "MATRIX_DENSITY": 9.999955555555555e-06, "TIME_S": 10.421836137771606, "TIME_S_1KI": 0.10320693343010108, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 437.94505512237555, "W": 40.87157984460665, "J_1KI": 4.336948456351511, "W_1KI": 0.4047492557398163, "W_D": 22.93957984460665, "J_D": 245.80100886058815, "W_D_1KI": 0.22716953698362696, "J_D_1KI": 0.002249648811483729} diff --git a/pytorch/output_16core/altra_10_10_10_150000_1e-05.output b/pytorch/output_16core/altra_10_10_10_150000_1e-05.output new file mode 100644 index 0000000..806bb7e --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_1e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 224998, 224999, + 224999]), + col_indices=tensor([ 54569, 101050, 20988, ..., 82049, 110775, + 48410]), + values=tensor([-0.8716, 0.8544, -1.2720, ..., 0.2452, 0.7228, + 0.1621]), size=(150000, 150000), nnz=224999, + layout=torch.sparse_csr) +tensor([0.2422, 0.3715, 0.5419, ..., 0.6661, 0.8156, 0.9122]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 224999 +Density: 9.999955555555555e-06 +Time: 10.421836137771606 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_150000_2e-05.json b/pytorch/output_16core/altra_10_10_10_150000_2e-05.json new file mode 100644 index 0000000..bdb5bf3 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 72827, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 449998, "MATRIX_DENSITY": 1.999991111111111e-05, "TIME_S": 10.586020708084106, "TIME_S_1KI": 0.1453584619452141, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 442.4028211212158, "W": 43.31909206142047, "J_1KI": 6.074708845911761, "W_1KI": 0.5948218663602849, "W_D": 25.733092061420468, "J_D": 262.80311941909787, "W_D_1KI": 0.3533454908402168, "J_D_1KI": 0.004851847403301205} diff --git a/pytorch/output_16core/altra_10_10_10_150000_2e-05.output b/pytorch/output_16core/altra_10_10_10_150000_2e-05.output new file mode 100644 index 0000000..b93ee0c --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 449993, 449994, + 449998]), + col_indices=tensor([ 66827, 13180, 62321, ..., 29562, 45238, + 134599]), + values=tensor([ 0.1032, 0.0320, 1.2243, ..., 0.9533, -1.1891, + 1.2580]), size=(150000, 150000), nnz=449998, + layout=torch.sparse_csr) +tensor([0.3606, 0.8590, 0.6142, ..., 0.3221, 0.1513, 0.9214]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 449998 +Density: 1.999991111111111e-05 +Time: 10.586020708084106 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_150000_5e-05.json b/pytorch/output_16core/altra_10_10_10_150000_5e-05.json new file mode 100644 index 0000000..65b95bb --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 42029, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1124964, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.425989151000977, "TIME_S_1KI": 0.24806655288017743, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 544.9276902675628, "W": 49.506354437696494, "J_1KI": 12.965516435498412, "W_1KI": 1.1779094063074662, "W_D": 31.889354437696497, "J_D": 351.01336899542804, "W_D_1KI": 0.7587464473981417, "J_D_1KI": 0.01805292648880872} diff --git a/pytorch/output_16core/altra_10_10_10_150000_5e-05.output b/pytorch/output_16core/altra_10_10_10_150000_5e-05.output new file mode 100644 index 0000000..d8f035f --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 16, ..., 1124945, + 1124952, 1124964]), + col_indices=tensor([ 21251, 34723, 46174, ..., 81420, 118474, + 136795]), + values=tensor([ 0.3066, 0.1728, 0.0401, ..., -0.7916, 0.6291, + 2.5231]), size=(150000, 150000), nnz=1124964, + layout=torch.sparse_csr) +tensor([0.3979, 0.8931, 0.8055, ..., 0.6413, 0.6433, 0.6850]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1124964 +Density: 4.99984e-05 +Time: 10.425989151000977 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_150000_8e-05.json b/pytorch/output_16core/altra_10_10_10_150000_8e-05.json new file mode 100644 index 0000000..7f7cae4 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 33948, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1799946, "MATRIX_DENSITY": 7.99976e-05, "TIME_S": 10.878687620162964, "TIME_S_1KI": 0.32045150289156843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 536.6147994422912, "W": 51.36202170411531, "J_1KI": 15.80696357494672, "W_1KI": 1.512961638509347, "W_D": 33.50802170411531, "J_D": 350.08163132762905, "W_D_1KI": 0.9870396401589285, "J_D_1KI": 0.02907504536817864} diff --git a/pytorch/output_16core/altra_10_10_10_150000_8e-05.output b/pytorch/output_16core/altra_10_10_10_150000_8e-05.output new file mode 100644 index 0000000..11dd4ef --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_150000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 21, ..., 1799926, + 1799935, 1799946]), + col_indices=tensor([ 4632, 7112, 14212, ..., 124633, 140187, + 147683]), + values=tensor([-1.8172, 0.7734, 0.9656, ..., 0.4938, 0.9281, + 0.7013]), size=(150000, 150000), nnz=1799946, + layout=torch.sparse_csr) +tensor([0.2540, 0.9127, 0.1735, ..., 0.5861, 0.6699, 0.8095]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1799946 +Density: 7.99976e-05 +Time: 10.878687620162964 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_200000_0.0001.json b/pytorch/output_16core/altra_10_10_10_200000_0.0001.json new file mode 100644 index 0000000..287b93f --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 9139, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3999800, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.467170715332031, "TIME_S_1KI": 1.1453299830760513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 498.041587638855, "W": 46.344394921195324, "J_1KI": 54.49628927003556, "W_1KI": 5.0710575469083405, "W_D": 29.150394921195325, "J_D": 313.26569246482853, "W_D_1KI": 3.189670086573512, "J_D_1KI": 0.3490174074377407} diff --git a/pytorch/output_16core/altra_10_10_10_200000_0.0001.output b/pytorch/output_16core/altra_10_10_10_200000_0.0001.output new file mode 100644 index 0000000..760e024 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 22, 47, ..., 3999759, + 3999781, 3999800]), + col_indices=tensor([ 1299, 3744, 6987, ..., 188420, 195819, + 198823]), + values=tensor([-0.2786, -0.1175, -0.4360, ..., -1.3931, 0.0258, + -0.4577]), size=(200000, 200000), nnz=3999800, + layout=torch.sparse_csr) +tensor([0.2287, 0.5622, 0.5229, ..., 0.2820, 0.0596, 0.0899]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3999800 +Density: 9.9995e-05 +Time: 10.467170715332031 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_200000_1e-05.json b/pytorch/output_16core/altra_10_10_10_200000_1e-05.json new file mode 100644 index 0000000..0e588df --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 58540, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 399997, "MATRIX_DENSITY": 9.999925e-06, "TIME_S": 10.480172872543335, "TIME_S_1KI": 0.17902584339841704, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 440.6894346141815, "W": 43.66832447332347, "J_1KI": 7.5280053743454305, "W_1KI": 0.745957028925922, "W_D": 26.138324473323472, "J_D": 263.78120921373363, "W_D_1KI": 0.44650366370555983, "J_D_1KI": 0.007627325994286981} diff --git a/pytorch/output_16core/altra_10_10_10_200000_1e-05.output b/pytorch/output_16core/altra_10_10_10_200000_1e-05.output new file mode 100644 index 0000000..f16d6b9 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_1e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 399991, 399996, + 399997]), + col_indices=tensor([160082, 160307, 162363, ..., 190956, 198013, + 161884]), + values=tensor([-0.6975, 1.2342, 1.1846, ..., 0.1218, 1.0676, + 0.7007]), size=(200000, 200000), nnz=399997, + layout=torch.sparse_csr) +tensor([0.5762, 0.6340, 0.7876, ..., 0.1377, 0.2839, 0.7957]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 399997 +Density: 9.999925e-06 +Time: 10.480172872543335 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_200000_2e-05.json b/pytorch/output_16core/altra_10_10_10_200000_2e-05.json new file mode 100644 index 0000000..4abb46e --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 44540, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 799989, "MATRIX_DENSITY": 1.9999725e-05, "TIME_S": 10.084713459014893, "TIME_S_1KI": 0.226419251437245, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 495.6630394935608, "W": 45.56414063287463, "J_1KI": 11.12849213052449, "W_1KI": 1.0229937277250702, "W_D": 28.07714063287463, "J_D": 305.4331909496784, "W_D_1KI": 0.6303803464947155, "J_D_1KI": 0.014153128569706231} diff --git a/pytorch/output_16core/altra_10_10_10_200000_2e-05.output b/pytorch/output_16core/altra_10_10_10_200000_2e-05.output new file mode 100644 index 0000000..1238165 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 8, ..., 799983, 799985, + 799989]), + col_indices=tensor([ 27664, 33238, 44014, ..., 124946, 138710, + 170817]), + values=tensor([-0.8725, -0.7108, 0.7697, ..., -1.4556, 1.0518, + 0.8270]), size=(200000, 200000), nnz=799989, + layout=torch.sparse_csr) +tensor([0.0240, 0.7234, 0.7805, ..., 0.9779, 0.3691, 0.0024]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 799989 +Density: 1.9999725e-05 +Time: 10.084713459014893 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_200000_5e-05.json b/pytorch/output_16core/altra_10_10_10_200000_5e-05.json new file mode 100644 index 0000000..8dfd61e --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 24847, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 1999945, "MATRIX_DENSITY": 4.9998625e-05, "TIME_S": 10.208934545516968, "TIME_S_1KI": 0.4108719179585853, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 502.7416627597809, "W": 49.780366353890884, "J_1KI": 20.23349550286879, "W_1KI": 2.003475926827822, "W_D": 32.31436635389089, "J_D": 326.349110335827, "W_D_1KI": 1.3005339217567873, "J_D_1KI": 0.05234168800083661} diff --git a/pytorch/output_16core/altra_10_10_10_200000_5e-05.output b/pytorch/output_16core/altra_10_10_10_200000_5e-05.output new file mode 100644 index 0000000..69f7b0d --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 19, ..., 1999921, + 1999935, 1999945]), + col_indices=tensor([ 3824, 96425, 103457, ..., 173104, 181406, + 194614]), + values=tensor([ 0.0349, -0.0525, -0.3643, ..., 1.0650, 0.0714, + 0.7456]), size=(200000, 200000), nnz=1999945, + layout=torch.sparse_csr) +tensor([0.4906, 0.6274, 0.6737, ..., 0.6970, 0.2171, 0.8266]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 1999945 +Density: 4.9998625e-05 +Time: 10.208934545516968 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_200000_8e-05.json b/pytorch/output_16core/altra_10_10_10_200000_8e-05.json new file mode 100644 index 0000000..bd7f605 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 13587, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3199867, "MATRIX_DENSITY": 7.9996675e-05, "TIME_S": 10.251005172729492, "TIME_S_1KI": 0.7544715664038781, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 476.3702043151855, "W": 47.48744469039588, "J_1KI": 35.06073484324616, "W_1KI": 3.4950647450059527, "W_D": 28.960444690395885, "J_D": 290.51664169692987, "W_D_1KI": 2.13148190847103, "J_D_1KI": 0.15687656645845513} diff --git a/pytorch/output_16core/altra_10_10_10_200000_8e-05.output b/pytorch/output_16core/altra_10_10_10_200000_8e-05.output new file mode 100644 index 0000000..9108de8 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_200000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 18, 36, ..., 3199838, + 3199856, 3199867]), + col_indices=tensor([ 14010, 33286, 34826, ..., 171960, 177566, + 181420]), + values=tensor([ 0.4827, 1.1570, -0.1634, ..., -1.2927, -0.3453, + -0.1386]), size=(200000, 200000), nnz=3199867, + layout=torch.sparse_csr) +tensor([0.5598, 0.1350, 0.8221, ..., 0.1901, 0.1582, 0.7521]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3199867 +Density: 7.9996675e-05 +Time: 10.251005172729492 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_20000_0.0001.json b/pytorch/output_16core/altra_10_10_10_20000_0.0001.json new file mode 100644 index 0000000..e15b50a --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 567731, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39998, "MATRIX_DENSITY": 9.9995e-05, "TIME_S": 10.283060550689697, "TIME_S_1KI": 0.018112557797072375, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.2048466873169, "W": 33.56132319147934, "J_1KI": 0.6256569514212134, "W_1KI": 0.05911483289001189, "W_D": 16.472323191479337, "J_D": 174.3390449903011, "W_D_1KI": 0.029014309931075344, "J_D_1KI": 5.110573481292257e-05} diff --git a/pytorch/output_16core/altra_10_10_10_20000_0.0001.output b/pytorch/output_16core/altra_10_10_10_20000_0.0001.output new file mode 100644 index 0000000..13294cb --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 2, 3, ..., 39994, 39995, 39998]), + col_indices=tensor([ 1674, 5478, 7295, ..., 5282, 9148, 19746]), + values=tensor([ 1.0454, -0.2042, -0.9908, ..., -0.4352, 1.3976, + -0.2792]), size=(20000, 20000), nnz=39998, + layout=torch.sparse_csr) +tensor([0.5891, 0.6809, 0.8267, ..., 0.3289, 0.5314, 0.8509]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39998 +Density: 9.9995e-05 +Time: 10.283060550689697 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_20000_1e-05.json b/pytorch/output_16core/altra_10_10_10_20000_1e-05.json new file mode 100644 index 0000000..66702e5 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 742526, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.885217666625977, "TIME_S_1KI": 0.014659712476904481, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 385.8624301719666, "W": 35.598855722519104, "J_1KI": 0.519661843722599, "W_1KI": 0.0479429080227751, "W_D": 18.531855722519108, "J_D": 200.87013302969942, "W_D_1KI": 0.024957854300750554, "J_D_1KI": 3.361209479634458e-05} diff --git a/pytorch/output_16core/altra_10_10_10_20000_1e-05.output b/pytorch/output_16core/altra_10_10_10_20000_1e-05.output new file mode 100644 index 0000000..b180812 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 2, ..., 4000, 4000, 4000]), + col_indices=tensor([ 7067, 11731, 3926, ..., 19119, 620, 6588]), + values=tensor([ 1.0868, -0.8958, -0.4960, ..., 1.4149, 1.0552, + -0.7229]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.3996, 0.2483, 0.8086, ..., 0.3640, 0.6840, 0.9715]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 10.885217666625977 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_20000_2e-05.json b/pytorch/output_16core/altra_10_10_10_20000_2e-05.json new file mode 100644 index 0000000..ebecb3e --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 725562, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.872642755508423, "TIME_S_1KI": 0.014985132566904584, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 450.6815788841247, "W": 39.35287649094676, "J_1KI": 0.6211482669766673, "W_1KI": 0.054237786007187205, "W_D": 22.100876490946764, "J_D": 253.10622246265407, "W_D_1KI": 0.030460355546385785, "J_D_1KI": 4.198174042519562e-05} diff --git a/pytorch/output_16core/altra_10_10_10_20000_2e-05.output b/pytorch/output_16core/altra_10_10_10_20000_2e-05.output new file mode 100644 index 0000000..0e2f1e7 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_2e-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, 2, ..., 7999, 8000, 8000]), + col_indices=tensor([16956, 3068, 14321, ..., 16428, 14864, 8672]), + values=tensor([ 0.6797, 0.5462, -0.0183, ..., -0.4996, 1.2405, + 3.8311]), size=(20000, 20000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.9619, 0.9404, 0.0252, ..., 0.6566, 0.1906, 0.7994]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 8000 +Density: 2e-05 +Time: 10.872642755508423 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_20000_5e-05.json b/pytorch/output_16core/altra_10_10_10_20000_5e-05.json new file mode 100644 index 0000000..bacbf0c --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 639751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19999, "MATRIX_DENSITY": 4.99975e-05, "TIME_S": 10.804665803909302, "TIME_S_1KI": 0.01688886114114601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 374.2699551010132, "W": 36.07346397818204, "J_1KI": 0.585024415907147, "W_1KI": 0.0563867254262706, "W_D": 18.95946397818204, "J_D": 196.70852059412005, "W_D_1KI": 0.029635692602562623, "J_D_1KI": 4.632379254203999e-05} diff --git a/pytorch/output_16core/altra_10_10_10_20000_5e-05.output b/pytorch/output_16core/altra_10_10_10_20000_5e-05.output new file mode 100644 index 0000000..a110972 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 0, 1, ..., 19997, 19998, 19999]), + col_indices=tensor([ 8271, 7022, 133, ..., 16237, 2122, 4630]), + values=tensor([ 1.1453, -0.0767, 0.5090, ..., -0.5285, 0.4972, + 1.3006]), size=(20000, 20000), nnz=19999, + layout=torch.sparse_csr) +tensor([0.8942, 0.9806, 0.8705, ..., 0.0714, 0.6754, 0.5950]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 19999 +Density: 4.99975e-05 +Time: 10.804665803909302 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_20000_8e-05.json b/pytorch/output_16core/altra_10_10_10_20000_8e-05.json new file mode 100644 index 0000000..59fa426 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 606932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 31998, "MATRIX_DENSITY": 7.9995e-05, "TIME_S": 10.414053440093994, "TIME_S_1KI": 0.017158517659464315, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.62409350395205, "W": 34.2116997670769, "J_1KI": 0.5826420315685316, "W_1KI": 0.056368258333844484, "W_D": 16.485699767076902, "J_D": 170.40195826578145, "W_D_1KI": 0.02716235058800146, "J_D_1KI": 4.4753531842119806e-05} diff --git a/pytorch/output_16core/altra_10_10_10_20000_8e-05.output b/pytorch/output_16core/altra_10_10_10_20000_8e-05.output new file mode 100644 index 0000000..6ed19b6 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_20000_8e-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, 4, ..., 31996, 31997, 31998]), + col_indices=tensor([12913, 3728, 7950, ..., 936, 12780, 2993]), + values=tensor([ 1.4614, -2.3542, -0.3627, ..., -1.4645, 0.4434, + -0.9357]), size=(20000, 20000), nnz=31998, + layout=torch.sparse_csr) +tensor([0.0649, 0.7612, 0.4354, ..., 0.9877, 0.3768, 0.8444]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 31998 +Density: 7.9995e-05 +Time: 10.414053440093994 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_50000_0.0001.json b/pytorch/output_16core/altra_10_10_10_50000_0.0001.json new file mode 100644 index 0000000..c4deb92 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 227433, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249985, "MATRIX_DENSITY": 9.9994e-05, "TIME_S": 10.572213649749756, "TIME_S_1KI": 0.046484958865906686, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 509.1497803020477, "W": 43.42214042283746, "J_1KI": 2.238680315970188, "W_1KI": 0.19092277911665176, "W_D": 26.133140422837457, "J_D": 306.4262280790805, "W_D_1KI": 0.1149047870046891, "J_D_1KI": 0.00050522477830697} diff --git a/pytorch/output_16core/altra_10_10_10_50000_0.0001.output b/pytorch/output_16core/altra_10_10_10_50000_0.0001.output new file mode 100644 index 0000000..7dcb971 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 7, 13, ..., 249980, 249982, + 249985]), + col_indices=tensor([ 1700, 11150, 13215, ..., 20054, 23403, 31752]), + values=tensor([ 1.2136, -0.7249, 0.7742, ..., -0.8923, -0.7123, + 0.2359]), size=(50000, 50000), nnz=249985, + layout=torch.sparse_csr) +tensor([0.8666, 0.4330, 0.6051, ..., 0.7413, 0.9082, 0.0999]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249985 +Density: 9.9994e-05 +Time: 10.572213649749756 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_50000_1e-05.json b/pytorch/output_16core/altra_10_10_10_50000_1e-05.json new file mode 100644 index 0000000..0523587 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 482951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677425146102905, "TIME_S_1KI": 0.02210871319471935, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 379.57143712997436, "W": 34.71486620028037, "J_1KI": 0.7859419219133501, "W_1KI": 0.07188072123316935, "W_D": 17.25286620028037, "J_D": 188.64238682270047, "W_D_1KI": 0.035723844034447325, "J_D_1KI": 7.396991420340225e-05} diff --git a/pytorch/output_16core/altra_10_10_10_50000_1e-05.output b/pytorch/output_16core/altra_10_10_10_50000_1e-05.output new file mode 100644 index 0000000..afd9cba --- /dev/null +++ b/pytorch/output_16core/altra_10_10_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, 2, 3, ..., 25000, 25000, 25000]), + col_indices=tensor([ 1673, 3858, 25004, ..., 30267, 19651, 6711]), + values=tensor([-0.6027, -0.6575, -0.2557, ..., -1.5398, 0.1707, + -0.6771]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.1612, 0.8476, 0.5546, ..., 0.2757, 0.8411, 0.1929]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.677425146102905 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_50000_2e-05.json b/pytorch/output_16core/altra_10_10_10_50000_2e-05.json new file mode 100644 index 0000000..256959f --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 375882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 50000, "MATRIX_DENSITY": 2e-05, "TIME_S": 11.363938331604004, "TIME_S_1KI": 0.03023272817427811, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.57937900543214, "W": 34.8864729090874, "J_1KI": 0.9140618039848467, "W_1KI": 0.09281229989488031, "W_D": 17.3774729090874, "J_D": 171.14201731848715, "W_D_1KI": 0.04623119199399652, "J_D_1KI": 0.00012299389700490184} diff --git a/pytorch/output_16core/altra_10_10_10_50000_2e-05.output b/pytorch/output_16core/altra_10_10_10_50000_2e-05.output new file mode 100644 index 0000000..bf8c573 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_2e-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, 1, ..., 50000, 50000, 50000]), + col_indices=tensor([20193, 2603, 22907, ..., 39448, 46130, 37169]), + values=tensor([-0.0912, 0.5738, -1.6784, ..., 0.2528, -1.8292, + 0.9168]), size=(50000, 50000), nnz=50000, + layout=torch.sparse_csr) +tensor([0.8671, 0.8813, 0.4085, ..., 0.9714, 0.5561, 0.6401]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 50000 +Density: 2e-05 +Time: 11.363938331604004 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_50000_5e-05.json b/pytorch/output_16core/altra_10_10_10_50000_5e-05.json new file mode 100644 index 0000000..f802708 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 290131, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124996, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.922548770904541, "TIME_S_1KI": 0.0376469552405794, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 375.18905561447144, "W": 34.68174199802756, "J_1KI": 1.2931712075389097, "W_1KI": 0.11953821548896036, "W_D": 17.293741998027564, "J_D": 187.0846835966111, "W_D_1KI": 0.05960666732623389, "J_D_1KI": 0.00020544742659775716} diff --git a/pytorch/output_16core/altra_10_10_10_50000_5e-05.output b/pytorch/output_16core/altra_10_10_10_50000_5e-05.output new file mode 100644 index 0000000..737265a --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_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, 2, 3, ..., 124994, 124994, + 124996]), + col_indices=tensor([13998, 32736, 41097, ..., 22742, 7968, 9054]), + values=tensor([0.6512, 0.4983, 2.0227, ..., 0.2925, 0.1402, 1.7352]), + size=(50000, 50000), nnz=124996, layout=torch.sparse_csr) +tensor([0.2986, 0.6337, 0.9352, ..., 0.9360, 0.6645, 0.9075]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124996 +Density: 4.99984e-05 +Time: 10.922548770904541 seconds + diff --git a/pytorch/output_16core/altra_10_10_10_50000_8e-05.json b/pytorch/output_16core/altra_10_10_10_50000_8e-05.json new file mode 100644 index 0000000..1a8fa5c --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "ITERATIONS": 242304, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 199986, "MATRIX_DENSITY": 7.99944e-05, "TIME_S": 10.34809398651123, "TIME_S_1KI": 0.042707070401277865, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 370.049839553833, "W": 35.47238359008601, "J_1KI": 1.527213085850143, "W_1KI": 0.14639619482173638, "W_D": 17.97238359008601, "J_D": 187.48888545989993, "W_D_1KI": 0.0741728720536434, "J_D_1KI": 0.0003061149302266714} diff --git a/pytorch/output_16core/altra_10_10_10_50000_8e-05.output b/pytorch/output_16core/altra_10_10_10_50000_8e-05.output new file mode 100644 index 0000000..cc4d5f8 --- /dev/null +++ b/pytorch/output_16core/altra_10_10_10_50000_8e-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, 7, 10, ..., 199981, 199982, + 199986]), + col_indices=tensor([ 1840, 12534, 16220, ..., 13556, 41718, 49099]), + values=tensor([ 0.3522, -0.6832, -0.4871, ..., 0.5234, -0.4343, + 1.6842]), size=(50000, 50000), nnz=199986, + layout=torch.sparse_csr) +tensor([0.5177, 0.9782, 0.1581, ..., 0.4536, 0.4967, 0.9151]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 199986 +Density: 7.99944e-05 +Time: 10.34809398651123 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_100000_0.0001.json new file mode 100644 index 0000000..21e77e6 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 97450, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999942, "MATRIX_DENSITY": 9.99942e-05, "TIME_S": 10.576393604278564, "TIME_S_1KI": 0.10853148901260712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1525.2442362952233, "W": 146.27, "J_1KI": 15.651557068191105, "W_1KI": 1.500974858902001, "W_D": 110.24725000000001, "J_D": 1149.613609283507, "W_D_1KI": 1.1313211903540277, "J_D_1KI": 0.01160924772041075} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_100000_0.0001.output new file mode 100644 index 0000000..8deab0c --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 18, ..., 999917, 999929, + 999942]), + col_indices=tensor([ 1892, 9410, 16896, ..., 95042, 98559, 99925]), + values=tensor([ 6.8886e-04, 2.2999e+00, -7.2309e-01, ..., + 1.2011e-01, -3.2614e-01, 2.3038e+00]), + size=(100000, 100000), nnz=999942, layout=torch.sparse_csr) +tensor([0.1643, 0.6304, 0.0977, ..., 0.5343, 0.9179, 0.3813]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999942 +Density: 9.99942e-05 +Time: 10.576393604278564 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_100000_1e-05.json new file mode 100644 index 0000000..1ff95c9 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 149990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.76275634765625, "TIME_S_1KI": 0.07175649275055837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1218.5091412353515, "W": 117.11999999999999, "J_1KI": 8.123935870627053, "W_1KI": 0.7808520568037869, "W_D": 81.863, "J_D": 851.6975224466324, "W_D_1KI": 0.545789719314621, "J_D_1KI": 0.0036388407181453496} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_100000_1e-05.output new file mode 100644 index 0000000..fef0013 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 4, 7, ..., 100000, 100000, + 100000]), + col_indices=tensor([ 6887, 13446, 39563, ..., 66177, 81567, 22565]), + values=tensor([ 1.5183, 0.2136, -1.3397, ..., -0.8325, 0.6394, + -0.8776]), size=(100000, 100000), nnz=100000, + layout=torch.sparse_csr) +tensor([0.4787, 0.6498, 0.7941, ..., 0.9578, 0.9373, 0.9290]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.76275634765625 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.json new file mode 100644 index 0000000..1794b3b --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 124803, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 199997, "MATRIX_DENSITY": 1.99997e-05, "TIME_S": 10.25207233428955, "TIME_S_1KI": 0.08214604083467185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1312.704617705345, "W": 126.02, "J_1KI": 10.518213646349407, "W_1KI": 1.0097513681562142, "W_D": 90.04275, "J_D": 937.9426576407551, "W_D_1KI": 0.7214790509843513, "J_D_1KI": 0.0057809431743175346} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-05.output new file mode 100644 index 0000000..c2e0dc0 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_2e-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, 4, 5, ..., 199993, 199995, + 199997]), + col_indices=tensor([15473, 40399, 66570, ..., 49105, 11119, 13122]), + values=tensor([-0.2163, 1.4513, 0.2678, ..., -1.0774, 1.7715, + -0.9998]), size=(100000, 100000), nnz=199997, + layout=torch.sparse_csr) +tensor([0.4636, 0.9972, 0.6979, ..., 0.4218, 0.8637, 0.4224]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 199997 +Density: 1.99997e-05 +Time: 10.25207233428955 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.json new file mode 100644 index 0000000..6f3e677 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 131904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499990, "MATRIX_DENSITY": 4.9999e-05, "TIME_S": 10.4083731174469, "TIME_S_1KI": 0.0789086996410033, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1520.4457294797899, "W": 147.62, "J_1KI": 11.52691146197075, "W_1KI": 1.1191472586123241, "W_D": 112.009, "J_D": 1153.6621441085338, "W_D_1KI": 0.849170608927705, "J_D_1KI": 0.006437792704752737} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_100000_5e-05.output new file mode 100644 index 0000000..d0aa934 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 2, 8, ..., 499980, 499982, + 499990]), + col_indices=tensor([39304, 58830, 2565, ..., 78956, 94237, 96571]), + values=tensor([ 0.3416, -1.0238, -2.6750, ..., 0.9475, -1.7082, + 1.1162]), size=(100000, 100000), nnz=499990, + layout=torch.sparse_csr) +tensor([0.2472, 0.9751, 0.5124, ..., 0.1633, 0.7065, 0.9039]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499990 +Density: 4.9999e-05 +Time: 10.4083731174469 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-05.json new file mode 100644 index 0000000..f294a63 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 111056, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 799965, "MATRIX_DENSITY": 7.99965e-05, "TIME_S": 10.777923822402954, "TIME_S_1KI": 0.09704945092928752, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1669.2134556055069, "W": 152.94, "J_1KI": 15.030376167028408, "W_1KI": 1.3771430629592278, "W_D": 117.34675, "J_D": 1280.7426054111122, "W_D_1KI": 1.0566448458435385, "J_D_1KI": 0.009514522815908538} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-05.output new file mode 100644 index 0000000..13fb848 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_100000_8e-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, 5, 10, ..., 799943, 799953, + 799965]), + col_indices=tensor([22485, 40332, 73808, ..., 86131, 87836, 93692]), + values=tensor([ 0.5614, 0.8180, -0.1837, ..., -1.4660, -0.5348, + -0.9075]), size=(100000, 100000), nnz=799965, + layout=torch.sparse_csr) +tensor([0.7081, 0.2887, 0.0123, ..., 0.7844, 0.3920, 0.0991]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 799965 +Density: 7.99965e-05 +Time: 10.777923822402954 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.json new file mode 100644 index 0000000..e39d570 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 395708, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9998, "MATRIX_DENSITY": 9.998e-05, "TIME_S": 17.949547052383423, "TIME_S_1KI": 0.04536058672653427, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.098508644104, "W": 98.08, "J_1KI": 2.651193578709816, "W_1KI": 0.24785953278680237, "W_D": 62.85875, "J_D": 672.3595114216208, "W_D_1KI": 0.15885134998534273, "J_D_1KI": 0.00040143578089233155} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_10000_0.0001.output new file mode 100644 index 0000000..8aecbf3 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, ..., 9995, 9997, 9998]), + col_indices=tensor([2642, 4658, 4879, ..., 272, 3607, 1334]), + values=tensor([-0.2834, 1.1792, 0.0754, ..., 0.1847, 0.9832, + 2.2685]), size=(10000, 10000), nnz=9998, + layout=torch.sparse_csr) +tensor([0.4716, 0.6890, 0.7469, ..., 0.5460, 0.8993, 0.3038]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 9998 +Density: 9.998e-05 +Time: 17.949547052383423 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.json new file mode 100644 index 0000000..a6cbd62 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 506768, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.661507368087769, "TIME_S_1KI": 0.02103824110458389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1001.9527449011803, "W": 95.75, "J_1KI": 1.9771428837282155, "W_1KI": 0.18894247466296213, "W_D": 60.63199999999999, "J_D": 634.4689172725676, "W_D_1KI": 0.11964449215420071, "J_D_1KI": 0.00023609322639590643} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.output new file mode 100644 index 0000000..2491b4a --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_1e-05.output @@ -0,0 +1,292 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([4994, 5090, 9262, 1673, 3489, 8361, 3981, 7152, 7935, + 9359, 1676, 6154, 3546, 3749, 9584, 6196, 9090, 9844, + 2058, 1732, 685, 4661, 3525, 4505, 9726, 6717, 7573, + 4278, 2888, 8180, 6068, 9146, 100, 5234, 6345, 96, + 4151, 766, 4028, 1493, 1539, 54, 8661, 7198, 4599, + 7599, 8751, 2928, 6104, 9619, 5323, 3978, 3624, 4124, + 6084, 8986, 9232, 9211, 9966, 3077, 5972, 1065, 9588, + 8145, 8053, 7626, 2008, 634, 3618, 7156, 6292, 8087, + 3639, 2557, 6251, 2782, 9366, 9693, 520, 2077, 4250, + 6870, 3557, 4135, 6941, 8536, 9165, 9028, 2345, 224, + 978, 4900, 4521, 7772, 1755, 5686, 9321, 8466, 792, + 6315, 2096, 6260, 6204, 5940, 9121, 7473, 656, 9001, + 5020, 5116, 8177, 5730, 6528, 2924, 8891, 7839, 4517, + 3755, 6338, 7132, 6324, 5881, 8542, 181, 4231, 2882, + 7981, 1316, 6635, 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"TIME_S_1KI": 0.02215954311991361, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 985.5935904884338, "W": 96.19, "J_1KI": 2.1277835382585435, "W_1KI": 0.2076631793472394, "W_D": 60.73975, "J_D": 622.3589592251777, "W_D_1KI": 0.13113015487843316, "J_D_1KI": 0.0002830949669440831} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_10000_2e-05.output new file mode 100644 index 0000000..2a2e489 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_2e-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, ..., 1999, 1999, 2000]), + col_indices=tensor([8577, 5335, 9363, ..., 493, 9156, 619]), + values=tensor([-0.1709, -0.4161, -1.7005, ..., -1.2204, 0.3457, + 0.2974]), size=(10000, 10000), nnz=2000, + layout=torch.sparse_csr) +tensor([0.4540, 0.9054, 0.6007, ..., 0.1570, 0.9375, 0.1255]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 2000 +Density: 2e-05 +Time: 10.264344692230225 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_10000_5e-05.json new file mode 100644 index 0000000..65527c5 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 421140, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.421851396560669, "TIME_S_1KI": 0.024746762113692998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 988.9332138633728, "W": 96.86, "J_1KI": 2.3482291253819936, "W_1KI": 0.22999477608396257, "W_D": 61.95675, "J_D": 632.5736929385662, "W_D_1KI": 0.1471167545234364, "J_D_1KI": 0.0003493298060584043} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_10000_5e-05.output new file mode 100644 index 0000000..e55279a --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 1, ..., 5000, 5000, 5000]), + col_indices=tensor([ 158, 2525, 1382, ..., 2114, 444, 4507]), + values=tensor([ 2.0070, 1.0507, 0.6574, ..., 0.5865, 1.2696, + -0.7873]), size=(10000, 10000), nnz=5000, + layout=torch.sparse_csr) +tensor([0.2973, 0.1452, 0.7931, ..., 0.5079, 0.5321, 0.0471]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 5000 +Density: 5e-05 +Time: 10.421851396560669 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-05.json new file mode 100644 index 0000000..28ea154 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 391437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.263977527618408, "TIME_S_1KI": 0.026221275780313073, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 952.8949096679687, "W": 97.6, "J_1KI": 2.434350635397187, "W_1KI": 0.2493376967430263, "W_D": 61.95824999999999, "J_D": 604.9149696407318, "W_D_1KI": 0.15828409169291607, "J_D_1KI": 0.0004043667095673533} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-05.output new file mode 100644 index 0000000..7a76a75 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_10000_8e-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, ..., 7998, 8000, 8000]), + col_indices=tensor([7114, 497, 7750, ..., 3944, 456, 7448]), + values=tensor([ 0.4943, 2.6522, -0.5457, ..., 0.8590, -0.1148, + 2.1282]), size=(10000, 10000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.4457, 0.6463, 0.4549, ..., 0.7645, 0.7943, 0.2305]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 8000 +Density: 8e-05 +Time: 10.263977527618408 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.json new file mode 100644 index 0000000..68b57cb --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 49707, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 2249888, "MATRIX_DENSITY": 9.999502222222223e-05, "TIME_S": 10.143399477005005, "TIME_S_1KI": 0.20406380342818928, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1627.3860547471047, "W": 161.13, "J_1KI": 32.73957500446828, "W_1KI": 3.241595751101454, "W_D": 124.94174999999998, "J_D": 1261.8907813920378, "W_D_1KI": 2.5135644878990884, "J_D_1KI": 0.050567615987669505} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.output new file mode 100644 index 0000000..4f0ab98 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 35, ..., 2249850, + 2249868, 2249888]), + col_indices=tensor([ 12425, 19703, 20683, ..., 137244, 148113, + 149331]), + values=tensor([-0.4641, -0.4936, -0.3021, ..., 1.3227, -0.6800, + -1.8534]), size=(150000, 150000), nnz=2249888, + layout=torch.sparse_csr) +tensor([0.1935, 0.6757, 0.3773, ..., 0.0853, 0.8759, 0.6154]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 2249888 +Density: 9.999502222222223e-05 +Time: 10.143399477005005 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.json new file mode 100644 index 0000000..c809154 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 103207, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 224997, "MATRIX_DENSITY": 9.999866666666667e-06, "TIME_S": 10.442121028900146, "TIME_S_1KI": 0.10117648055752175, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1320.9493335485458, "W": 127.7, "J_1KI": 12.799028491754878, "W_1KI": 1.237319174087029, "W_D": 92.3135, "J_D": 954.9056875687838, "W_D_1KI": 0.8944499888573451, "J_D_1KI": 0.008666563206539723} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.output new file mode 100644 index 0000000..14da7de --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_1e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 224997, 224997, + 224997]), + col_indices=tensor([ 17535, 49936, 15919, ..., 120467, 18748, + 140421]), + values=tensor([ 0.2086, -0.2135, 0.8987, ..., 0.0070, -1.1373, + -1.3024]), size=(150000, 150000), nnz=224997, + layout=torch.sparse_csr) +tensor([0.9820, 0.9824, 0.4146, ..., 0.0878, 0.0275, 0.3574]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 224997 +Density: 9.999866666666667e-06 +Time: 10.442121028900146 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.json new file mode 100644 index 0000000..630dbad --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 80059, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 449996, "MATRIX_DENSITY": 1.9999822222222222e-05, "TIME_S": 11.024610042572021, "TIME_S_1KI": 0.137706067307511, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1478.8713603067397, "W": 137.29, "J_1KI": 18.47226870566382, "W_1KI": 1.7148602905357297, "W_D": 101.51749999999998, "J_D": 1093.5342947041986, "W_D_1KI": 1.2680335752382614, "J_D_1KI": 0.015838738620745467} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.output new file mode 100644 index 0000000..bb46ac4 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 7, ..., 449987, 449992, + 449996]), + col_indices=tensor([ 26830, 38805, 39964, ..., 35924, 100960, + 149008]), + values=tensor([-1.6227, 0.6078, 0.1377, ..., -0.4547, -0.3313, + 1.8573]), size=(150000, 150000), nnz=449996, + layout=torch.sparse_csr) +tensor([0.6636, 0.3846, 0.0412, ..., 0.1798, 0.6708, 0.5140]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 449996 +Density: 1.9999822222222222e-05 +Time: 11.024610042572021 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.json new file mode 100644 index 0000000..8bb1697 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 76302, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1124967, "MATRIX_DENSITY": 4.999853333333333e-05, "TIME_S": 10.245508193969727, "TIME_S_1KI": 0.13427574891837338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1582.9392757558821, "W": 158.07, "J_1KI": 20.745711459147625, "W_1KI": 2.071636392230872, "W_D": 121.98974999999999, "J_D": 1221.6256501210926, "W_D_1KI": 1.5987752614610362, "J_D_1KI": 0.020953254979699566} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.output new file mode 100644 index 0000000..954df6f --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 14, ..., 1124950, + 1124960, 1124967]), + col_indices=tensor([ 30457, 53538, 58099, ..., 43595, 68704, + 127802]), + values=tensor([ 0.9993, 1.3551, -0.8257, ..., -0.5831, -0.3575, + 0.8935]), size=(150000, 150000), nnz=1124967, + layout=torch.sparse_csr) +tensor([0.3516, 0.5198, 0.0988, ..., 0.3923, 0.0390, 0.6416]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1124967 +Density: 4.999853333333333e-05 +Time: 10.245508193969727 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.json new file mode 100644 index 0000000..18cc4e9 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 61488, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1799911, "MATRIX_DENSITY": 7.999604444444445e-05, "TIME_S": 10.417873620986938, "TIME_S_1KI": 0.16942937843135147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1688.2730289936067, "W": 160.55, "J_1KI": 27.456951421311583, "W_1KI": 2.61107858443924, "W_D": 125.08400000000002, "J_D": 1315.3282065315248, "W_D_1KI": 2.034283112151965, "J_D_1KI": 0.03308422964077486} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.output new file mode 100644 index 0000000..910f733 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_150000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 22, ..., 1799885, + 1799902, 1799911]), + col_indices=tensor([ 8148, 18288, 20984, ..., 90221, 145590, + 147539]), + values=tensor([ 1.1715, -0.3998, -1.8516, ..., 0.6898, -0.1280, + -0.8730]), size=(150000, 150000), nnz=1799911, + layout=torch.sparse_csr) +tensor([0.6125, 0.0901, 0.3657, ..., 0.4636, 0.0050, 0.1440]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1799911 +Density: 7.999604444444445e-05 +Time: 10.417873620986938 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.json new file mode 100644 index 0000000..7fcde7b --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 32983, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3999807, "MATRIX_DENSITY": 9.9995175e-05, "TIME_S": 10.48969292640686, "TIME_S_1KI": 0.3180333179640075, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1717.8176005411146, "W": 159.42, "J_1KI": 52.08190887854697, "W_1KI": 4.833399023739502, "W_D": 124.50374999999998, "J_D": 1341.580310396254, "W_D_1KI": 3.7747854955583175, "J_D_1KI": 0.11444639649390043} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.output new file mode 100644 index 0000000..ec825c1 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 25, 44, ..., 3999753, + 3999777, 3999807]), + col_indices=tensor([ 11440, 17199, 18924, ..., 189529, 194020, + 195373]), + values=tensor([ 2.0210, -0.2315, -1.7881, ..., -0.7783, 1.1984, + -1.3241]), size=(200000, 200000), nnz=3999807, + layout=torch.sparse_csr) +tensor([0.2301, 0.8723, 0.5820, ..., 0.6894, 0.3744, 0.2923]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3999807 +Density: 9.9995175e-05 +Time: 10.48969292640686 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.json new file mode 100644 index 0000000..6be092a --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 77881, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 400000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.578283071517944, "TIME_S_1KI": 0.13582623581512748, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1463.2235719990729, "W": 136.67, "J_1KI": 18.78794021647222, "W_1KI": 1.7548567686598784, "W_D": 101.13924999999999, "J_D": 1082.82237985152, "W_D_1KI": 1.2986383071609249, "J_D_1KI": 0.016674648594149084} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.output new file mode 100644 index 0000000..2c9edc1 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_1e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 399999, 400000, + 400000]), + col_indices=tensor([ 34673, 146068, 158534, ..., 145873, 173490, + 70375]), + values=tensor([ 1.0796, -0.5052, 1.4130, ..., 1.5574, 0.2912, + 1.0338]), size=(200000, 200000), nnz=400000, + layout=torch.sparse_csr) +tensor([0.3430, 0.0135, 0.1563, ..., 0.0368, 0.8801, 0.7194]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 400000 +Density: 1e-05 +Time: 10.578283071517944 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.json new file mode 100644 index 0000000..e0688b0 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 73404, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 799992, "MATRIX_DENSITY": 1.99998e-05, "TIME_S": 10.732025861740112, "TIME_S_1KI": 0.14620491882922065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1703.6127026557924, "W": 159.46, "J_1KI": 23.208717544763125, "W_1KI": 2.1723611792272903, "W_D": 123.84325000000001, "J_D": 1323.096286455393, "W_D_1KI": 1.6871457958694351, "J_D_1KI": 0.022984384990864738} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.output new file mode 100644 index 0000000..b32b9e7 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 7, ..., 799981, 799987, + 799992]), + col_indices=tensor([ 27009, 174256, 194418, ..., 140709, 156996, + 173843]), + values=tensor([ 0.0935, 1.3801, -0.5551, ..., 0.6888, 0.8476, + -0.0900]), size=(200000, 200000), nnz=799992, + layout=torch.sparse_csr) +tensor([0.8622, 0.0079, 0.1360, ..., 0.5227, 0.4709, 0.3275]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 799992 +Density: 1.99998e-05 +Time: 10.732025861740112 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.json new file mode 100644 index 0000000..3f6e27b --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 48787, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 1999955, "MATRIX_DENSITY": 4.9998875e-05, "TIME_S": 10.624053478240967, "TIME_S_1KI": 0.21776402480662815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1748.5560211658478, "W": 162.38, "J_1KI": 35.84061371196933, "W_1KI": 3.3283456658536084, "W_D": 126.24249999999999, "J_D": 1359.4166984975336, "W_D_1KI": 2.5876258019554386, "J_D_1KI": 0.053039248200451736} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.output new file mode 100644 index 0000000..96ac54a --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 17, 32, ..., 1999929, + 1999945, 1999955]), + col_indices=tensor([ 10339, 23771, 43985, ..., 142873, 182508, + 198479]), + values=tensor([ 0.9127, -1.4974, 0.0924, ..., 1.0221, -2.4782, + -0.5277]), size=(200000, 200000), nnz=1999955, + layout=torch.sparse_csr) +tensor([0.9521, 0.4634, 0.1847, ..., 0.7016, 0.1879, 0.0658]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 1999955 +Density: 4.9998875e-05 +Time: 10.624053478240967 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.json new file mode 100644 index 0000000..aa6338f --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 36618, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3199867, "MATRIX_DENSITY": 7.9996675e-05, "TIME_S": 10.282621145248413, "TIME_S_1KI": 0.2808078307184558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1651.7103869795799, "W": 160.09, "J_1KI": 45.10651556555737, "W_1KI": 4.371893604238353, "W_D": 124.06425, "J_D": 1280.0188042840362, "W_D_1KI": 3.3880673439292153, "J_D_1KI": 0.09252464208665725} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.output new file mode 100644 index 0000000..5d3f94d --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_200000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 31, ..., 3199841, + 3199852, 3199867]), + col_indices=tensor([ 3804, 8724, 12172, ..., 120964, 148272, + 152836]), + values=tensor([-0.6224, -0.8097, 1.2416, ..., -1.2954, -0.4499, + -0.0195]), size=(200000, 200000), nnz=3199867, + layout=torch.sparse_csr) +tensor([0.5490, 0.9304, 0.8028, ..., 0.1619, 0.9607, 0.8253]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3199867 +Density: 7.9996675e-05 +Time: 10.282621145248413 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_20000_0.0001.json new file mode 100644 index 0000000..52afd15 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 224092, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39997, "MATRIX_DENSITY": 9.99925e-05, "TIME_S": 10.579524993896484, "TIME_S_1KI": 0.04721063221309321, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1080.7335785508155, "W": 102.05, "J_1KI": 4.822722714558376, "W_1KI": 0.45539332060046767, "W_D": 66.69274999999999, "J_D": 706.2919585584997, "W_D_1KI": 0.297613257055138, "J_D_1KI": 0.0013280851483102388} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_20000_0.0001.output new file mode 100644 index 0000000..a781196 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, ..., 39994, 39997, 39997]), + col_indices=tensor([ 2366, 3223, 2214, ..., 5035, 11101, 14435]), + values=tensor([ 1.1702, -0.1846, 1.6909, ..., 0.1925, -0.9320, + 0.2601]), size=(20000, 20000), nnz=39997, + layout=torch.sparse_csr) +tensor([0.5906, 0.1541, 0.3203, ..., 0.6560, 0.3329, 0.6663]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39997 +Density: 9.99925e-05 +Time: 10.579524993896484 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_20000_1e-05.json new file mode 100644 index 0000000..ad96dbc --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 362795, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.505573511123657, "TIME_S_1KI": 0.031713704739932076, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1044.8693219661714, "W": 98.37, "J_1KI": 2.8800543611851634, "W_1KI": 0.2711448614231178, "W_D": 63.24950000000001, "J_D": 671.8253754162789, "W_D_1KI": 0.1743395030251244, "J_D_1KI": 0.00048054549545921085} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_20000_1e-05.output new file mode 100644 index 0000000..d782229 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 1, 1, ..., 4000, 4000, 4000]), + col_indices=tensor([14802, 12293, 16868, ..., 8910, 14786, 2624]), + values=tensor([ 0.5992, 0.3674, -0.0621, ..., -1.7356, -2.0751, + 1.0123]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.5865, 0.6189, 0.4921, ..., 0.0030, 0.4528, 0.0967]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 11.505573511123657 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-05.json new file mode 100644 index 0000000..64190a8 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 289318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 2e-05, "TIME_S": 11.454038619995117, "TIME_S_1KI": 0.03958978915931645, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 893.5451690912247, "W": 97.67, "J_1KI": 3.0884534287228056, "W_1KI": 0.33758701498005655, "W_D": 61.70824999999999, "J_D": 564.5449849551916, "W_D_1KI": 0.21328866506750355, "J_D_1KI": 0.000737211874364898} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-05.output new file mode 100644 index 0000000..af54b19 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_2e-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, ..., 7999, 7999, 8000]), + col_indices=tensor([10245, 11034, 13886, ..., 7430, 8003, 19311]), + values=tensor([-1.7912, 0.6946, 1.1502, ..., -2.7518, -0.4487, + -0.3588]), size=(20000, 20000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.9751, 0.4671, 0.2516, ..., 0.5826, 0.7740, 0.9574]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 8000 +Density: 2e-05 +Time: 11.454038619995117 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_20000_5e-05.json new file mode 100644 index 0000000..6b38867 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 243024, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19998, "MATRIX_DENSITY": 4.9995e-05, "TIME_S": 10.287146806716919, "TIME_S_1KI": 0.042329756759484326, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1044.4556510877608, "W": 99.01999999999998, "J_1KI": 4.2977469348202675, "W_1KI": 0.40744947001119225, "W_D": 63.88999999999997, "J_D": 673.9070041203496, "W_D_1KI": 0.262895845677793, "J_D_1KI": 0.0010817690667497574} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_20000_5e-05.output new file mode 100644 index 0000000..d3ce510 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 1, 2, ..., 19993, 19996, 19998]), + col_indices=tensor([ 3912, 18187, 649, ..., 13656, 12253, 15678]), + values=tensor([-0.4053, 1.0456, 0.4592, ..., 1.2515, 0.3411, + -0.8823]), size=(20000, 20000), nnz=19998, + layout=torch.sparse_csr) +tensor([0.5163, 0.5327, 0.0154, ..., 0.9275, 0.9368, 0.7027]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 19998 +Density: 4.9995e-05 +Time: 10.287146806716919 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-05.json new file mode 100644 index 0000000..891dbf8 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 204465, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 32000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.984980821609497, "TIME_S_1KI": 0.05372548270662215, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1066.9015097999572, "W": 100.03, "J_1KI": 5.218015356173219, "W_1KI": 0.4892279852297459, "W_D": 64.762, "J_D": 690.7395339164734, "W_D_1KI": 0.31673880615264227, "J_D_1KI": 0.0015491101467373012} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-05.output new file mode 100644 index 0000000..f6c4750 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_20000_8e-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, 4, ..., 31998, 31999, 32000]), + col_indices=tensor([ 4279, 5031, 1281, ..., 12976, 11908, 15197]), + values=tensor([-0.4083, 1.4233, -1.7102, ..., -0.7038, -0.1313, + 0.8677]), size=(20000, 20000), nnz=32000, + layout=torch.sparse_csr) +tensor([0.9246, 0.7207, 0.8058, ..., 0.9422, 0.4179, 0.5079]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 32000 +Density: 8e-05 +Time: 10.984980821609497 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_0.0001.json b/pytorch/output_16core/epyc_7313p_10_10_10_50000_0.0001.json new file mode 100644 index 0000000..9025bc7 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 118166, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249990, "MATRIX_DENSITY": 9.9996e-05, "TIME_S": 10.047747611999512, "TIME_S_1KI": 0.08503078391415053, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.9110194563866, "W": 118.65, "J_1KI": 10.323705799099459, "W_1KI": 1.0040959328402417, "W_D": 83.1905, "J_D": 855.330869482398, "W_D_1KI": 0.7040138449300137, "J_D_1KI": 0.005957837659986915} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_0.0001.output b/pytorch/output_16core/epyc_7313p_10_10_10_50000_0.0001.output new file mode 100644 index 0000000..a80f3db --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 3, 12, ..., 249983, 249986, + 249990]), + col_indices=tensor([40052, 41484, 47620, ..., 29456, 36044, 48972]), + values=tensor([-0.1995, -0.2284, -0.7662, ..., 0.6765, 1.2221, + 0.8848]), size=(50000, 50000), nnz=249990, + layout=torch.sparse_csr) +tensor([0.8093, 0.5729, 0.5830, ..., 0.2836, 0.0191, 0.7755]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249990 +Density: 9.9996e-05 +Time: 10.047747611999512 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_1e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_50000_1e-05.json new file mode 100644 index 0000000..627f09b --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 194914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.567578315734863, "TIME_S_1KI": 0.05421662023115253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1112.6452126598358, "W": 102.92, "J_1KI": 5.708390431984546, "W_1KI": 0.528027745569841, "W_D": 67.77175, "J_D": 732.6653050046563, "W_D_1KI": 0.3477007808571986, "J_D_1KI": 0.0017838676588505628} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_1e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_50000_1e-05.output new file mode 100644 index 0000000..82d2d19 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([40401, 21186, 16147, ..., 47817, 41742, 31627]), + values=tensor([ 0.1524, 0.7768, -0.0082, ..., -0.6538, -0.1625, + 0.3057]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.0914, 0.8339, 0.4755, ..., 0.0319, 0.4551, 0.3369]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.567578315734863 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-05.json new file mode 100644 index 0000000..dac3f80 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 171152, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 49999, "MATRIX_DENSITY": 1.99996e-05, "TIME_S": 10.966970205307007, "TIME_S_1KI": 0.06407737102287445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1142.3507367610932, "W": 105.15, "J_1KI": 6.6744807934531485, "W_1KI": 0.6143661774329251, "W_D": 62.4275, "J_D": 678.2130348944664, "W_D_1KI": 0.3647488781901468, "J_D_1KI": 0.0021311400286888075} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-05.output new file mode 100644 index 0000000..b746f6a --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_2e-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, ..., 49998, 49999, 49999]), + col_indices=tensor([ 6580, 30422, 29461, ..., 10100, 27383, 11385]), + values=tensor([ 1.4955, -0.1848, -0.6687, ..., 0.1932, -0.4539, + -0.1435]), size=(50000, 50000), nnz=49999, + layout=torch.sparse_csr) +tensor([0.2355, 0.3316, 0.5562, ..., 0.8010, 0.4002, 0.5402]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 49999 +Density: 1.99996e-05 +Time: 10.966970205307007 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_5e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_50000_5e-05.json new file mode 100644 index 0000000..eab0376 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 155603, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124997, "MATRIX_DENSITY": 4.99988e-05, "TIME_S": 11.056920528411865, "TIME_S_1KI": 0.07105853054511715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.446080982685, "W": 112.75, "J_1KI": 8.113250264986439, "W_1KI": 0.7246004254416688, "W_D": 77.44575, "J_D": 867.1493000112176, "W_D_1KI": 0.4977137330257129, "J_D_1KI": 0.0031986127068611334} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_5e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_50000_5e-05.output new file mode 100644 index 0000000..f260fc5 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_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, 5, ..., 124990, 124996, + 124997]), + col_indices=tensor([30540, 31585, 35836, ..., 43809, 44708, 4278]), + values=tensor([-0.5691, -0.5412, 0.0542, ..., -0.1235, 0.5056, + -1.4410]), size=(50000, 50000), nnz=124997, + layout=torch.sparse_csr) +tensor([0.4378, 0.6669, 0.6001, ..., 0.6667, 0.7637, 0.4259]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124997 +Density: 4.99988e-05 +Time: 11.056920528411865 seconds + diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-05.json b/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-05.json new file mode 100644 index 0000000..5174133 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "ITERATIONS": 133146, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 199991, "MATRIX_DENSITY": 7.99964e-05, "TIME_S": 10.677424669265747, "TIME_S_1KI": 0.08019335668563643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1588.1087809085845, "W": 116.02, "J_1KI": 11.927574098422667, "W_1KI": 0.8713742808646148, "W_D": 79.12375, "J_D": 1083.0643178194762, "W_D_1KI": 0.594263064605771, "J_D_1KI": 0.004463243842141492} diff --git a/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-05.output b/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-05.output new file mode 100644 index 0000000..4dda579 --- /dev/null +++ b/pytorch/output_16core/epyc_7313p_10_10_10_50000_8e-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, 10, ..., 199985, 199988, + 199991]), + col_indices=tensor([ 770, 4002, 37904, ..., 26080, 33282, 49871]), + values=tensor([ 0.2675, -0.6311, 0.0864, ..., 1.3958, 1.2633, + -0.8916]), size=(50000, 50000), nnz=199991, + layout=torch.sparse_csr) +tensor([0.3210, 0.2620, 0.7737, ..., 0.2041, 0.3621, 0.7069]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 199991 +Density: 7.99964e-05 +Time: 10.677424669265747 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_100000_0.0001.json new file mode 100644 index 0000000..0896f23 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 38727, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 999941, "MATRIX_DENSITY": 9.99941e-05, "TIME_S": 10.05482292175293, "TIME_S_1KI": 0.2596334061960113, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 902.7284339904785, "W": 89.28, "J_1KI": 23.310053295904112, "W_1KI": 2.30536834766442, "W_D": 72.24475, "J_D": 730.4815191704034, "W_D_1KI": 1.865487902496966, "J_D_1KI": 0.04817021464345201} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_100000_0.0001.output new file mode 100644 index 0000000..c37974c --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 9, 15, ..., 999920, 999931, + 999941]), + col_indices=tensor([ 4526, 41016, 41646, ..., 64451, 77652, 91991]), + values=tensor([-0.2984, -1.3999, 1.7625, ..., -0.0585, -0.5563, + -0.1414]), size=(100000, 100000), nnz=999941, + layout=torch.sparse_csr) +tensor([0.2262, 0.0145, 0.4277, ..., 0.3106, 0.3041, 0.6473]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 999941 +Density: 9.99941e-05 +Time: 10.05482292175293 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_100000_1e-05.json new file mode 100644 index 0000000..5ae87c7 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 115045, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 99998, "MATRIX_DENSITY": 9.9998e-06, "TIME_S": 10.300944328308105, "TIME_S_1KI": 0.08953839217965236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 925.4704199624061, "W": 89.13, "J_1KI": 8.044421052304804, "W_1KI": 0.7747403190056065, "W_D": 71.804, "J_D": 745.5680246267319, "W_D_1KI": 0.6241383806336651, "J_D_1KI": 0.005425167374798254} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_100000_1e-05.output new file mode 100644 index 0000000..c292261 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 2, 2, ..., 99995, 99997, 99998]), + col_indices=tensor([39053, 90019, 37392, ..., 49716, 84746, 5739]), + values=tensor([ 0.5516, -0.7211, -0.7375, ..., -0.4448, 0.2410, + -1.5980]), size=(100000, 100000), nnz=99998, + layout=torch.sparse_csr) +tensor([0.9029, 0.2783, 0.3901, ..., 0.7328, 0.7352, 0.6890]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 99998 +Density: 9.9998e-06 +Time: 10.300944328308105 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-05.json new file mode 100644 index 0000000..ebc56df --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 95581, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 199999, "MATRIX_DENSITY": 1.99999e-05, "TIME_S": 10.389726638793945, "TIME_S_1KI": 0.10870075264742936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 918.613394870758, "W": 89.57, "J_1KI": 9.610836828143231, "W_1KI": 0.9371109320890134, "W_D": 72.32974999999999, "J_D": 741.8005715937613, "W_D_1KI": 0.7567377407643777, "J_D_1KI": 0.007917240254489675} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-05.output new file mode 100644 index 0000000..9aa4929 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_2e-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, 3, ..., 199996, 199997, + 199999]), + col_indices=tensor([10841, 12096, 54263, ..., 44275, 12625, 24016]), + values=tensor([ 0.3295, 0.6407, 0.6369, ..., 0.1085, -0.6229, + 0.0400]), size=(100000, 100000), nnz=199999, + layout=torch.sparse_csr) +tensor([0.8021, 0.0280, 0.4134, ..., 0.1950, 0.0778, 0.5961]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 199999 +Density: 1.99999e-05 +Time: 10.389726638793945 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_100000_5e-05.json new file mode 100644 index 0000000..87081ca --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 67901, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 499984, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.272206783294678, "TIME_S_1KI": 0.15128211341945888, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 903.1645608949661, "W": 89.83, "J_1KI": 13.301196755496475, "W_1KI": 1.3229554793007468, "W_D": 72.82525, "J_D": 732.1962032541036, "W_D_1KI": 1.0725210232544438, "J_D_1KI": 0.015795364181005342} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_100000_5e-05.output new file mode 100644 index 0000000..a215f04 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 7, 11, ..., 499974, 499979, + 499984]), + col_indices=tensor([ 295, 12849, 15827, ..., 57780, 57988, 61102]), + values=tensor([-1.7971, 0.3959, -1.6136, ..., -1.2419, -0.2402, + -0.0622]), size=(100000, 100000), nnz=499984, + layout=torch.sparse_csr) +tensor([0.2153, 0.8006, 0.9578, ..., 0.1586, 0.3344, 0.1012]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 499984 +Density: 4.99984e-05 +Time: 10.272206783294678 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-05.json new file mode 100644 index 0000000..a12e736 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 48526, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 799969, "MATRIX_DENSITY": 7.99969e-05, "TIME_S": 10.602272033691406, "TIME_S_1KI": 0.2184864203456169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 915.2872189927101, "W": 89.81, "J_1KI": 18.861789947506697, "W_1KI": 1.8507604170959897, "W_D": 72.3995, "J_D": 737.8503174642325, "W_D_1KI": 1.4919733750978856, "J_D_1KI": 0.030745855316693844} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-05.output new file mode 100644 index 0000000..a52d723 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_100000_8e-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, 12, 17, ..., 799956, 799964, + 799969]), + col_indices=tensor([ 4851, 7603, 14931, ..., 74822, 91042, 92442]), + values=tensor([ 0.7850, -0.6544, 0.3578, ..., -2.5477, -0.3614, + -0.6828]), size=(100000, 100000), nnz=799969, + layout=torch.sparse_csr) +tensor([0.6959, 0.2575, 0.0243, ..., 0.4139, 0.9300, 0.4366]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([100000, 100000]) +Size: 10000000000 +NNZ: 799969 +Density: 7.99969e-05 +Time: 10.602272033691406 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_10000_0.0001.json new file mode 100644 index 0000000..e5feb80 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 390985, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 9998, "MATRIX_DENSITY": 9.998e-05, "TIME_S": 10.489486455917358, "TIME_S_1KI": 0.026828360310286476, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.0973235940934, "W": 80.78, "J_1KI": 2.1921488640078093, "W_1KI": 0.20660639154954794, "W_D": 63.57, "J_D": 674.4946380400658, "W_D_1KI": 0.16258935764799162, "J_D_1KI": 0.0004158455123546725} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_10000_0.0001.output new file mode 100644 index 0000000..dc7e683 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 1, ..., 9996, 9996, 9998]), + col_indices=tensor([5970, 9042, 7603, ..., 3451, 1795, 8247]), + values=tensor([ 0.0825, -0.0130, 0.8423, ..., 0.3598, -1.2678, + -0.6074]), size=(10000, 10000), nnz=9998, + layout=torch.sparse_csr) +tensor([0.1036, 0.6058, 0.2606, ..., 0.6565, 0.3359, 0.7850]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 9998 +Density: 9.998e-05 +Time: 10.489486455917358 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_10000_1e-05.json new file mode 100644 index 0000000..4b7d435 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 468056, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.249268770217896, "TIME_S_1KI": 0.02189752672803659, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 830.0039513206482, "W": 79.66, "J_1KI": 1.7733005266904989, "W_1KI": 0.17019331020219802, "W_D": 62.65725, "J_D": 652.8466617987156, "W_D_1KI": 0.13386699454766096, "J_D_1KI": 0.0002860063636566158} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_10000_1e-05.output new file mode 100644 index 0000000..8026f80 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([4619, 4429, 1579, 2562, 9908, 5195, 2789, 2450, 268, + 1460, 7483, 8287, 7021, 4661, 8923, 6044, 8898, 4830, + 143, 7062, 667, 9766, 8879, 8937, 5248, 1555, 858, + 6675, 3387, 9291, 9513, 1650, 8509, 1792, 3512, 3087, + 8912, 7035, 8393, 8980, 4673, 9552, 4800, 9025, 6733, + 2578, 8345, 3464, 3752, 3427, 615, 2799, 6952, 8376, + 4971, 7404, 4226, 695, 7990, 8156, 6105, 6921, 3122, + 3999, 798, 7687, 1487, 9476, 7485, 8943, 5939, 659, + 1622, 5762, 4019, 3273, 3508, 4385, 4277, 808, 4566, + 2467, 7876, 1596, 7842, 7280, 7722, 6098, 6117, 9604, + 1958, 5381, 8747, 5273, 4786, 1449, 9148, 5541, 7226, + 8209, 8860, 9312, 6512, 5165, 5489, 5585, 9417, 5769, + 7207, 232, 1672, 6039, 1614, 3279, 6826, 5893, 840, + 8894, 6820, 5410, 105, 3648, 3075, 389, 1933, 6590, + 6491, 2059, 5823, 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"synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 2000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.541255235671997, "TIME_S_1KI": 0.022200575873484417, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.1009757637978, "W": 80.53, "J_1KI": 1.8367019343450828, "W_1KI": 0.16960146919141822, "W_D": 63.31075, "J_D": 685.6248212012648, "W_D_1KI": 0.13333659773513695, "J_D_1KI": 0.0002808156323465088} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_10000_2e-05.output new file mode 100644 index 0000000..87b1cb1 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_2e-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, ..., 1999, 2000, 2000]), + col_indices=tensor([8211, 9593, 6283, ..., 3447, 4620, 7781]), + values=tensor([ 0.6260, 0.3491, -1.2314, ..., 0.9024, 0.0470, + -0.2971]), size=(10000, 10000), nnz=2000, + layout=torch.sparse_csr) +tensor([0.0323, 0.7495, 0.6194, ..., 0.2795, 0.8466, 0.9349]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 2000 +Density: 2e-05 +Time: 10.541255235671997 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_10000_5e-05.json new file mode 100644 index 0000000..c297dc0 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 431761, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.392480373382568, "TIME_S_1KI": 0.024069984026770755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 820.5975255584716, "W": 80.21, "J_1KI": 1.9005827889931504, "W_1KI": 0.1857740740826522, "W_D": 62.902499999999996, "J_D": 643.5311787986755, "W_D_1KI": 0.14568823955845941, "J_D_1KI": 0.0003374279741765917} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_10000_5e-05.output new file mode 100644 index 0000000..22420d7 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 1, ..., 4998, 4999, 5000]), + col_indices=tensor([8667, 3625, 4489, ..., 7663, 708, 110]), + values=tensor([ 0.0414, -0.9966, 0.5092, ..., -1.3793, -1.3651, + 0.3731]), size=(10000, 10000), nnz=5000, + layout=torch.sparse_csr) +tensor([0.3425, 0.7310, 0.5055, ..., 0.6076, 0.8006, 0.1392]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 5000 +Density: 5e-05 +Time: 10.392480373382568 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-05.json new file mode 100644 index 0000000..fc710a6 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 410986, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 8e-05, "TIME_S": 10.448603391647339, "TIME_S_1KI": 0.02542325867948626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 842.3473458051682, "W": 80.65, "J_1KI": 2.049576739366227, "W_1KI": 0.19623539487963096, "W_D": 63.568250000000006, "J_D": 663.9373424052001, "W_D_1KI": 0.15467254359029264, "J_D_1KI": 0.00037634504238658403} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-05.output new file mode 100644 index 0000000..e0ea6ea --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_10000_8e-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, 3, ..., 8000, 8000, 8000]), + col_indices=tensor([3099, 7751, 8256, ..., 6838, 9688, 6072]), + values=tensor([-0.3090, -1.0659, -1.0309, ..., 1.1039, 0.3439, + -0.8471]), size=(10000, 10000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.2900, 0.9570, 0.3383, ..., 0.9471, 0.0317, 0.0786]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([10000, 10000]) +Size: 100000000 +NNZ: 8000 +Density: 8e-05 +Time: 10.448603391647339 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.json new file mode 100644 index 0000000..162be68 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 17939, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 2249875, "MATRIX_DENSITY": 9.999444444444444e-05, "TIME_S": 10.947336912155151, "TIME_S_1KI": 0.6102534651962289, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 988.1535816693307, "W": 89.79, "J_1KI": 55.084095081628334, "W_1KI": 5.005295724399354, "W_D": 72.67950000000002, "J_D": 799.8497409392597, "W_D_1KI": 4.051480015608452, "J_D_1KI": 0.2258475954963182} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.output new file mode 100644 index 0000000..9ca8abb --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 18, 43, ..., 2249851, + 2249867, 2249875]), + col_indices=tensor([ 20302, 42228, 50870, ..., 131717, 135296, + 138377]), + values=tensor([ 0.7319, -0.4312, 0.4648, ..., -0.8177, -0.3874, + -0.8534]), size=(150000, 150000), nnz=2249875, + layout=torch.sparse_csr) +tensor([0.1039, 0.4972, 0.2824, ..., 0.4303, 0.7718, 0.3652]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 2249875 +Density: 9.999444444444444e-05 +Time: 10.947336912155151 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_150000_1e-05.json new file mode 100644 index 0000000..652e7c8 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 68141, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 224997, "MATRIX_DENSITY": 9.999866666666667e-06, "TIME_S": 10.564588069915771, "TIME_S_1KI": 0.15504010903737503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 941.0761591148377, "W": 89.98, "J_1KI": 13.810718350403393, "W_1KI": 1.3204972043263234, "W_D": 72.83575, "J_D": 761.7691471021176, "W_D_1KI": 1.0688975800179041, "J_D_1KI": 0.015686555524836797} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_150000_1e-05.output new file mode 100644 index 0000000..0a332df --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_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, 1, 1, ..., 224995, 224996, + 224997]), + col_indices=tensor([ 70240, 38345, 97324, ..., 129730, 30172, + 18427]), + values=tensor([1.3200, 1.0650, 0.2917, ..., 0.0954, 2.3423, 0.8303]), + size=(150000, 150000), nnz=224997, layout=torch.sparse_csr) +tensor([0.2593, 0.2943, 0.5935, ..., 0.0440, 0.8772, 0.4096]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 224997 +Density: 9.999866666666667e-06 +Time: 10.564588069915771 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.json new file mode 100644 index 0000000..09529e0 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 48646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 449994, "MATRIX_DENSITY": 1.9999733333333334e-05, "TIME_S": 10.377671480178833, "TIME_S_1KI": 0.21333041730417368, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 910.060439722538, "W": 89.79, "J_1KI": 18.707816464304116, "W_1KI": 1.845783826008305, "W_D": 72.70400000000001, "J_D": 736.8864484863282, "W_D_1KI": 1.4945524811906428, "J_D_1KI": 0.030723029256067156} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.output new file mode 100644 index 0000000..6c85c17 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 449987, 449988, + 449994]), + col_indices=tensor([ 56707, 77387, 89891, ..., 69715, 75903, + 136492]), + values=tensor([ 0.6407, -0.2456, 0.6109, ..., -1.3808, -1.1927, + -0.3110]), size=(150000, 150000), nnz=449994, + layout=torch.sparse_csr) +tensor([0.9850, 0.9712, 0.2197, ..., 0.1497, 0.4404, 0.0505]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 449994 +Density: 1.9999733333333334e-05 +Time: 10.377671480178833 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.json new file mode 100644 index 0000000..e87c191 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 33342, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1124973, "MATRIX_DENSITY": 4.99988e-05, "TIME_S": 10.345494270324707, "TIME_S_1KI": 0.3102841542296415, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 916.268411436081, "W": 89.73, "J_1KI": 27.480907307182562, "W_1KI": 2.6912002879251395, "W_D": 72.385, "J_D": 739.1517771291733, "W_D_1KI": 2.170985543758623, "J_D_1KI": 0.06511263702713163} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.output new file mode 100644 index 0000000..def065d --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 19, ..., 1124962, + 1124966, 1124973]), + col_indices=tensor([ 37801, 38548, 46857, ..., 79439, 118430, + 125709]), + values=tensor([-0.3635, -1.1258, 1.6372, ..., -1.2190, -0.0468, + 0.9276]), size=(150000, 150000), nnz=1124973, + layout=torch.sparse_csr) +tensor([0.0525, 0.9196, 0.1308, ..., 0.0363, 0.0266, 0.4674]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1124973 +Density: 4.99988e-05 +Time: 10.345494270324707 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.json new file mode 100644 index 0000000..8d4c983 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 21467, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [150000, 150000], "MATRIX_SIZE": 22500000000, "MATRIX_NNZ": 1799945, "MATRIX_DENSITY": 7.999755555555555e-05, "TIME_S": 10.831941366195679, "TIME_S_1KI": 0.5045857067217441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 964.4512218475342, "W": 89.7, "J_1KI": 44.927154322799375, "W_1KI": 4.1785065449294265, "W_D": 72.64975000000001, "J_D": 781.1275379533769, "W_D_1KI": 3.3842525737177995, "J_D_1KI": 0.15764906944229745} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.output new file mode 100644 index 0000000..f05a7b5 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_150000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 21, ..., 1799914, + 1799930, 1799945]), + col_indices=tensor([ 19941, 23845, 32069, ..., 116208, 127555, + 127564]), + values=tensor([ 0.5609, 0.4045, -0.4944, ..., 0.3628, -0.3411, + -0.1916]), size=(150000, 150000), nnz=1799945, + layout=torch.sparse_csr) +tensor([0.1874, 0.5108, 0.9554, ..., 0.6194, 0.1604, 0.2134]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([150000, 150000]) +Size: 22500000000 +NNZ: 1799945 +Density: 7.999755555555555e-05 +Time: 10.831941366195679 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.json new file mode 100644 index 0000000..7bf1cf5 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 9306, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3999808, "MATRIX_DENSITY": 9.99952e-05, "TIME_S": 10.848084688186646, "TIME_S_1KI": 1.165708649063684, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 959.3007300567626, "W": 88.71, "J_1KI": 103.08411025755025, "W_1KI": 9.532559638942617, "W_D": 71.455, "J_D": 772.7069514846802, "W_D_1KI": 7.678379540081668, "J_D_1KI": 0.8250998861037683} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.output new file mode 100644 index 0000000..c8759e8 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_0.0001.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 25, 47, ..., 3999763, + 3999791, 3999808]), + col_indices=tensor([ 2081, 5854, 15676, ..., 162207, 191520, + 192024]), + values=tensor([ 1.7167, 0.3013, -0.5133, ..., -1.2113, 0.0063, + -1.5317]), size=(200000, 200000), nnz=3999808, + layout=torch.sparse_csr) +tensor([0.0306, 0.7375, 0.3263, ..., 0.4333, 0.9269, 0.9209]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3999808 +Density: 9.99952e-05 +Time: 10.848084688186646 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.json new file mode 100644 index 0000000..15fbab7 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 46225, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 399997, "MATRIX_DENSITY": 9.999925e-06, "TIME_S": 10.494405031204224, "TIME_S_1KI": 0.2270287729844072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 949.3207837843895, "W": 90.63, "J_1KI": 20.53695584173909, "W_1KI": 1.9606273661438613, "W_D": 73.3945, "J_D": 768.784334828019, "W_D_1KI": 1.5877663601946999, "J_D_1KI": 0.034348650301670086} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.output new file mode 100644 index 0000000..b853883 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_1e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 399995, 399995, + 399997]), + col_indices=tensor([ 33454, 53466, 135337, ..., 68056, 79622, + 195035]), + values=tensor([ 1.4413, -0.1667, -1.7910, ..., -0.2304, 0.3830, + 0.9685]), size=(200000, 200000), nnz=399997, + layout=torch.sparse_csr) +tensor([0.1342, 0.6830, 0.0552, ..., 0.1515, 0.6538, 0.1057]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 399997 +Density: 9.999925e-06 +Time: 10.494405031204224 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.json new file mode 100644 index 0000000..c616efe --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 32984, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 799988, "MATRIX_DENSITY": 1.99997e-05, "TIME_S": 10.555753469467163, "TIME_S_1KI": 0.32002648161130137, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 944.1450488376616, "W": 90.13, "J_1KI": 28.624334490591245, "W_1KI": 2.732536987630366, "W_D": 73.21825, "J_D": 766.988219483614, "W_D_1KI": 2.2198111205432935, "J_D_1KI": 0.06729963377829534} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.output new file mode 100644 index 0000000..07fb69f --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_2e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 8, ..., 799983, 799986, + 799988]), + col_indices=tensor([ 12921, 65362, 78943, ..., 131198, 116512, + 180157]), + values=tensor([-0.0941, 0.2677, 2.4279, ..., 0.7081, -1.7754, + 1.3698]), size=(200000, 200000), nnz=799988, + layout=torch.sparse_csr) +tensor([0.8361, 0.5446, 0.0261, ..., 0.4797, 0.5907, 0.1395]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 799988 +Density: 1.99997e-05 +Time: 10.555753469467163 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.json new file mode 100644 index 0000000..3f61c32 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 16759, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 1999943, "MATRIX_DENSITY": 4.9998575e-05, "TIME_S": 10.905771970748901, "TIME_S_1KI": 0.6507412119308373, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 982.7032818317414, "W": 89.81, "J_1KI": 58.6373460129925, "W_1KI": 5.35891162957217, "W_D": 72.47325000000001, "J_D": 793.0041267120839, "W_D_1KI": 4.324437615609524, "J_D_1KI": 0.2580367334333507} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.output new file mode 100644 index 0000000..838baea --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_5e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 16, ..., 1999916, + 1999928, 1999943]), + col_indices=tensor([ 11439, 13158, 30996, ..., 116070, 121303, + 149877]), + values=tensor([-0.1881, -1.3143, 0.9779, ..., 1.8370, -0.1254, + 0.3528]), size=(200000, 200000), nnz=1999943, + layout=torch.sparse_csr) +tensor([0.1478, 0.3313, 0.5825, ..., 0.8520, 0.7896, 0.9891]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 1999943 +Density: 4.9998575e-05 +Time: 10.905771970748901 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.json new file mode 100644 index 0000000..70b4fa5 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 11415, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [200000, 200000], "MATRIX_SIZE": 40000000000, "MATRIX_NNZ": 3199867, "MATRIX_DENSITY": 7.9996675e-05, "TIME_S": 11.126498937606812, "TIME_S_1KI": 0.9747261443369962, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 978.2810427045822, "W": 89.31, "J_1KI": 85.70136160355517, "W_1KI": 7.823915900131406, "W_D": 72.25025, "J_D": 791.4124947448968, "W_D_1KI": 6.3294130530004376, "J_D_1KI": 0.5544820896189608} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.output new file mode 100644 index 0000000..c9dda7b --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_200000_8e-05.output @@ -0,0 +1,18 @@ +/nfshomes/vut/ampere_research/pytorch/spmv.py:59: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 16, 40, ..., 3199835, + 3199853, 3199867]), + col_indices=tensor([ 4953, 14074, 55932, ..., 176572, 189828, + 199926]), + values=tensor([-0.4044, -0.9783, -0.0224, ..., -0.3788, -0.2987, + 0.8363]), size=(200000, 200000), nnz=3199867, + layout=torch.sparse_csr) +tensor([0.1842, 0.3432, 0.5985, ..., 0.7673, 0.6232, 0.7057]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([200000, 200000]) +Size: 40000000000 +NNZ: 3199867 +Density: 7.9996675e-05 +Time: 11.126498937606812 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_20000_0.0001.json new file mode 100644 index 0000000..9f45256 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 257663, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 39999, "MATRIX_DENSITY": 9.99975e-05, "TIME_S": 10.560895919799805, "TIME_S_1KI": 0.0409872427154842, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.7552035093308, "W": 83.65, "J_1KI": 3.4492930824733503, "W_1KI": 0.3246488630497976, "W_D": 66.6255, "J_D": 707.8751920073032, "W_D_1KI": 0.2585761246279054, "J_D_1KI": 0.0010035438717545997} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_20000_0.0001.output new file mode 100644 index 0000000..f1b3089 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 4, ..., 39996, 39999, 39999]), + col_indices=tensor([11749, 15646, 12276, ..., 695, 1523, 11577]), + values=tensor([ 0.2488, 1.2893, 0.3985, ..., 0.2212, -0.5858, + 0.8726]), size=(20000, 20000), nnz=39999, + layout=torch.sparse_csr) +tensor([0.5272, 0.7301, 0.9724, ..., 0.0085, 0.4605, 0.7849]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 39999 +Density: 9.99975e-05 +Time: 10.560895919799805 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_20000_1e-05.json new file mode 100644 index 0000000..25ddbd1 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 362653, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 4000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462361574172974, "TIME_S_1KI": 0.02884951061806458, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 841.4804955482483, "W": 80.4, "J_1KI": 2.3203461588577743, "W_1KI": 0.2216995309565894, "W_D": 63.3455, "J_D": 662.9851085914373, "W_D_1KI": 0.17467248306232128, "J_D_1KI": 0.0004816518353972565} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_20000_1e-05.output new file mode 100644 index 0000000..ab11d6a --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, ..., 3999, 3999, 4000]), + col_indices=tensor([13117, 12680, 15540, ..., 16563, 17608, 13623]), + values=tensor([-0.6742, 0.2656, 0.4606, ..., 1.4582, -0.8717, + -0.9889]), size=(20000, 20000), nnz=4000, + layout=torch.sparse_csr) +tensor([0.9859, 0.1836, 0.7149, ..., 0.2314, 0.4109, 0.5785]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 4000 +Density: 1e-05 +Time: 10.462361574172974 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-05.json new file mode 100644 index 0000000..8e30f7c --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 337073, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 8000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.619555473327637, "TIME_S_1KI": 0.03150520947488419, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.8205809545518, "W": 80.79, "J_1KI": 2.5152432290766447, "W_1KI": 0.23968101865174607, "W_D": 63.71800000000001, "J_D": 668.6648319997788, "W_D_1KI": 0.18903323612392572, "J_D_1KI": 0.0005608080033818364} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-05.output new file mode 100644 index 0000000..9699537 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_2e-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, ..., 8000, 8000, 8000]), + col_indices=tensor([14066, 3357, 3824, ..., 13785, 11893, 596]), + values=tensor([-0.6955, 0.2205, 0.1416, ..., -1.3169, 1.3309, + -0.3151]), size=(20000, 20000), nnz=8000, + layout=torch.sparse_csr) +tensor([0.2213, 0.0157, 0.6722, ..., 0.5533, 0.4456, 0.6235]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 8000 +Density: 2e-05 +Time: 10.619555473327637 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_20000_5e-05.json new file mode 100644 index 0000000..fb65376 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 283083, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 19998, "MATRIX_DENSITY": 4.9995e-05, "TIME_S": 10.528499364852905, "TIME_S_1KI": 0.03719226998743445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 881.6079878568648, "W": 82.35, "J_1KI": 3.1143091879655964, "W_1KI": 0.2909040811352147, "W_D": 65.2235, "J_D": 698.2581493136883, "W_D_1KI": 0.23040415708467127, "J_D_1KI": 0.0008139102563017605} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_20000_5e-05.output new file mode 100644 index 0000000..a538187 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, ..., 19997, 19998, 19998]), + col_indices=tensor([ 7402, 16308, 5128, ..., 17800, 4057, 19827]), + values=tensor([ 0.1811, 0.0940, -2.5742, ..., 0.6224, 0.4439, + 1.3413]), size=(20000, 20000), nnz=19998, + layout=torch.sparse_csr) +tensor([0.2569, 0.0030, 0.7911, ..., 0.0740, 0.3518, 0.5788]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 19998 +Density: 4.9995e-05 +Time: 10.528499364852905 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-05.json new file mode 100644 index 0000000..ffa7a60 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 236129, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [20000, 20000], "MATRIX_SIZE": 400000000, "MATRIX_NNZ": 31999, "MATRIX_DENSITY": 7.99975e-05, "TIME_S": 10.501948833465576, "TIME_S_1KI": 0.04447547244711821, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 861.6724290561676, "W": 81.81, "J_1KI": 3.649159692609411, "W_1KI": 0.34646316208513145, "W_D": 64.44175, "J_D": 678.7395092914104, "W_D_1KI": 0.27290908782911033, "J_D_1KI": 0.0011557626883149054} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-05.output new file mode 100644 index 0000000..9482fc3 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_20000_8e-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, 4, ..., 31996, 31999, 31999]), + col_indices=tensor([ 4717, 11763, 9151, ..., 944, 12023, 16801]), + values=tensor([-0.7876, 0.5531, -0.1188, ..., -2.3943, -1.3679, + -1.0621]), size=(20000, 20000), nnz=31999, + layout=torch.sparse_csr) +tensor([0.4929, 0.0862, 0.8305, ..., 0.0114, 0.1340, 0.2559]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([20000, 20000]) +Size: 400000000 +NNZ: 31999 +Density: 7.99975e-05 +Time: 10.501948833465576 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_0.0001.json b/pytorch/output_16core/xeon_4216_10_10_10_50000_0.0001.json new file mode 100644 index 0000000..df89aca --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 135814, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 249989, "MATRIX_DENSITY": 9.99956e-05, "TIME_S": 10.707024335861206, "TIME_S_1KI": 0.07883593985790277, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 925.3117226076126, "W": 89.78, "J_1KI": 6.813080555816136, "W_1KI": 0.6610511434756359, "W_D": 72.7525, "J_D": 749.818902862668, "W_D_1KI": 0.5356774706584004, "J_D_1KI": 0.003944199203752194} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_0.0001.output b/pytorch/output_16core/xeon_4216_10_10_10_50000_0.0001.output new file mode 100644 index 0000000..3c21af0 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, 7, 13, ..., 249977, 249981, + 249989]), + col_indices=tensor([ 9692, 17775, 19254, ..., 44722, 46749, 49817]), + values=tensor([-0.3085, 0.9185, 0.3206, ..., 1.7563, -0.4654, + 1.0041]), size=(50000, 50000), nnz=249989, + layout=torch.sparse_csr) +tensor([0.4000, 0.7016, 0.7370, ..., 0.5540, 0.2535, 0.6470]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 249989 +Density: 9.99956e-05 +Time: 10.707024335861206 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_1e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_50000_1e-05.json new file mode 100644 index 0000000..b5faf39 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 205519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.283442735671997, "TIME_S_1KI": 0.050036457630058526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.151675002575, "W": 82.81, "J_1KI": 4.2144603418787305, "W_1KI": 0.4029311158579012, "W_D": 65.73925, "J_D": 687.6000664281249, "W_D_1KI": 0.3198694524593833, "J_D_1KI": 0.001556398447147871} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_1e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_50000_1e-05.output new file mode 100644 index 0000000..663109c --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_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, ..., 24998, 25000, 25000]), + col_indices=tensor([41777, 29923, 44439, ..., 47964, 34236, 44673]), + values=tensor([ 2.1760, -0.3572, 0.4415, ..., -0.7696, -0.1279, + -0.1867]), size=(50000, 50000), nnz=25000, + layout=torch.sparse_csr) +tensor([0.2975, 0.4636, 0.5723, ..., 0.4333, 0.2326, 0.4041]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.283442735671997 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-05.json new file mode 100644 index 0000000..9b67ef0 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 187769, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 50000, "MATRIX_DENSITY": 2e-05, "TIME_S": 10.02957010269165, "TIME_S_1KI": 0.05341440867604157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 815.6657229065895, "W": 82.17, "J_1KI": 4.343985018328848, "W_1KI": 0.4376121724033254, "W_D": 64.87975, "J_D": 644.0329583272338, "W_D_1KI": 0.3455296135144779, "J_D_1KI": 0.0018401845539704525} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-05.output new file mode 100644 index 0000000..61254e7 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_2e-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, ..., 49998, 49999, 50000]), + col_indices=tensor([12868, 46094, 27485, ..., 33208, 48338, 43412]), + values=tensor([-1.3485, -0.1142, 0.1078, ..., -0.4668, 0.8287, + 0.4367]), size=(50000, 50000), nnz=50000, + layout=torch.sparse_csr) +tensor([0.8086, 0.5430, 0.3582, ..., 0.4171, 0.7771, 0.5359]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 50000 +Density: 2e-05 +Time: 10.02957010269165 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.json new file mode 100644 index 0000000..e9df119 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 164917, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 124996, "MATRIX_DENSITY": 4.99984e-05, "TIME_S": 10.22811770439148, "TIME_S_1KI": 0.06201978998157545, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 912.5170663833618, "W": 87.1, "J_1KI": 5.533189825083902, "W_1KI": 0.5281444605468204, "W_D": 69.83425, "J_D": 731.6296778769492, "W_D_1KI": 0.42345088741609416, "J_D_1KI": 0.002567660625745643} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_50000_5e-05.output new file mode 100644 index 0000000..e85e002 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_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, 3, 5, ..., 124990, 124994, + 124996]), + col_indices=tensor([ 8436, 26852, 31055, ..., 49294, 31786, 37499]), + values=tensor([0.0313, 0.0331, 1.1563, ..., 2.8604, 0.7750, 0.6871]), + size=(50000, 50000), nnz=124996, layout=torch.sparse_csr) +tensor([0.3154, 0.1564, 0.4306, ..., 0.4157, 0.9253, 0.8915]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 124996 +Density: 4.99984e-05 +Time: 10.22811770439148 seconds + diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.json b/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.json new file mode 100644 index 0000000..c5409a3 --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "ITERATIONS": 150832, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 199990, "MATRIX_DENSITY": 7.9996e-05, "TIME_S": 10.676726818084717, "TIME_S_1KI": 0.07078555490933434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 964.5338623046874, "W": 89.6, "J_1KI": 6.394756167820406, "W_1KI": 0.5940384003394504, "W_D": 72.53375, "J_D": 780.8176119971275, "W_D_1KI": 0.4808909913015806, "J_D_1KI": 0.0031882557501165575} diff --git a/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.output b/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-05.output new file mode 100644 index 0000000..353372f --- /dev/null +++ b/pytorch/output_16core/xeon_4216_10_10_10_50000_8e-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, 4, 6, ..., 199981, 199986, + 199990]), + col_indices=tensor([17396, 24016, 41144, ..., 31894, 43730, 44012]), + values=tensor([-1.7437, -0.6747, -0.2540, ..., -0.2142, 0.9450, + 0.8557]), size=(50000, 50000), nnz=199990, + layout=torch.sparse_csr) +tensor([0.2398, 0.4706, 0.7339, ..., 0.3825, 0.9948, 0.9751]) +Matrix: synthetic +Matrix: csr +Shape: torch.Size([50000, 50000]) +Size: 2500000000 +NNZ: 199990 +Density: 7.9996e-05 +Time: 10.676726818084717 seconds +