diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..d950ac8 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 113, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.044164180755615, "TIME_S_1KI": 88.88640867925324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 399.0583483409882, "W": 27.463802555817157, "J_1KI": 3531.4898083273292, "W_1KI": 243.04250049395714, "W_D": 11.900802555817158, "J_D": 172.92268986439706, "W_D_1KI": 105.31683677714298, "J_D_1KI": 932.00740510746} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..e3f9213 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/amazon0312.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.9289438724517822} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.3618, 0.1403, 0.4609, ..., 0.6433, 0.7116, 0.4571]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.9289438724517822 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 113 -m matrices/389000+_cols/amazon0312.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.044164180755615} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8763, 0.0482, 0.2785, ..., 0.8044, 0.9404, 0.7022]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.044164180755615 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8763, 0.0482, 0.2785, ..., 0.8044, 0.9404, 0.7022]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.044164180755615 seconds + +[16.52, 16.76, 16.8, 16.72, 17.04, 16.92, 16.72, 16.88, 16.88, 16.76] +[16.56, 16.68, 16.76, 18.68, 19.36, 24.52, 29.16, 33.8, 36.48, 38.88, 38.88, 38.68, 38.88, 38.84] +14.530338525772095 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 113, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.044164180755615, 'TIME_S_1KI': 88.88640867925324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 399.0583483409882, 'W': 27.463802555817157} +[16.52, 16.76, 16.8, 16.72, 17.04, 16.92, 16.72, 16.88, 16.88, 16.76, 16.92, 16.76, 16.76, 16.88, 17.24, 17.72, 18.96, 18.92, 18.88, 18.64] +311.26 +15.562999999999999 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 113, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.044164180755615, 'TIME_S_1KI': 88.88640867925324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 399.0583483409882, 'W': 27.463802555817157, 'J_1KI': 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187.8839495840073, "W_D_1KI": 70.90380169316634, "J_D_1KI": 389.58132798443046} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..ff75ea0 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_helm2d03.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/helm2d03.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.7667891979217529} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.8101, 0.0565, 0.3715, ..., 0.3044, 0.3984, 0.4752]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.7667891979217529 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 136 -m matrices/389000+_cols/helm2d03.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 7.835829734802246} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.6143, 0.6941, 0.3080, ..., 0.2687, 0.1392, 0.4472]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 7.835829734802246 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 182 -m matrices/389000+_cols/helm2d03.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.577990770339966} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4534, 0.9614, 0.4347, ..., 0.7492, 0.4190, 0.3129]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.577990770339966 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4534, 0.9614, 0.4347, ..., 0.7492, 0.4190, 0.3129]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.577990770339966 seconds + +[16.76, 16.52, 16.88, 16.96, 16.68, 16.68, 16.6, 16.12, 15.96, 15.96] +[16.2, 16.36, 16.56, 20.4, 21.44, 25.84, 30.32, 32.76, 35.8, 38.96, 39.04, 39.0, 39.0, 39.28] +14.559577465057373 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.577990770339966, 'TIME_S_1KI': 58.12082840846135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 405.2002028274536, 'W': 27.830491908156272} +[16.76, 16.52, 16.88, 16.96, 16.68, 16.68, 16.6, 16.12, 15.96, 15.96, 16.36, 16.32, 16.48, 16.48, 16.6, 16.8, 16.92, 16.88, 16.76, 16.68] +298.52 +14.925999999999998 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.577990770339966, 'TIME_S_1KI': 58.12082840846135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 405.2002028274536, 'W': 27.830491908156272, 'J_1KI': 2226.3747408101844, 'W_1KI': 152.91479070415534, 'W_D': 12.904491908156274, 'J_D': 187.8839495840073, 'W_D_1KI': 70.90380169316634, 'J_D_1KI': 389.58132798443046} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.json b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.json new file mode 100644 index 0000000..2168084 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 373, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.37615156173706, "TIME_S_1KI": 27.818100701707937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 403.1560953426361, "W": 27.703453693461576, "J_1KI": 1080.8474405968796, "W_1KI": 74.27199381625087, "W_D": 12.822453693461576, "J_D": 186.59949120306968, "W_D_1KI": 34.37655145700155, "J_D_1KI": 92.16233634584866} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.output b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.output new file mode 100644 index 0000000..3e9b72b --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_language.output @@ -0,0 +1,77 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/language.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3676939010620117} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.2195, 0.2966, 0.0612, ..., 0.9691, 0.9520, 0.8459]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.3676939010620117 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 285 -m matrices/389000+_cols/language.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 8.002167224884033} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5703, 0.5551, 0.8897, ..., 0.2083, 0.9973, 0.5202]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 8.002167224884033 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 373 -m matrices/389000+_cols/language.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": 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+Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.37615156173706 seconds + +[16.36, 16.48, 16.52, 16.52, 16.64, 16.64, 16.68, 16.6, 16.76, 17.04] +[17.08, 16.96, 17.6, 18.68, 20.08, 25.16, 29.72, 34.04, 37.16, 38.8, 38.8, 38.68, 38.56, 38.36] +14.552557229995728 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 373, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.37615156173706, 'TIME_S_1KI': 27.818100701707937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 403.1560953426361, 'W': 27.703453693461576} +[16.36, 16.48, 16.52, 16.52, 16.64, 16.64, 16.68, 16.6, 16.76, 17.04, 16.64, 16.56, 16.44, 16.36, 16.2, 16.36, 16.52, 16.52, 16.52, 16.56] +297.62 +14.881 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.303558588027954} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6645, 0.1791, 0.7371, ..., 0.0295, 0.0962, 0.0984]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.303558588027954 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 80 -m matrices/389000+_cols/marine1.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.39077091217041} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.4439, 0.0248, 0.1672, ..., 0.5097, 0.8471, 0.8016]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.39077091217041 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.4439, 0.0248, 0.1672, ..., 0.5097, 0.8471, 0.8016]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.39077091217041 seconds + +[16.6, 16.88, 16.92, 16.76, 16.76, 16.56, 16.56, 16.56, 16.6, 16.36] +[16.32, 16.4, 16.8, 17.84, 20.24, 23.84, 28.8, 32.4, 36.48, 38.68, 38.4, 38.56, 38.56, 38.56, 38.6] +15.597429037094116 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 80, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.39077091217041, 'TIME_S_1KI': 129.88463640213013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 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0000000..05aebdc --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 212, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.325457572937012, "TIME_S_1KI": 48.70498855158968, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 398.62124523162845, "W": 27.453193435135873, "J_1KI": 1880.288892602021, "W_1KI": 129.49619544875412, "W_D": 12.278193435135872, "J_D": 178.2797607088089, "W_D_1KI": 57.91600676950883, "J_D_1KI": 273.18871117692845} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_mario002.output b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_mario002.output new file mode 100644 index 0000000..69cbdd9 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/mario002.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.494342565536499} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.5569, 0.7798, 0.9004, ..., 0.9998, 0.9126, 0.0675]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.494342565536499 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 212 -m matrices/389000+_cols/mario002.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.325457572937012} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.0146, 0.8011, 0.0938, ..., 0.9319, 0.8634, 0.7309]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.325457572937012 seconds + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.0146, 0.8011, 0.0938, ..., 0.9319, 0.8634, 0.7309]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.325457572937012 seconds + +[16.32, 16.56, 16.64, 16.88, 17.2, 17.16, 17.04, 17.04, 17.04, 17.04] +[16.88, 16.8, 16.84, 18.84, 19.64, 24.36, 28.92, 33.4, 36.08, 38.8, 38.88, 39.04, 38.8, 39.0] +14.520031929016113 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.325457572937012, 'TIME_S_1KI': 48.70498855158968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 398.62124523162845, 'W': 27.453193435135873} +[16.32, 16.56, 16.64, 16.88, 17.2, 17.16, 17.04, 17.04, 17.04, 17.04, 16.8, 16.52, 16.52, 16.44, 16.92, 16.68, 16.84, 17.12, 17.2, 17.24] +303.5 +15.175 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.325457572937012, 'TIME_S_1KI': 48.70498855158968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 398.62124523162845, 'W': 27.453193435135873, 'J_1KI': 1880.288892602021, 'W_1KI': 129.49619544875412, 'W_D': 12.278193435135872, 'J_D': 178.2797607088089, 'W_D_1KI': 57.91600676950883, 'J_D_1KI': 273.18871117692845} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.json b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.json new file mode 100644 index 0000000..38f0ede --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 23, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.135860443115234, "TIME_S_1KI": 440.68958448327106, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 398.50457602500916, "W": 24.202014645160418, "J_1KI": 17326.285914130833, "W_1KI": 1052.2615063113226, "W_D": 9.021014645160417, "J_D": 148.53786633849143, "W_D_1KI": 392.2180280504529, "J_D_1KI": 17052.95774132404} diff --git a/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.output b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..3964137 --- /dev/null +++ b/pytorch/output_389000+_16core/altra_16_coo_10_10_10_msdoor.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/msdoor.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 4.422544717788696} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.9166, 0.9706, 0.4570, ..., 0.6253, 0.2257, 0.3666]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 4.422544717788696 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 23 -m matrices/389000+_cols/msdoor.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.135860443115234} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4048, 0.1108, 0.3428, ..., 0.0325, 0.2921, 0.4433]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.135860443115234 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4048, 0.1108, 0.3428, ..., 0.0325, 0.2921, 0.4433]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.135860443115234 seconds + +[16.88, 16.84, 16.84, 16.84, 17.2, 17.2, 16.84, 16.8, 16.72, 16.52] +[16.72, 16.68, 17.12, 20.72, 22.56, 26.36, 29.6, 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8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.7393, 0.0550, 0.6192, ..., 0.4571, 0.5565, 0.6848]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 2.7536518573760986 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 38 -m matrices/389000+_cols/test1.mtx -c 16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.248776912689209} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.5704, 0.2557, 0.8650, ..., 0.6085, 0.9225, 0.5985]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.248776912689209 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.5704, 0.2557, 0.8650, ..., 0.6085, 0.9225, 0.5985]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.248776912689209 seconds + +[16.56, 16.72, 16.72, 16.64, 16.84, 16.88, 16.44, 16.76, 16.96, 16.8] +[16.96, 16.76, 19.36, 21.6, 24.24, 28.08, 28.08, 31.8, 32.16, 34.6, 33.48, 34.56, 33.88, 33.84, 33.44] +15.568520784378052 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 38, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.248776912689209, 'TIME_S_1KI': 269.70465559708447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 416.651335029602, 'W': 26.76242276322637} +[16.56, 16.72, 16.72, 16.64, 16.84, 16.88, 16.44, 16.76, 16.96, 16.8, 17.24, 17.0, 17.08, 17.04, 16.8, 16.56, 16.68, 16.6, 16.6, 16.64] +301.94 +15.097 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 38, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.248776912689209, 'TIME_S_1KI': 269.70465559708447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 416.651335029602, 'W': 26.76242276322637, 'J_1KI': 10964.508816568474, 'W_1KI': 704.2742832427991, 'W_D': 11.665422763226369, 'J_D': 181.61337674784656, 'W_D_1KI': 306.98480955858867, 'J_D_1KI': 8078.54761996286} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..cd6ea08 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 109, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": 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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.9576153755187988} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8582, 0.0335, 0.3781, ..., 0.4604, 0.7075, 0.2246]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.9576153755187988 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '109', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.09192419052124} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8590, 0.3599, 0.3245, ..., 0.4552, 0.4593, 0.9347]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.09192419052124 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8590, 0.3599, 0.3245, ..., 0.4552, 0.4593, 0.9347]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.09192419052124 seconds + +[39.76, 40.28, 38.64, 39.25, 38.69, 44.83, 38.88, 40.23, 38.56, 39.39] +[72.37] +12.80178952217102 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 109, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.09192419052124, 'TIME_S_1KI': 92.58646046349762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.4655077195168, 'W': 72.37} +[39.76, 40.28, 38.64, 39.25, 38.69, 44.83, 38.88, 40.23, 38.56, 39.39, 39.23, 39.52, 43.66, 39.53, 39.23, 38.82, 38.54, 38.52, 38.81, 39.2] +714.78 +35.739 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 109, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.09192419052124, 'TIME_S_1KI': 92.58646046349762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.4655077195168, 'W': 72.37, 'J_1KI': 8499.683557059787, 'W_1KI': 663.9449541284405, 'W_D': 36.63100000000001, 'J_D': 468.94235198664677, 'W_D_1KI': 336.0642201834863, 'J_D_1KI': 3083.1579833347373} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..aa5501c --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.266744375228882, "TIME_S_1KI": 79.58716569944869, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 946.7289336967468, "W": 73.64, "J_1KI": 7338.983982145324, "W_1KI": 570.8527131782946, "W_D": 38.252, "J_D": 491.7745134677887, "W_D_1KI": 296.5271317829458, "J_D_1KI": 2298.659936301905} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..1106727 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/helm2d03.mtx', 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'matrices/389000+_cols/helm2d03.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.266744375228882} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4954, 0.9146, 0.5987, ..., 0.8110, 0.0171, 0.3763]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.266744375228882 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 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'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3706076145172119} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.3756, 0.2751, 0.7481, ..., 0.7667, 0.3191, 0.0169]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.3706076145172119 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 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399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.7403, 0.2941, 0.3666, ..., 0.1304, 0.7725, 0.2717]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.11699652671814 seconds + +[39.98, 38.51, 39.32, 38.32, 38.86, 38.39, 39.16, 38.23, 40.42, 38.3] +[78.16] +12.554764986038208 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 283, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.11699652671814, 'TIME_S_1KI': 35.74910433469307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 981.2804313087463, 'W': 78.16} +[39.98, 38.51, 39.32, 38.32, 38.86, 38.39, 39.16, 38.23, 40.42, 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'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.793755292892456} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.4589, 0.9913, 0.7859, ..., 0.4970, 0.0192, 0.7518]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.793755292892456 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '58', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.230907678604126} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6941, 0.8954, 0.8378, ..., 0.6157, 0.9097, 0.4892]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.230907678604126 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6941, 0.8954, 0.8378, ..., 0.6157, 0.9097, 0.4892]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.230907678604126 seconds + +[39.15, 38.49, 39.55, 38.34, 39.46, 38.47, 39.32, 38.41, 38.92, 38.4] +[72.74] +13.08204436302185 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 58, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.230907678604126, 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b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_mario002.json new file mode 100644 index 0000000..af70f4d --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 164, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.193230390548706, "TIME_S_1KI": 62.15384384480918, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 985.1247665071487, "W": 76.66, "J_1KI": 6006.858332360663, "W_1KI": 467.4390243902439, "W_D": 41.51175, "J_D": 533.4496872691512, "W_D_1KI": 253.12042682926827, "J_D_1KI": 1543.4172367638307} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_mario002.output b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_mario002.output new file mode 100644 index 0000000..c57ce17 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.637404203414917} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.8745, 0.2349, 0.8071, ..., 0.8209, 0.9739, 0.5990]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.637404203414917 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '164', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.193230390548706} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.9610, 0.5756, 0.1344, ..., 0.3073, 0.5171, 0.6260]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) 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a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.json b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.json new file mode 100644 index 0000000..a5e6cc2 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.329004049301147, "TIME_S_1KI": 573.8335582945082, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 955.633627319336, "W": 68.48, "J_1KI": 53090.757073296445, "W_1KI": 3804.4444444444443, "W_D": 33.47175000000001, "J_D": 467.0959384524823, "W_D_1KI": 1859.5416666666672, "J_D_1KI": 103307.8703703704} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.output b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..4473b06 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_msdoor.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 5.811160326004028} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.6964, 0.8661, 0.3713, ..., 0.0236, 0.8266, 0.1911]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 5.811160326004028 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '18', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.329004049301147} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.3796, 0.0872, 0.8222, ..., 0.5842, 0.0242, 0.7522]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.329004049301147 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.3796, 0.0872, 0.8222, ..., 0.5842, 0.0242, 0.7522]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.329004049301147 seconds + +[39.88, 39.68, 38.94, 38.42, 39.2, 38.24, 39.25, 38.35, 39.28, 38.22] +[68.48] +13.954930305480957 +{'CPU': 'Epyc 7313P', 'CORES': 16, 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"W_D_1KI": 1204.285714285714, "J_D_1KI": 43010.20408163264} diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_test1.output b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_test1.output new file mode 100644 index 0000000..5ee5f57 --- /dev/null +++ b/pytorch/output_389000+_16core/epyc_7313p_16_coo_10_10_10_test1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.7318027019500732} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.1777, 0.2943, 0.4487, ..., 0.9808, 0.9328, 0.6122]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 3.7318027019500732 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '28', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.37741208076477} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.9880, 0.2978, 0.6008, ..., 0.3725, 0.1138, 0.2491]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.37741208076477 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.9880, 0.2978, 0.6008, ..., 0.3725, 0.1138, 0.2491]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.37741208076477 seconds + +[40.14, 39.31, 38.5, 38.49, 38.57, 39.63, 38.53, 39.33, 38.44, 39.47] +[68.82] +13.786789894104004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.37741208076477, 'TIME_S_1KI': 370.62186002731323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 948.8068805122374, 'W': 68.82} +[40.14, 39.31, 38.5, 38.49, 38.57, 39.63, 38.53, 39.33, 38.44, 39.47, 40.16, 38.74, 38.59, 39.33, 38.44, 40.4, 38.7, 39.27, 38.39, 38.91] +702.0 +35.1 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.37741208076477, 'TIME_S_1KI': 370.62186002731323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 948.8068805122374, 'W': 68.82, 'J_1KI': 33885.960018294194, 'W_1KI': 2457.8571428571427, 'W_D': 33.71999999999999, 'J_D': 464.8905552291869, 'W_D_1KI': 1204.285714285714, 'J_D_1KI': 43010.20408163264} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..1a6f0e6 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 77, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.254347562789917, "TIME_S_1KI": 133.17334497129764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 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1.9930237750099465e-05, "TIME_S": 1.3474905490875244} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.7537, 0.2277, 0.2410, ..., 0.4977, 0.3821, 0.0641]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 1.3474905490875244 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '77', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, 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400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.254347562789917 seconds + +[18.87, 21.6, 17.84, 17.47, 18.42, 17.58, 17.51, 17.65, 17.83, 17.46] +[53.33] +14.251187324523926 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.254347562789917, 'TIME_S_1KI': 133.17334497129764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.015820016861, 'W': 53.33} +[18.87, 21.6, 17.84, 17.47, 18.42, 17.58, 17.51, 17.65, 17.83, 17.46, 18.22, 17.55, 18.12, 17.78, 17.6, 17.5, 18.37, 17.46, 17.6, 17.51] +323.91 +16.195500000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.254347562789917, 'TIME_S_1KI': 133.17334497129764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.015820016861, 'W': 53.33, 'J_1KI': 9870.335324894299, 'W_1KI': 692.5974025974025, 'W_D': 37.134499999999996, 'J_D': 529.2107157025337, 'W_D_1KI': 482.2662337662337, 'J_D_1KI': 6263.197841119919} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..1fad455 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 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[392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.9949631690979004} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.9925, 0.4330, 0.1828, ..., 0.8001, 0.4801, 0.2464]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.9949631690979004 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '105', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16'] +{"MATRIX_TYPE": 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size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.9576, 0.5984, 0.0037, ..., 0.0961, 0.2003, 0.8345]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.336423397064209 seconds + +[18.25, 17.7, 17.68, 18.22, 18.16, 17.63, 17.72, 17.97, 17.96, 17.86] +[54.1] +14.092806577682495 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.336423397064209, 'TIME_S_1KI': 98.44212759108771, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 762.420835852623, 'W': 54.1} +[18.25, 17.7, 17.68, 18.22, 18.16, 17.63, 17.72, 17.97, 17.96, 17.86, 18.43, 17.95, 17.78, 18.07, 18.85, 18.16, 18.37, 17.77, 17.86, 17.84] +324.03999999999996 +16.201999999999998 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.336423397064209, 'TIME_S_1KI': 98.44212759108771, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 762.420835852623, 'W': 54.1, 'J_1KI': 7261.150817644028, 'W_1KI': 515.2380952380953, 'W_D': 37.898, 'J_D': 534.0891836810113, 'W_D_1KI': 360.9333333333334, 'J_D_1KI': 3437.460317460318} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.json b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.json new file mode 100644 index 0000000..c05da45 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 215, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.087749004364014, "TIME_S_1KI": 46.919762810995415, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 763.481853518486, "W": 55.86, "J_1KI": 3551.0783884580746, "W_1KI": 259.81395348837214, "W_D": 39.6545, "J_D": 541.9887425769567, "W_D_1KI": 184.4395348837209, "J_D_1KI": 857.8583017847484} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.output b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.output new file mode 100644 index 0000000..bcd66c2 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_language.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.4869344234466553} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.8840, 0.7061, 0.4568, ..., 0.5397, 0.8562, 0.2748]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.4869344234466553 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '215', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.087749004364014} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.9507, 0.2421, 0.7401, ..., 0.9797, 0.0911, 0.9488]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.087749004364014 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.9507, 0.2421, 0.7401, ..., 0.9797, 0.0911, 0.9488]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.087749004364014 seconds + +[22.61, 17.79, 17.77, 17.79, 17.85, 17.67, 17.71, 18.34, 17.94, 17.8] +[55.86] +13.667773962020874 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 215, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.087749004364014, 'TIME_S_1KI': 46.919762810995415, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 763.481853518486, 'W': 55.86} +[22.61, 17.79, 17.77, 17.79, 17.85, 17.67, 17.71, 18.34, 17.94, 17.8, 18.32, 17.91, 18.06, 17.88, 17.72, 17.83, 18.05, 17.65, 17.89, 17.79] +324.11 +16.2055 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 215, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.087749004364014, 'TIME_S_1KI': 46.919762810995415, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 763.481853518486, 'W': 55.86, 'J_1KI': 3551.0783884580746, 'W_1KI': 259.81395348837214, 'W_D': 39.6545, 'J_D': 541.9887425769567, 'W_D_1KI': 184.4395348837209, 'J_D_1KI': 857.8583017847484} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.json b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.json new file mode 100644 index 0000000..fe1b45e --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 47, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.327017784118652, "TIME_S_1KI": 219.7237826408224, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 772.0291032004357, "W": 53.83, "J_1KI": 16426.15113192416, "W_1KI": 1145.3191489361702, "W_D": 37.69475, "J_D": 540.6175745469927, "W_D_1KI": 802.0159574468086, "J_D_1KI": 17064.169307378903} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.output b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.output new file mode 100644 index 0000000..70f96a0 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 2.1987831592559814} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.3511, 0.2996, 0.9993, ..., 0.9615, 0.6656, 0.4347]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 2.1987831592559814 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '47', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.327017784118652} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6780, 0.4945, 0.4849, ..., 0.7922, 0.5342, 0.1189]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.327017784118652 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6780, 0.4945, 0.4849, ..., 0.7922, 0.5342, 0.1189]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.327017784118652 seconds + +[18.25, 18.06, 17.94, 17.78, 17.64, 18.11, 17.56, 17.64, 17.8, 18.2] +[53.83] +14.341985940933228 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 47, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 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b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.json new file mode 100644 index 0000000..458fc47 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 130, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.277937412261963, "TIME_S_1KI": 79.0610570173997, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 761.6858348536491, "W": 54.33, "J_1KI": 5859.121806566532, "W_1KI": 417.9230769230769, "W_D": 37.87025, "J_D": 530.9264308368563, "W_D_1KI": 291.30961538461537, "J_D_1KI": 2240.843195266272} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.output b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.output new file mode 100644 index 0000000..e103729 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.8022470474243164} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.9493, 0.2558, 0.7787, ..., 0.1890, 0.8243, 0.0970]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.8022470474243164 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '130', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.277937412261963} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.6748, 0.3800, 0.3206, ..., 0.2562, 0.6115, 0.5688]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.277937412261963 seconds + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.6748, 0.3800, 0.3206, ..., 0.2562, 0.6115, 0.5688]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.277937412261963 seconds + +[18.51, 17.91, 17.97, 18.79, 19.0, 18.67, 18.09, 18.11, 17.69, 17.57] +[54.33] +14.019617795944214 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 130, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 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diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.json b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.json new file mode 100644 index 0000000..f5ef325 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 14, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.01833987236023, "TIME_S_1KI": 715.5957051685879, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 812.1177711701393, "W": 54.11, "J_1KI": 58008.41222643852, "W_1KI": 3864.9999999999995, "W_D": 37.522499999999994, "J_D": 563.1618752306699, "W_D_1KI": 2680.1785714285706, "J_D_1KI": 191441.3265306122} diff --git a/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.output b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..cb06333 --- /dev/null +++ b/pytorch/output_389000+_16core/xeon_4216_16_coo_10_10_10_msdoor.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 7.114613771438599} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.0102, 0.2070, 0.2866, ..., 0.0409, 0.9140, 0.2865]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 7.114613771438599 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '14', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 9.983821392059326} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.9398, 0.5387, 0.6658, ..., 0.2061, 0.5323, 0.4978]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 9.983821392059326 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '14', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.01833987236023} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.1104, 0.4017, 0.9613, ..., 0.4739, 0.5251, 0.9828]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.01833987236023 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.1104, 0.4017, 0.9613, ..., 0.4739, 0.5251, 0.9828]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 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8.400361127706946e-05, "TIME_S": 4.575356721878052} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.4990, 0.7914, 0.6861, ..., 0.3990, 0.3110, 0.0256]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 4.575356721878052 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '22', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, 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0.4547, 0.9784]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.053152322769165 seconds + +[18.51, 17.74, 17.84, 17.63, 17.86, 17.98, 17.78, 17.66, 18.09, 17.63] +[53.58] +15.001968145370483 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 22, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.053152322769165, 'TIME_S_1KI': 456.9614692167802, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 803.8054532289505, 'W': 53.58} +[18.51, 17.74, 17.84, 17.63, 17.86, 17.98, 17.78, 17.66, 18.09, 17.63, 18.02, 17.96, 18.11, 17.66, 17.95, 17.84, 17.62, 17.85, 17.86, 18.05] +321.53499999999997 +16.076749999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 22, 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"MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.42647123336792, "TIME_S_1KI": 88.35992570650781, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.8359175777435, "W": 22.428269506583625, "J_1KI": 2786.745064218165, "W_1KI": 190.07008056426798, "W_D": 3.8172695065836244, "J_D": 55.96755115103717, "W_D_1KI": 32.34974158121716, "J_D_1KI": 274.1503523831963} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..4801a00 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/amazon0312.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.8844993114471436} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.8531, 0.9903, 0.1907, ..., 0.6178, 0.5730, 0.1427]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.8844993114471436 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 118 -m matrices/389000+_cols/amazon0312.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.42647123336792} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.7960, 0.3029, 0.2622, ..., 0.1249, 0.6362, 0.8154]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.42647123336792 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.7960, 0.3029, 0.2622, ..., 0.1249, 0.6362, 0.8154]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.42647123336792 seconds + +[20.76, 20.72, 20.56, 20.36, 20.48, 20.64, 20.68, 20.76, 20.64, 20.24] +[20.04, 19.96, 21.0, 22.32, 22.32, 24.32, 25.52, 26.4, 26.28, 26.6, 25.24, 25.08, 24.92, 24.84] +14.66167140007019 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.42647123336792, 'TIME_S_1KI': 88.35992570650781, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8359175777435, 'W': 22.428269506583625} +[20.76, 20.72, 20.56, 20.36, 20.48, 20.64, 20.68, 20.76, 20.64, 20.24, 20.08, 20.12, 20.2, 20.84, 21.12, 21.08, 21.08, 21.0, 21.0, 20.8] +372.22 +18.611 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.42647123336792, 'TIME_S_1KI': 88.35992570650781, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8359175777435, 'W': 22.428269506583625, 'J_1KI': 2786.745064218165, 'W_1KI': 190.07008056426798, 'W_D': 3.8172695065836244, 'J_D': 55.96755115103717, 'W_D_1KI': 32.34974158121716, 'J_D_1KI': 274.1503523831963} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..0756413 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 179, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.34884238243103, "TIME_S_1KI": 57.814761913022515, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.50949724197386, "W": 23.27814688227516, "J_1KI": 1907.8742862680106, "W_1KI": 130.04551330879977, "W_D": 4.72914688227516, "J_D": 69.38046152544023, "W_D_1KI": 26.419814984777428, "J_D_1KI": 147.5967317585331} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..43448d3 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/helm2d03.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.5865864753723145} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.7242, 0.5518, 0.9315, ..., 0.6424, 0.0724, 0.7341]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.5865864753723145 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 179 -m matrices/389000+_cols/helm2d03.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.34884238243103} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.0973, 0.1404, 0.8769, ..., 0.5073, 0.9593, 0.3478]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.34884238243103 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.0973, 0.1404, 0.8769, ..., 0.5073, 0.9593, 0.3478]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.34884238243103 seconds + +[20.72, 20.72, 20.32, 20.4, 20.36, 20.32, 20.4, 20.4, 20.4, 20.48] +[20.48, 20.68, 20.64, 24.16, 26.48, 27.4, 28.6, 26.64, 26.6, 25.44, 25.48, 25.16, 25.0, 24.76] +14.670819759368896 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 179, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.34884238243103, 'TIME_S_1KI': 57.814761913022515, 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b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_language.json new file mode 100644 index 0000000..0bb6f8c --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 372, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.207688808441162, "TIME_S_1KI": 27.440023678605275, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.7927499103546, "W": 22.33219484534502, "J_1KI": 878.4751341676199, "W_1KI": 60.03278184232532, "W_D": 3.981194845345019, "J_D": 58.257847938776024, "W_D_1KI": 10.702136681034998, "J_D_1KI": 28.769184626438168} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_language.output b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_language.output new file mode 100644 index 0000000..ac194a9 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_language.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/language.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.2821774482727051} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.3990, 0.5258, 0.6068, ..., 0.4899, 0.7657, 0.0813]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.2821774482727051 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 372 -m matrices/389000+_cols/language.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.207688808441162} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.2595, 0.1640, 0.9913, ..., 0.6702, 0.2615, 0.2087]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.207688808441162 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.2595, 0.1640, 0.9913, ..., 0.6702, 0.2615, 0.2087]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.207688808441162 seconds + +[20.44, 20.44, 20.52, 20.72, 20.52, 20.4, 20.48, 20.16, 20.28, 20.36] +[20.48, 20.52, 20.84, 22.04, 23.4, 24.56, 24.56, 25.84, 25.92, 25.6, 24.84, 24.76, 25.16, 25.16] +14.633257150650024 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 372, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 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28.769184626438168} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.json b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.json new file mode 100644 index 0000000..e9680c0 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 79, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.23322343826294, "TIME_S_1KI": 129.53447390206253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.62979657173156, "W": 20.897730759205288, "J_1KI": 4248.478437616855, "W_1KI": 264.52823745829477, "W_D": 2.311730759205286, "J_D": 37.12775005960458, "W_D_1KI": 29.262414673484635, "J_D_1KI": 370.4103123225903} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.output b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.output new file mode 100644 index 0000000..fc2756d --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/marine1.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.3190975189208984} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.2159, 0.9263, 0.0662, ..., 0.5560, 0.4037, 0.0575]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.3190975189208984 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 79 -m matrices/389000+_cols/marine1.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.23322343826294} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.0226, 0.2012, 0.8514, ..., 0.6390, 0.7546, 0.2394]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.23322343826294 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.0226, 0.2012, 0.8514, ..., 0.6390, 0.7546, 0.2394]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.23322343826294 seconds + +[20.56, 20.48, 20.44, 20.6, 20.6, 20.64, 20.52, 20.44, 20.6, 20.72] +[20.76, 20.72, 21.04, 22.04, 23.44, 24.68, 25.48, 25.52, 25.6, 24.4, 24.48, 24.6, 24.52, 24.76, 24.92] +16.060585737228394 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 79, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.23322343826294, 'TIME_S_1KI': 129.53447390206253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.62979657173156, 'W': 20.897730759205288} +[20.56, 20.48, 20.44, 20.6, 20.6, 20.64, 20.52, 20.44, 20.6, 20.72, 20.68, 20.8, 20.84, 20.84, 20.52, 20.6, 20.76, 20.8, 20.84, 20.84] +371.72 +18.586000000000002 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 79, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.23322343826294, 'TIME_S_1KI': 129.53447390206253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.62979657173156, 'W': 20.897730759205288, 'J_1KI': 4248.478437616855, 'W_1KI': 264.52823745829477, 'W_D': 2.311730759205286, 'J_D': 37.12775005960458, 'W_D_1KI': 29.262414673484635, 'J_D_1KI': 370.4103123225903} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.json b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.json new file mode 100644 index 0000000..c85544a --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 208, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.302243709564209, "TIME_S_1KI": 49.53001783444331, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.5055000305175, "W": 23.919871959330212, "J_1KI": 1685.122596300565, "W_1KI": 114.99938441985678, "W_D": 5.689871959330212, "J_D": 83.37550550460807, "W_D_1KI": 27.35515365062602, "J_D_1KI": 131.51516178185585} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.output b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.output new file mode 100644 index 0000000..22d7059 --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/mario002.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.5031006336212158} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.1542, 0.3790, 0.1583, ..., 0.9853, 0.1360, 0.4181]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.5031006336212158 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 208 -m matrices/389000+_cols/mario002.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.302243709564209} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.0394, 0.6620, 0.2548, ..., 0.1309, 0.3006, 0.4100]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.302243709564209 seconds + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.0394, 0.6620, 0.2548, ..., 0.1309, 0.3006, 0.4100]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.302243709564209 seconds + +[20.36, 20.12, 20.12, 19.68, 19.68, 19.72, 20.0, 20.04, 20.52, 20.64] +[20.84, 20.88, 23.56, 25.44, 27.24, 28.2, 29.48, 27.48, 27.48, 26.52, 25.48, 25.16, 24.84, 24.56] +14.653318405151367 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 208, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.302243709564209, 'TIME_S_1KI': 49.53001783444331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.5055000305175, 'W': 23.919871959330212} +[20.36, 20.12, 20.12, 19.68, 19.68, 19.72, 20.0, 20.04, 20.52, 20.64, 20.68, 20.56, 20.48, 20.64, 20.24, 20.4, 20.56, 20.36, 20.44, 20.4] +364.6 +18.23 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 208, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.302243709564209, 'TIME_S_1KI': 49.53001783444331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.5055000305175, 'W': 23.919871959330212, 'J_1KI': 1685.122596300565, 'W_1KI': 114.99938441985678, 'W_D': 5.689871959330212, 'J_D': 83.37550550460807, 'W_D_1KI': 27.35515365062602, 'J_D_1KI': 131.51516178185585} diff --git a/pytorch/output_389000+_1core/altra_1_coo_10_10_10_msdoor.json b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_msdoor.json new file mode 100644 index 0000000..96da49c --- /dev/null +++ b/pytorch/output_389000+_1core/altra_1_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 25, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 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415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 4.196976661682129} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4118, 0.0766, 0.9896, ..., 0.1976, 0.4777, 0.8870]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 4.196976661682129 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 25 -m matrices/389000+_cols/msdoor.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.48160195350647} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.8535, 0.7550, 0.2820, ..., 0.4483, 0.9345, 0.3205]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.48160195350647 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.8535, 0.7550, 0.2820, ..., 0.4483, 0.9345, 0.3205]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.48160195350647 seconds + +[20.4, 20.72, 20.44, 20.52, 20.64, 20.28, 20.32, 20.2, 20.44, 20.64] +[20.6, 20.6, 23.96, 25.04, 26.52, 28.32, 29.04, 26.16, 25.28, 25.56, 24.8, 24.6, 24.92, 24.88, 24.68, 25.08] +17.548298120498657 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 25, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.48160195350647, 'TIME_S_1KI': 419.2640781402588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 390.95131686210624, 'W': 22.278588737071036} +[20.4, 20.72, 20.44, 20.52, 20.64, 20.28, 20.32, 20.2, 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'-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/test1.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 2.733165979385376} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.0428, 0.6726, 0.0165, ..., 0.5617, 0.0517, 0.8990]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 2.733165979385376 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 38 -m matrices/389000+_cols/test1.mtx -c 1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.370359182357788} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.8359, 0.7992, 0.2566, ..., 0.7668, 0.0927, 0.6220]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.370359182357788 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.8359, 0.7992, 0.2566, ..., 0.7668, 0.0927, 0.6220]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.370359182357788 seconds + +[20.4, 20.24, 20.16, 20.32, 20.48, 20.48, 20.52, 20.48, 20.36, 20.16] +[20.2, 20.36, 20.96, 27.88, 28.8, 30.24, 27.44, 26.32, 26.32, 25.16, 24.68, 24.88, 24.72, 24.72, 24.84] +16.592537879943848 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 38, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 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a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..eca4254 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 112, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.258270025253296, "TIME_S_1KI": 91.59169665404728, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 863.5131076049804, "W": 65.44, "J_1KI": 7709.938460758754, "W_1KI": 584.2857142857143, "W_D": 30.220749999999995, "J_D": 398.77771617746345, "W_D_1KI": 269.828125, "J_D_1KI": 2409.1796875} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..dab1b94 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.9330589771270752} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.5874, 0.2343, 0.6606, ..., 0.4897, 0.9784, 0.1786]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.9330589771270752 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '112', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.258270025253296} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.5028, 0.5163, 0.3573, ..., 0.3849, 0.9903, 0.9560]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.258270025253296 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.5028, 0.5163, 0.3573, ..., 0.3849, 0.9903, 0.9560]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.258270025253296 seconds + +[40.11, 38.74, 39.96, 38.89, 39.32, 38.86, 39.18, 39.03, 39.39, 39.1] +[65.44] +13.195493698120117 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 112, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 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2409.1796875} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..c3e8f8d --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 133, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.395626068115234, "TIME_S_1KI": 78.16260201590401, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.5650195121766, "W": 64.84, "J_1KI": 6508.007665505088, "W_1KI": 487.5187969924812, "W_D": 29.441000000000003, "J_D": 393.0151101088524, "W_D_1KI": 221.36090225563913, "J_D_1KI": 1664.367686132625} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..945bedb --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.7890067100524902} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.6340, 0.6548, 0.6497, ..., 0.4649, 0.5624, 0.0700]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.7890067100524902 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '133', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.395626068115234} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.3991, 0.2131, 0.1886, ..., 0.5996, 0.9337, 0.0168]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.395626068115234 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.3991, 0.2131, 0.1886, ..., 0.5996, 0.9337, 0.0168]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.395626068115234 seconds + +[39.52, 38.66, 39.73, 43.42, 39.12, 39.17, 39.14, 38.98, 38.91, 39.74] +[64.84] +13.349244594573975 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 133, 'MATRIX_TYPE': 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100.6962457337884, "J_D_1KI": 343.6731936306772} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_language.output b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_language.output new file mode 100644 index 0000000..e2c0955 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_language.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.35765957832336426} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5605, 0.3613, 0.6180, ..., 0.8010, 0.0807, 0.0320]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.35765957832336426 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '293', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.13910436630249} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5123, 0.8630, 0.7218, ..., 0.4435, 0.1290, 0.1042]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.13910436630249 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5123, 0.8630, 0.7218, ..., 0.4435, 0.1290, 0.1042]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.13910436630249 seconds + +[40.2, 38.6, 39.85, 38.63, 39.47, 39.53, 38.79, 38.95, 39.14, 39.0] +[64.7] +13.00883674621582 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 293, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.13910436630249, 'TIME_S_1KI': 34.60445176212454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.6717374801636, 'W': 64.7} +[40.2, 38.6, 39.85, 38.63, 39.47, 39.53, 38.79, 38.95, 39.14, 39.0, 39.33, 39.08, 38.92, 38.72, 39.93, 38.71, 38.64, 39.27, 39.1, 38.65] +703.9200000000001 +35.196000000000005 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 293, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.13910436630249, 'TIME_S_1KI': 34.60445176212454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.6717374801636, 'W': 64.7, 'J_1KI': 2872.599786621719, 'W_1KI': 220.81911262798636, 'W_D': 29.503999999999998, 'J_D': 383.81271936035154, 'W_D_1KI': 100.6962457337884, 'J_D_1KI': 343.6731936306772} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.json b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.json new file mode 100644 index 0000000..7c55c37 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 59, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.364520788192749, "TIME_S_1KI": 175.6698438676737, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 961.4618274116517, "W": 65.62, "J_1KI": 16295.963176468675, "W_1KI": 1112.2033898305085, "W_D": 30.160250000000005, "J_D": 441.9068741266728, "W_D_1KI": 511.19067796610176, "J_D_1KI": 8664.24877908647} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.output b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.output new file mode 100644 index 0000000..7b7d633 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7657136917114258} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.6077, 0.2167, 0.6786, ..., 0.1750, 0.5259, 0.2235]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.7657136917114258 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '59', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.364520788192749} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.9822, 0.8834, 0.6037, ..., 0.0197, 0.1253, 0.1122]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.364520788192749 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.9822, 0.8834, 0.6037, ..., 0.0197, 0.1253, 0.1122]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.364520788192749 seconds + +[44.79, 38.85, 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+tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.6279, 0.8600, 0.4207, ..., 0.8405, 0.2520, 0.5539]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 7.1603169441223145 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '174', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.467077732086182} + 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"TIME_S": 5.76108193397522} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.2249, 0.2866, 0.3884, ..., 0.3376, 0.6837, 0.0302]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 5.76108193397522 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '18', '-m', 'matrices/389000+_cols/msdoor.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.285197496414185} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.6145, 0.4380, 0.6420, ..., 0.8060, 0.5360, 0.8878]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.285197496414185 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.6145, 0.4380, 0.6420, ..., 0.8060, 0.5360, 0.8878]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.285197496414185 seconds + +[39.34, 38.54, 39.14, 38.52, 38.62, 38.53, 39.08, 39.07, 39.07, 38.67] +[66.05] +15.256330490112305 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.285197496414185, 'TIME_S_1KI': 571.399860911899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.6806288719176, 'W': 66.05} +[39.34, 38.54, 39.14, 38.52, 38.62, 38.53, 39.08, 39.07, 39.07, 38.67, 39.81, 38.75, 38.91, 38.55, 38.86, 39.09, 38.8, 41.1, 38.75, 38.55] +701.565 +35.078250000000004 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.285197496414185, 'TIME_S_1KI': 571.399860911899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.6806288719176, 'W': 66.05, 'J_1KI': 55982.25715955098, 'W_1KI': 3669.4444444444443, 'W_D': 30.971749999999993, 'J_D': 472.5152538571357, 'W_D_1KI': 1720.6527777777774, 'J_D_1KI': 95591.8209876543} diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_test1.json b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_test1.json new file mode 100644 index 0000000..8031988 --- /dev/null +++ b/pytorch/output_389000+_1core/epyc_7313p_1_coo_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 28, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, 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392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.700636386871338} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.1321, 0.9082, 0.6338, ..., 0.5624, 0.7605, 0.7399]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 3.700636386871338 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '28', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.235199689865112} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.8475, 0.2021, 0.3393, ..., 0.1129, 0.8066, 0.4919]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.235199689865112 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.8475, 0.2021, 0.3393, ..., 0.1129, 0.8066, 0.4919]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.235199689865112 seconds + +[39.34, 40.0, 39.04, 38.84, 38.72, 38.76, 38.81, 39.11, 39.25, 39.21] +[65.82] +14.941608190536499 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.235199689865112, 'TIME_S_1KI': 365.54284606661116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 983.4566511011122, 'W': 65.82} +[39.34, 40.0, 39.04, 38.84, 38.72, 38.76, 38.81, 39.11, 39.25, 39.21, 40.9, 38.68, 39.21, 38.85, 39.04, 38.67, 39.25, 38.75, 39.48, 38.9] +703.635 +35.18175 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.235199689865112, 'TIME_S_1KI': 365.54284606661116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 983.4566511011122, 'W': 65.82, 'J_1KI': 35123.45182503972, 'W_1KI': 2350.7142857142853, 'W_D': 30.638249999999992, 'J_D': 457.7847271437048, 'W_D_1KI': 1094.223214285714, 'J_D_1KI': 39079.400510204076} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..87ea424 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 78, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.350221633911133, "TIME_S_1KI": 132.69514915270682, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 787.6715193271638, "W": 52.7, "J_1KI": 10098.352811886714, "W_1KI": 675.6410256410256, "W_D": 35.49875, "J_D": 530.5759838086367, "W_D_1KI": 455.1121794871795, "J_D_1KI": 5834.771531886917} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..ea98314 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.3456165790557861} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.7578, 0.8738, 0.7812, ..., 0.1908, 0.2600, 0.1843]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 1.3456165790557861 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '78', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.350221633911133} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.9341, 0.7942, 0.5455, ..., 0.8242, 0.0511, 0.9771]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.350221633911133 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.9341, 0.7942, 0.5455, ..., 0.8242, 0.0511, 0.9771]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.350221633911133 seconds + +[19.2, 18.65, 18.64, 18.58, 18.9, 22.43, 19.13, 18.44, 19.45, 18.64] +[52.7] +14.94632863998413 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 78, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.350221633911133, 'TIME_S_1KI': 132.69514915270682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 787.6715193271638, 'W': 52.7} +[19.2, 18.65, 18.64, 18.58, 18.9, 22.43, 19.13, 18.44, 19.45, 18.64, 22.48, 18.8, 18.56, 19.18, 18.6, 18.69, 18.73, 18.45, 19.06, 19.15] +344.025 +17.201249999999998 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 78, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.350221633911133, 'TIME_S_1KI': 132.69514915270682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 787.6715193271638, 'W': 52.7, 'J_1KI': 10098.352811886714, 'W_1KI': 675.6410256410256, 'W_D': 35.49875, 'J_D': 530.5759838086367, 'W_D_1KI': 455.1121794871795, 'J_D_1KI': 5834.771531886917} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..6e74b45 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 106, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.285914182662964, "TIME_S_1KI": 97.0369262515374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 778.2659219670296, "W": 53.03, "J_1KI": 7342.131339311601, "W_1KI": 500.28301886792457, "W_D": 35.92175, "J_D": 527.1860057027936, "W_D_1KI": 338.8844339622642, "J_D_1KI": 3197.022961908153} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..f59d00c --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.9858012199401855} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.5787, 0.8820, 0.9111, ..., 0.9416, 0.9789, 0.7593]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.9858012199401855 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '106', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.285914182662964} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.1615, 0.5061, 0.8321, ..., 0.7824, 0.7226, 0.3625]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.285914182662964 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.1615, 0.5061, 0.8321, ..., 0.7824, 0.7226, 0.3625]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.285914182662964 seconds + +[19.12, 18.9, 18.8, 22.81, 18.83, 18.49, 19.6, 18.61, 18.53, 18.45] +[53.03] +14.675955533981323 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 106, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.285914182662964, 'TIME_S_1KI': 97.0369262515374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 778.2659219670296, 'W': 53.03} +[19.12, 18.9, 18.8, 22.81, 18.83, 18.49, 19.6, 18.61, 18.53, 18.45, 18.79, 19.35, 18.62, 18.67, 18.63, 18.44, 18.54, 19.16, 18.71, 18.59] +342.16499999999996 +17.108249999999998 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 106, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.285914182662964, 'TIME_S_1KI': 97.0369262515374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 778.2659219670296, 'W': 53.03, 'J_1KI': 7342.131339311601, 'W_1KI': 500.28301886792457, 'W_D': 35.92175, 'J_D': 527.1860057027936, 'W_D_1KI': 338.8844339622642, 'J_D_1KI': 3197.022961908153} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.json b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.json new file mode 100644 index 0000000..305747e --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 219, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.081467151641846, "TIME_S_1KI": 46.034096582839474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.2577638244628, "W": 53.12, "J_1KI": 3439.533168148232, "W_1KI": 242.55707762557077, "W_D": 36.251999999999995, "J_D": 514.0643910799026, "W_D_1KI": 165.53424657534242, "J_D_1KI": 755.8641396134357} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.output b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.output new file mode 100644 index 0000000..bdd374b --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_language.output @@ -0,0 +1,59 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.4792313575744629} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5325, 0.3541, 0.4678, ..., 0.7358, 0.9635, 0.6558]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.4792313575744629 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '219', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.081467151641846} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.8318, 0.2147, 0.8221, ..., 0.0090, 0.7154, 0.3050]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.081467151641846 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.8318, 0.2147, 0.8221, ..., 0.0090, 0.7154, 0.3050]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.081467151641846 seconds + +[19.37, 18.43, 18.79, 18.48, 19.21, 18.42, 18.51, 18.77, 18.93, 18.43] +[53.12] +14.180304288864136 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 219, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 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diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.json b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.json new file mode 100644 index 0000000..b1d01fb --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 47, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.328421354293823, "TIME_S_1KI": 219.7536458360388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 848.0926424980164, "W": 53.2, "J_1KI": 18044.524308468437, "W_1KI": 1131.9148936170213, "W_D": 36.205000000000005, "J_D": 577.1653030383587, "W_D_1KI": 770.3191489361703, "J_D_1KI": 16389.769126301493} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.output b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.output new file mode 100644 index 0000000..741aab4 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 2.2054905891418457} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.9154, 0.1846, 0.9485, ..., 0.4631, 0.0963, 0.4258]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 2.2054905891418457 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '47', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.328421354293823} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.7453, 0.6480, 0.1736, ..., 0.9772, 0.4216, 0.4950]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.328421354293823 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.7453, 0.6480, 0.1736, ..., 0.9772, 0.4216, 0.4950]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.328421354293823 seconds + +[18.94, 19.02, 18.78, 18.59, 18.75, 18.93, 18.73, 18.66, 18.56, 18.95] +[53.2] +15.941591024398804 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 47, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.328421354293823, 'TIME_S_1KI': 219.7536458360388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 848.0926424980164, 'W': 53.2} +[18.94, 19.02, 18.78, 18.59, 18.75, 18.93, 18.73, 18.66, 18.56, 18.95, 19.13, 19.14, 18.57, 19.66, 18.71, 18.74, 18.64, 19.29, 19.21, 18.82] +339.9 +16.994999999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 47, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.328421354293823, 'TIME_S_1KI': 219.7536458360388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 848.0926424980164, 'W': 53.2, 'J_1KI': 18044.524308468437, 'W_1KI': 1131.9148936170213, 'W_D': 36.205000000000005, 'J_D': 577.1653030383587, 'W_D_1KI': 770.3191489361703, 'J_D_1KI': 16389.769126301493} diff --git a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_mario002.json b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_mario002.json new file mode 100644 index 0000000..67df199 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 132, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.172460079193115, "TIME_S_1KI": 77.06409150903876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 767.5956591558457, "W": 53.09, "J_1KI": 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+tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.8898, 0.1489, 0.9742, ..., 0.4998, 0.5403, 0.0371]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.793898344039917 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '132', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.172460079193115} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.5137, 0.2358, 0.1035, ..., 0.9637, 0.3715, 0.6203]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.172460079193115 seconds + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.5137, 0.2358, 0.1035, ..., 0.9637, 0.3715, 0.6203]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.172460079193115 seconds + +[19.27, 18.6, 18.6, 18.55, 19.15, 18.64, 18.76, 18.6, 18.82, 18.72] +[53.09] +14.458384990692139 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 132, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.172460079193115, 'TIME_S_1KI': 77.06409150903876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 767.5956591558457, 'W': 53.09} +[19.27, 18.6, 18.6, 18.55, 19.15, 18.64, 18.76, 18.6, 18.82, 18.72, 19.0, 18.93, 18.7, 18.52, 19.04, 18.69, 18.61, 19.44, 18.55, 18.92] +338.15500000000003 +16.90775 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 132, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 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a/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_test1.output b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_test1.output new file mode 100644 index 0000000..215db94 --- /dev/null +++ b/pytorch/output_389000+_1core/xeon_4216_1_coo_10_10_10_test1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 4.5700438022613525} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.6505, 0.6654, 0.6056, ..., 0.6609, 0.0170, 0.3740]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 4.5700438022613525 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '22', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.045366048812866} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + 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400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.9505, 0.6447, 0.0685, ..., 0.2560, 0.0763, 0.0408]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 1.0661427974700928 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 98 -m matrices/389000+_cols/amazon0312.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 12.50285267829895} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.0494, 0.3118, 0.4120, ..., 0.6848, 0.1070, 0.9280]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 12.50285267829895 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.0494, 0.3118, 0.4120, ..., 0.6848, 0.1070, 0.9280]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 12.50285267829895 seconds + +[20.92, 21.0, 20.96, 20.92, 21.0, 21.08, 21.08, 21.04, 21.28, 21.48] +[21.4, 21.48, 21.84, 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392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.0879, 0.8018, 0.5457, ..., 0.6785, 0.5012, 0.4748]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.9020431041717529 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 116 -m matrices/389000+_cols/helm2d03.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.264106750488281} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4816, 0.4208, 0.1106, ..., 0.6357, 0.6404, 0.5046]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.264106750488281 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4816, 0.4208, 0.1106, ..., 0.6357, 0.6404, 0.5046]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.264106750488281 seconds + +[21.04, 21.04, 21.12, 21.12, 21.08, 20.92, 20.84, 20.8, 20.64, 20.52] +[20.24, 20.52, 23.8, 25.88, 30.72, 30.72, 47.16, 61.36, 73.56, 85.92, 89.8, 88.36, 86.32, 85.24] +14.49177360534668 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 116, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.264106750488281, 'TIME_S_1KI': 88.48367888351967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.778782119751, 'W': 52.35927656500255} +[21.04, 21.04, 21.12, 21.12, 21.08, 20.92, 20.84, 20.8, 20.64, 20.52, 21.12, 21.16, 20.96, 20.72, 20.76, 20.76, 20.8, 20.8, 20.52, 20.52] +375.64 +18.782 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 116, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.264106750488281, 'TIME_S_1KI': 88.48367888351967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.778782119751, 'W': 52.35927656500255, 'J_1KI': 6541.19639758406, 'W_1KI': 451.37307383622885, 'W_D': 33.57727656500255, 'J_D': 486.59429026412954, 'W_D_1KI': 289.4592807327806, 'J_D_1KI': 2495.3386270067294} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.json b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.json new file mode 100644 index 0000000..27044db --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 277, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.961499452590942, "TIME_S_1KI": 39.572200189859, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 920.4506818199158, "W": 55.830706536789286, "J_1KI": 3322.926649169371, "W_1KI": 201.55489724472667, "W_D": 37.00970653678928, "J_D": 610.1590276901721, "W_D_1KI": 133.60904886927537, "J_D_1KI": 482.3431367121854} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.output b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.output new file mode 100644 index 0000000..f159b95 --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_language.output @@ -0,0 +1,77 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.6067090034484863} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.0329, 0.4020, 0.6833, ..., 0.1615, 0.4251, 0.6942]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.6067090034484863 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 173 -m matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 6.540848970413208} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.4442, 0.4038, 0.0035, ..., 0.9209, 0.3652, 0.4475]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 6.540848970413208 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 277 -m matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.961499452590942} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.8618, 0.2877, 0.7105, ..., 0.6617, 0.4024, 0.3293]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.961499452590942 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.8618, 0.2877, 0.7105, ..., 0.6617, 0.4024, 0.3293]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.961499452590942 seconds + +[21.0, 21.0, 20.76, 20.84, 20.84, 20.84, 20.8, 20.92, 20.68, 20.84] +[21.04, 21.0, 24.24, 26.32, 33.16, 33.16, 46.48, 60.68, 70.92, 81.84, 86.76, 85.52, 84.88, 85.84, 85.4, 84.88] +16.486459493637085 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.961499452590942, 'TIME_S_1KI': 39.572200189859, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 920.4506818199158, 'W': 55.830706536789286} +[21.0, 21.0, 20.76, 20.84, 20.84, 20.84, 20.8, 20.92, 20.68, 20.84, 20.56, 20.52, 20.76, 20.72, 21.0, 21.0, 21.36, 21.2, 21.32, 21.32] +376.42 +18.821 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.961499452590942, 'TIME_S_1KI': 39.572200189859, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 920.4506818199158, 'W': 55.830706536789286, 'J_1KI': 3322.926649169371, 'W_1KI': 201.55489724472667, 'W_D': 37.00970653678928, 'J_D': 610.1590276901721, 'W_D_1KI': 133.60904886927537, 'J_D_1KI': 482.3431367121854} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_marine1.json new file mode 100644 index 0000000..425fa02 --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 64, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 11.195725679397583, "TIME_S_1KI": 174.93321374058723, 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+Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 11.195725679397583 seconds + +[20.72, 20.92, 21.12, 21.08, 21.12, 21.08, 21.2, 21.04, 20.96, 20.88] +[20.68, 21.04, 21.04, 24.56, 25.8, 36.04, 46.4, 58.76, 66.48, 77.8, 79.12, 81.76, 87.08, 88.0, 91.48, 91.32] +16.65320920944214 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 64, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 11.195725679397583, 'TIME_S_1KI': 174.93321374058723, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 921.011065006256, 'W': 55.30532003909826} +[20.72, 20.92, 21.12, 21.08, 21.12, 21.08, 21.2, 21.04, 20.96, 20.88, 21.28, 21.36, 21.12, 21.08, 20.88, 20.88, 20.76, 20.6, 20.56, 20.76] +377.58000000000004 +18.879 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 64, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 11.195725679397583, 'TIME_S_1KI': 174.93321374058723, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 921.011065006256, 'W': 55.30532003909826, 'J_1KI': 14390.79789072275, 'W_1KI': 864.1456256109103, 'W_D': 36.42632003909826, 'J_D': 606.6151283411979, 'W_D_1KI': 569.1612506109103, 'J_D_1KI': 8893.144540795474} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.json new file mode 100644 index 0000000..a548456 --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 190, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 14.603139877319336, "TIME_S_1KI": 76.85863093325966, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2405.9824257564537, "W": 69.56329259421916, "J_1KI": 12663.065398718178, "W_1KI": 366.1225926011535, "W_D": 50.577292594219166, "J_D": 1749.3145103678696, "W_D_1KI": 266.19627681167987, "J_D_1KI": 1401.0330358509466} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.output new file mode 100644 index 0000000..8d46b10 --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_mario002.output @@ -0,0 +1,77 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 1.286492109298706} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.1392, 0.9430, 0.9762, ..., 0.8832, 0.2899, 0.9813]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 1.286492109298706 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 81 -m matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 4.456863164901733} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.7242, 0.5892, 0.0728, ..., 0.5936, 0.7137, 0.5560]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 4.456863164901733 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 190 -m matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, 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152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 14.603139877319336 seconds + +[20.84, 20.96, 21.08, 20.96, 21.0, 20.96, 21.0, 21.0, 21.08, 21.32] +[21.44, 21.56, 21.88, 25.64, 27.36, 41.28, 55.08, 68.0, 81.88, 91.08, 90.64, 89.88, 88.36, 86.76, 85.6, 85.32, 85.32, 85.52, 85.32, 84.44, 85.2, 84.0, 83.04, 82.12, 82.2, 80.48, 81.04, 83.08, 83.88, 85.44, 86.0, 86.08, 84.44] +34.58695435523987 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 190, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 14.603139877319336, 'TIME_S_1KI': 76.85863093325966, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2405.9824257564537, 'W': 69.56329259421916} +[20.84, 20.96, 21.08, 20.96, 21.0, 20.96, 21.0, 21.0, 21.08, 21.32, 21.4, 21.52, 21.32, 21.24, 21.24, 21.0, 21.0, 20.96, 21.08, 21.08] 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"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 11.207796812057495, "TIME_S_1KI": 466.99153383572894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 626.2113814544678, "W": 38.17449578062175, "J_1KI": 26092.140893936157, "W_1KI": 1590.6039908592395, "W_D": 19.62949578062175, "J_D": 322.00068183422087, "W_D_1KI": 817.8956575259062, "J_D_1KI": 34078.985730246095} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..31fd72e --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_msdoor.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 5.270718097686768} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.9852, 0.2493, 0.1115, ..., 0.7409, 0.5809, 0.0205]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 5.270718097686768 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 19 -m matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 8.299206972122192} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.7835, 0.2436, 0.8418, ..., 0.5677, 0.0321, 0.3815]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 8.299206972122192 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 24 -m matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 11.207796812057495} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4055, 0.9965, 0.1099, ..., 0.1105, 0.0618, 0.7541]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.207796812057495 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4055, 0.9965, 0.1099, ..., 0.1105, 0.0618, 0.7541]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 11.207796812057495 seconds + +[20.64, 20.52, 20.4, 20.4, 20.56, 21.0, 20.88, 20.84, 20.84, 20.4] +[20.32, 20.44, 21.12, 22.84, 27.24, 37.4, 42.72, 42.72, 50.04, 55.8, 51.24, 52.08, 49.48, 50.2, 47.68, 49.84] +16.40392017364502 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 24, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 11.207796812057495, 'TIME_S_1KI': 466.99153383572894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 626.2113814544678, 'W': 38.17449578062175} +[20.64, 20.52, 20.4, 20.4, 20.56, 21.0, 20.88, 20.84, 20.84, 20.4, 20.32, 20.32, 20.44, 20.48, 20.44, 20.48, 20.48, 20.8, 20.92, 20.84] +370.9 +18.544999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 24, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 11.207796812057495, 'TIME_S_1KI': 466.99153383572894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 626.2113814544678, 'W': 38.17449578062175, 'J_1KI': 26092.140893936157, 'W_1KI': 1590.6039908592395, 'W_D': 19.62949578062175, 'J_D': 322.00068183422087, 'W_D_1KI': 817.8956575259062, 'J_D_1KI': 34078.985730246095} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.json b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.json new file mode 100644 index 0000000..edfe346 --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 35, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 11.63770079612732, "TIME_S_1KI": 332.50573703220914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 660.6712235164642, "W": 42.76027469966683, "J_1KI": 18876.32067189898, "W_1KI": 1221.7221342761952, "W_D": 23.82927469966683, "J_D": 368.1762145335674, "W_D_1KI": 680.8364199904809, "J_D_1KI": 19452.46914258517} diff --git a/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.output b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.output new file mode 100644 index 0000000..2d24dbf --- /dev/null +++ b/pytorch/output_389000+_maxcore/altra_max_coo_10_10_10_test1.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 10 -m matrices/389000+_cols/test1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.1338207721710205} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.3488, 0.1981, 0.1405, ..., 0.9231, 0.3819, 0.4583]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 3.1338207721710205 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 33 -m matrices/389000+_cols/test1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 9.847631216049194} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.0441, 0.5206, 0.9715, ..., 0.1738, 0.9140, 0.1638]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 9.847631216049194 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 35 -m matrices/389000+_cols/test1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 11.63770079612732} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.3106, 0.6373, 0.0752, ..., 0.4338, 0.2866, 0.1473]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 11.63770079612732 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.3106, 0.6373, 0.0752, ..., 0.4338, 0.2866, 0.1473]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 11.63770079612732 seconds + +[21.08, 20.84, 20.84, 20.72, 20.8, 21.28, 21.24, 21.12, 21.04, 20.64] +[20.2, 20.2, 20.88, 22.48, 25.2, 37.96, 37.96, 45.64, 57.92, 63.84, 65.96, 65.08, 64.64, 62.24, 63.72] +15.450584173202515 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 35, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 11.63770079612732, 'TIME_S_1KI': 332.50573703220914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6712235164642, 'W': 42.76027469966683} +[21.08, 20.84, 20.84, 20.72, 20.8, 21.28, 21.24, 21.12, 21.04, 20.64, 20.6, 20.68, 20.92, 20.92, 21.12, 21.24, 21.44, 21.44, 21.24, 21.16] +378.62 +18.931 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 35, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 11.63770079612732, 'TIME_S_1KI': 332.50573703220914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6712235164642, 'W': 42.76027469966683, 'J_1KI': 18876.32067189898, 'W_1KI': 1221.7221342761952, 'W_D': 23.82927469966683, 'J_D': 368.1762145335674, 'W_D_1KI': 680.8364199904809, 'J_D_1KI': 19452.46914258517} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..e7ef4f8 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 111, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.494628429412842, "TIME_S_1KI": 94.54620206678236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1296.4762573242188, "W": 86.4, "J_1KI": 11679.96628220017, "W_1KI": 778.3783783783784, "W_D": 43.46775000000001, "J_D": 652.2558545637132, "W_D_1KI": 391.6013513513514, "J_D_1KI": 3527.9401022644274} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..b195c7a --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.9449436664581299} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.4211, 0.9920, 0.9363, ..., 0.2555, 0.2504, 0.0922]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 0.9449436664581299 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '111', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.494628429412842} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.6100, 0.8301, 0.1851, ..., 0.5443, 0.2075, 0.2385]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.494628429412842 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.6100, 0.8301, 0.1851, ..., 0.5443, 0.2075, 0.2385]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.494628429412842 seconds + +[59.18, 53.78, 39.2, 39.37, 39.44, 62.28, 66.02, 67.2, 69.01, 72.0] +[86.4] +15.005512237548828 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 111, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.494628429412842, 'TIME_S_1KI': 94.54620206678236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.4762573242188, 'W': 86.4} +[59.18, 53.78, 39.2, 39.37, 39.44, 62.28, 66.02, 67.2, 69.01, 72.0, 45.54, 39.78, 39.33, 39.33, 39.24, 39.3, 39.08, 39.12, 39.27, 39.07] +858.645 +42.932249999999996 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 111, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.494628429412842, 'TIME_S_1KI': 94.54620206678236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.4762573242188, 'W': 86.4, 'J_1KI': 11679.96628220017, 'W_1KI': 778.3783783783784, 'W_D': 43.46775000000001, 'J_D': 652.2558545637132, 'W_D_1KI': 391.6013513513514, 'J_D_1KI': 3527.9401022644274} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..cf82d40 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.24282717704773, "TIME_S_1KI": 79.4017610623855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 953.2117093086243, "W": 74.2, "J_1KI": 7389.238056656002, "W_1KI": 575.1937984496124, "W_D": 38.954499999999996, "J_D": 500.429724127531, "W_D_1KI": 301.9728682170542, "J_D_1KI": 2340.874947419025} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..2115a36 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.8108494281768799} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.1590, 0.0885, 0.5920, ..., 0.6409, 0.5117, 0.0463]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.8108494281768799 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '129', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.24282717704773} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.3847, 0.8623, 0.3319, ..., 0.6263, 0.3030, 0.6933]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.24282717704773 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.3847, 0.8623, 0.3319, ..., 0.6263, 0.3030, 0.6933]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.24282717704773 seconds + +[39.65, 38.66, 38.69, 39.34, 38.7, 38.81, 38.61, 38.58, 38.74, 38.69] +[74.2] +12.846518993377686 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 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b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_language.json new file mode 100644 index 0000000..7716054 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 301, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.483814716339111, "TIME_S_1KI": 34.82994922371798, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1053.32080940485, "W": 79.57000000000001, "J_1KI": 3499.4046824081397, "W_1KI": 264.3521594684385, "W_D": 44.29125000000001, "J_D": 586.3126215854289, "W_D_1KI": 147.14700996677746, "J_D_1KI": 488.8604982284965} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_language.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_language.output new file mode 100644 index 0000000..f4b1aac --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_language.output @@ -0,0 +1,77 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.37233543395996094} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.6244, 0.1203, 0.4829, ..., 0.5136, 0.1845, 0.7543]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.37233543395996094 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '282', '-m', 'matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.823282718658447} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.3030, 0.0233, 0.2554, ..., 0.2617, 0.0751, 0.9928]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 9.823282718658447 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '301', '-m', 'matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.483814716339111} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.2704, 0.4487, 0.5182, ..., 0.6292, 0.0904, 0.2863]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.483814716339111 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.2704, 0.4487, 0.5182, ..., 0.6292, 0.0904, 0.2863]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.483814716339111 seconds + +[39.38, 38.64, 39.25, 40.55, 39.1, 39.32, 39.27, 39.09, 38.72, 39.05] +[79.57] +13.237662553787231 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 301, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.483814716339111, 'TIME_S_1KI': 34.82994922371798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1053.32080940485, 'W': 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0000000..dd368b1 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 58, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.336882591247559, "TIME_S_1KI": 178.22211364219928, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 959.5556811213493, "W": 73.13, "J_1KI": 16544.06346760947, "W_1KI": 1260.8620689655172, "W_D": 37.59925, "J_D": 493.3484745439887, "W_D_1KI": 648.2629310344828, "J_D_1KI": 11176.947086801427} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_marine1.output new file mode 100644 index 0000000..416a686 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7965192794799805} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.9882, 0.2187, 0.6297, ..., 0.3036, 0.3269, 0.4679]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 1.7965192794799805 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '58', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.336882591247559} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.8767, 0.3986, 0.3133, ..., 0.9400, 0.2569, 0.3724]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.336882591247559 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.8767, 0.3986, 0.3133, ..., 0.9400, 0.2569, 0.3724]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.336882591247559 seconds + +[40.02, 39.82, 39.18, 39.52, 39.54, 38.95, 39.6, 40.54, 39.52, 39.36] +[73.13] +13.1212317943573 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 58, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.336882591247559, 'TIME_S_1KI': 178.22211364219928, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 959.5556811213493, 'W': 73.13} +[40.02, 39.82, 39.18, 39.52, 39.54, 38.95, 39.6, 40.54, 39.52, 39.36, 40.24, 39.32, 39.29, 39.93, 38.96, 38.9, 38.91, 40.18, 38.85, 39.59] +710.615 +35.53075 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 58, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.336882591247559, 'TIME_S_1KI': 178.22211364219928, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 959.5556811213493, 'W': 73.13, 'J_1KI': 16544.06346760947, 'W_1KI': 1260.8620689655172, 'W_D': 37.59925, 'J_D': 493.3484745439887, 'W_D_1KI': 648.2629310344828, 'J_D_1KI': 11176.947086801427} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.json new file mode 100644 index 0000000..561aeb8 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 165, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.139087677001953, "TIME_S_1KI": 61.449016224254265, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 986.1521092557907, "W": 76.89, "J_1KI": 5976.679450035095, "W_1KI": 466.0, "W_D": 41.48075, "J_D": 532.0110431267024, "W_D_1KI": 251.39848484848488, "J_D_1KI": 1523.6271808999084} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.output new file mode 100644 index 0000000..32e9745 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.6358463764190674} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.8083, 0.3796, 0.7179, ..., 0.1421, 0.5094, 0.7719]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.6358463764190674 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '165', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.139087677001953} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.5896, 0.3107, 0.2730, ..., 0.7097, 0.9659, 0.7087]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.139087677001953 seconds + 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0000000..1342141 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.317313194274902, "TIME_S_1KI": 573.1840663486056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 961.8041999101639, "W": 68.9, "J_1KI": 53433.566661675766, "W_1KI": 3827.777777777778, "W_D": 33.76675000000001, "J_D": 471.3643246344925, "W_D_1KI": 1875.930555555556, "J_D_1KI": 104218.36419753089} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..add32d5 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_msdoor.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 5.754570484161377} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.6140, 0.8351, 0.9007, ..., 0.7097, 0.3651, 0.6328]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 5.754570484161377 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '18', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 10.317313194274902} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.9196, 0.4900, 0.9802, ..., 0.6636, 0.6725, 0.7139]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.317313194274902 seconds + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.9196, 0.4900, 0.9802, ..., 0.6636, 0.6725, 0.7139]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 10.317313194274902 seconds + +[39.52, 39.1, 40.98, 38.49, 38.52, 38.6, 38.66, 38.4, 39.05, 38.97] +[68.9] +13.95942234992981 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'msdoor', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [415863, 415863], 'MATRIX_ROWS': 415863, 'MATRIX_SIZE': 172942034769, 'MATRIX_NNZ': 20240935, 'MATRIX_DENSITY': 0.00011703883921012015, 'TIME_S': 10.317313194274902, 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b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_test1.json new file mode 100644 index 0000000..ac46f16 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_test1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.360844373703003, "TIME_S_1KI": 370.0301562036787, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 967.3078742837906, "W": 70.31, "J_1KI": 34546.70979584967, "W_1KI": 2511.071428571429, "W_D": 34.71, "J_D": 477.53173540592195, "W_D_1KI": 1239.6428571428573, "J_D_1KI": 44272.959183673476} diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_test1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_test1.output new file mode 100644 index 0000000..0fa4d62 --- /dev/null +++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_coo_10_10_10_test1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/test1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.6783218383789062} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.3337, 0.3949, 0.6606, ..., 0.9875, 0.5491, 0.7828]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 3.6783218383789062 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '28', '-m', 'matrices/389000+_cols/test1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.360844373703003} + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.1036, 0.9957, 0.7940, ..., 0.0164, 0.5943, 0.5007]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.360844373703003 seconds + +tensor(indices=tensor([[ 0, 1, 8, ..., 392905, 392906, 392907], + [ 0, 0, 0, ..., 392907, 392907, 392907]]), + values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00, ..., + 0.0000e+00, 0.0000e+00, 2.1156e-17]), + size=(392908, 392908), nnz=12968200, layout=torch.sparse_coo) +tensor([0.1036, 0.9957, 0.7940, ..., 0.0164, 0.5943, 0.5007]) +Matrix Type: SuiteSparse +Matrix: test1 +Matrix Format: coo +Shape: torch.Size([392908, 392908]) +Rows: 392908 +Size: 154376696464 +NNZ: 12968200 +Density: 8.400361127706946e-05 +Time: 10.360844373703003 seconds + +[40.85, 39.32, 39.46, 39.34, 39.47, 39.32, 39.15, 41.04, 39.28, 39.48] +[70.31] +13.75775671005249 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.360844373703003, 'TIME_S_1KI': 370.0301562036787, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 967.3078742837906, 'W': 70.31} +[40.85, 39.32, 39.46, 39.34, 39.47, 39.32, 39.15, 41.04, 39.28, 39.48, 39.47, 39.52, 39.32, 39.86, 39.84, 39.05, 38.91, 40.4, 39.26, 39.12] +712.0 +35.6 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.360844373703003, 'TIME_S_1KI': 370.0301562036787, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 967.3078742837906, 'W': 70.31, 'J_1KI': 34546.70979584967, 'W_1KI': 2511.071428571429, 'W_D': 34.71, 'J_D': 477.53173540592195, 'W_D_1KI': 1239.6428571428573, 'J_D_1KI': 44272.959183673476} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.json new file mode 100644 index 0000000..1dda271 --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 77, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.299436807632446, "TIME_S_1KI": 133.75891957964217, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 790.7893392920494, "W": 55.05, "J_1KI": 10269.991419377264, "W_1KI": 714.9350649350648, "W_D": 38.141749999999995, "J_D": 547.9035291905999, "W_D_1KI": 495.3474025974025, "J_D_1KI": 6433.083150615616} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.output new file mode 100644 index 0000000..995d4ae --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_amazon0312.output @@ -0,0 +1,59 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.357517957687378} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.0924, 0.1088, 0.8067, ..., 0.5293, 0.1431, 0.3673]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 1.357517957687378 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '77', '-m', 'matrices/389000+_cols/amazon0312.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.299436807632446} + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.5217, 0.0949, 0.4415, ..., 0.9526, 0.7874, 0.5082]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.299436807632446 seconds + +tensor(indices=tensor([[ 1, 2, 0, ..., 400725, 400724, 400707], + [ 0, 0, 1, ..., 400724, 400725, 400726]]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), + size=(400727, 400727), nnz=3200440, layout=torch.sparse_coo) +tensor([0.5217, 0.0949, 0.4415, ..., 0.9526, 0.7874, 0.5082]) +Matrix Type: SuiteSparse +Matrix: amazon0312 +Matrix Format: coo +Shape: torch.Size([400727, 400727]) +Rows: 400727 +Size: 160582128529 +NNZ: 3200440 +Density: 1.9930237750099465e-05 +Time: 10.299436807632446 seconds + +[19.59, 18.6, 18.55, 18.41, 18.98, 18.84, 18.47, 18.49, 19.59, 19.76] +[55.05] +14.364928960800171 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.299436807632446, 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b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_helm2d03.json new file mode 100644 index 0000000..e8f2f54 --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_helm2d03.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.319592475891113, "TIME_S_1KI": 98.28183310372489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 782.9226589202881, "W": 56.0, "J_1KI": 7456.406275431315, "W_1KI": 533.3333333333334, "W_D": 39.2755, "J_D": 549.1014087575675, "W_D_1KI": 374.052380952381, "J_D_1KI": 3562.403628117914} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_helm2d03.output new file mode 100644 index 0000000..803bba7 --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_helm2d03.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.9944000244140625} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.7967, 0.4605, 0.7387, ..., 0.6922, 0.1012, 0.1297]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 0.9944000244140625 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '105', '-m', 'matrices/389000+_cols/helm2d03.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.319592475891113} + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4484, 0.6920, 0.7670, ..., 0.5208, 0.6613, 0.4719]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.319592475891113 seconds + +tensor(indices=tensor([[ 0, 98273, 133833, ..., 392252, 392253, 392254], + [ 0, 0, 0, ..., 392256, 392255, 392256]]), + values=tensor([ 3.4808, -0.6217, -0.5806, ..., -0.6940, -0.7602, + -0.7602]), + size=(392257, 392257), nnz=2741935, layout=torch.sparse_coo) +tensor([0.4484, 0.6920, 0.7670, ..., 0.5208, 0.6613, 0.4719]) +Matrix Type: SuiteSparse +Matrix: helm2d03 +Matrix Format: coo +Shape: torch.Size([392257, 392257]) +Rows: 392257 +Size: 153865554049 +NNZ: 2741935 +Density: 1.7820330332848923e-05 +Time: 10.319592475891113 seconds + +[18.51, 18.38, 18.73, 18.23, 18.35, 18.5, 18.41, 18.48, 18.26, 18.71] +[56.0] +13.980761766433716 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 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a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.json b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.json new file mode 100644 index 0000000..d55fa4c --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 216, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.079247951507568, "TIME_S_1KI": 46.66318496068319, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 786.8017999219894, "W": 57.419999999999995, "J_1KI": 3642.600925564766, "W_1KI": 265.8333333333333, "W_D": 40.67699999999999, "J_D": 557.3796031944751, "W_D_1KI": 188.3194444444444, "J_D_1KI": 871.8492798353907} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.output new file mode 100644 index 0000000..fb953ab --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_language.output @@ -0,0 +1,59 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.48494982719421387} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.5210, 0.3385, 0.6933, ..., 0.1836, 0.0531, 0.2581]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 0.48494982719421387 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '216', '-m', 'matrices/389000+_cols/language.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.079247951507568} + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.1175, 0.1499, 0.8548, ..., 0.1280, 0.5960, 0.2026]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.079247951507568 seconds + +tensor(indices=tensor([[ 0, 1, 1, ..., 399128, 398241, 399129], + [ 0, 0, 1, ..., 399128, 399129, 399129]]), + values=tensor([ 1., -1., 1., ..., 1., -1., 1.]), + size=(399130, 399130), nnz=1216334, layout=torch.sparse_coo) +tensor([0.1175, 0.1499, 0.8548, ..., 0.1280, 0.5960, 0.2026]) +Matrix Type: SuiteSparse +Matrix: language +Matrix Format: coo +Shape: torch.Size([399130, 399130]) +Rows: 399130 +Size: 159304756900 +NNZ: 1216334 +Density: 7.635264782228233e-06 +Time: 10.079247951507568 seconds + +[19.02, 18.78, 19.52, 18.47, 18.42, 18.68, 18.96, 18.67, 18.53, 18.49] +[57.42] +13.702574014663696 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 216, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.079247951507568, 'TIME_S_1KI': 46.66318496068319, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.8017999219894, 'W': 57.419999999999995} +[19.02, 18.78, 19.52, 18.47, 18.42, 18.68, 18.96, 18.67, 18.53, 18.49, 18.71, 18.56, 18.58, 18.23, 18.51, 18.2, 18.55, 18.29, 18.7, 18.2] +334.85999999999996 +16.743 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 216, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.079247951507568, 'TIME_S_1KI': 46.66318496068319, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.8017999219894, 'W': 57.419999999999995, 'J_1KI': 3642.600925564766, 'W_1KI': 265.8333333333333, 'W_D': 40.67699999999999, 'J_D': 557.3796031944751, 'W_D_1KI': 188.3194444444444, 'J_D_1KI': 871.8492798353907} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.json new file mode 100644 index 0000000..f6fcd6f --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 47, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.365242719650269, "TIME_S_1KI": 220.53707914149507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 793.9842559289933, "W": 55.59, "J_1KI": 16893.28204104241, "W_1KI": 1182.7659574468087, "W_D": 38.42725, "J_D": 548.8510793064833, "W_D_1KI": 817.6010638297872, "J_D_1KI": 17395.767315527388} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.output new file mode 100644 index 0000000..c04e2fd --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_marine1.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 2.2128942012786865} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.5370, 0.8272, 0.6318, ..., 0.7214, 0.0929, 0.2656]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 2.2128942012786865 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '47', '-m', 'matrices/389000+_cols/marine1.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.365242719650269} + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.2986, 0.7259, 0.4436, ..., 0.6161, 0.3189, 0.6566]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.365242719650269 seconds + +tensor(indices=tensor([[ 0, 1, 10383, ..., 400315, 400318, 400319], + [ 0, 0, 0, ..., 400319, 400319, 400319]]), + values=tensor([ 6.2373e+03, 3.4166e-01, -6.5085e+00, ..., + -9.6129e-01, -6.8118e-01, 1.7595e+01]), + size=(400320, 400320), nnz=6226538, layout=torch.sparse_coo) +tensor([0.2986, 0.7259, 0.4436, ..., 0.6161, 0.3189, 0.6566]) +Matrix Type: SuiteSparse +Matrix: marine1 +Matrix Format: coo +Shape: torch.Size([400320, 400320]) +Rows: 400320 +Size: 160256102400 +NNZ: 6226538 +Density: 3.885367175883594e-05 +Time: 10.365242719650269 seconds + +[20.58, 18.71, 18.7, 18.44, 18.87, 18.66, 18.79, 18.93, 18.87, 18.59] +[55.59] +14.282861232757568 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 47, 'MATRIX_TYPE': 'SuiteSparse', 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38.42725, 'J_D': 548.8510793064833, 'W_D_1KI': 817.6010638297872, 'J_D_1KI': 17395.767315527388} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.json new file mode 100644 index 0000000..5cc48d7 --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 130, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.592259645462036, "TIME_S_1KI": 81.47892034970796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 781.6224474143983, "W": 56.24, "J_1KI": 6012.480364726141, "W_1KI": 432.61538461538464, "W_D": 34.62425, "J_D": 481.20716616088157, "W_D_1KI": 266.3403846153846, "J_D_1KI": 2048.7721893491125} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.output new file mode 100644 index 0000000..d43e25c --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_mario002.output @@ -0,0 +1,59 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.80599045753479} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.9383, 0.1771, 0.7725, ..., 0.5676, 0.9035, 0.6728]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 0.80599045753479 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '130', '-m', 'matrices/389000+_cols/mario002.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.592259645462036} + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.8337, 0.9717, 0.2324, ..., 0.5335, 0.0603, 0.8997]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.592259645462036 seconds + +tensor(indices=tensor([[ 0, 1027, 1028, ..., 234126, 234127, 234127], + [ 0, 0, 0, ..., 389703, 389823, 389873]]), + values=tensor([ 1., 0., 0., ..., -1., 1., -1.]), + size=(389874, 389874), nnz=2101242, layout=torch.sparse_coo) +tensor([0.8337, 0.9717, 0.2324, ..., 0.5335, 0.0603, 0.8997]) +Matrix Type: SuiteSparse +Matrix: mario002 +Matrix Format: coo +Shape: torch.Size([389874, 389874]) +Rows: 389874 +Size: 152001735876 +NNZ: 2101242 +Density: 1.3823802655215408e-05 +Time: 10.592259645462036 seconds + +[37.15, 43.2, 44.12, 38.68, 28.73, 18.77, 18.97, 18.88, 18.53, 18.37] +[56.24] +13.89798092842102 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 130, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'coo', 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'W_D_1KI': 266.3403846153846, 'J_D_1KI': 2048.7721893491125} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.json b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.json new file mode 100644 index 0000000..2096db5 --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 14, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 11.319983959197998, "TIME_S_1KI": 808.570282799857, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 835.3857048654556, "W": 55.62, "J_1KI": 59670.40749038968, "W_1KI": 3972.8571428571427, "W_D": 38.43825, "J_D": 577.3240663438439, "W_D_1KI": 2745.5892857142853, "J_D_1KI": 196113.52040816323} diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.output b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.output new file mode 100644 index 0000000..9baa4ae --- /dev/null +++ b/pytorch/output_389000+_maxcore/xeon_4216_max_coo_10_10_10_msdoor.output @@ -0,0 +1,214 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '10', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 7.138502359390259} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.4831, 0.5140, 0.6484, ..., 0.3604, 0.5115, 0.5635]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 7.138502359390259 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '14', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 9.992950677871704} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.6875, 0.7727, 0.1343, ..., 0.0856, 0.8269, 0.2703]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 9.992950677871704 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '14', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 9.974185228347778} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, -360329.0312]), + size=(415863, 415863), nnz=20240935, layout=torch.sparse_coo) +tensor([0.1867, 0.1778, 0.1962, ..., 0.9433, 0.0672, 0.8764]) +Matrix Type: SuiteSparse +Matrix: msdoor +Matrix Format: coo +Shape: torch.Size([415863, 415863]) +Rows: 415863 +Size: 172942034769 +NNZ: 20240935 +Density: 0.00011703883921012015 +Time: 9.974185228347778 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '14', '-m', 'matrices/389000+_cols/msdoor.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "msdoor", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [415863, 415863], "MATRIX_ROWS": 415863, "MATRIX_SIZE": 172942034769, "MATRIX_NNZ": 20240935, "MATRIX_DENSITY": 0.00011703883921012015, "TIME_S": 9.989935398101807} + +tensor(indices=tensor([[ 0, 1, 2, ..., 415860, 415860, 415861], + [ 0, 0, 0, ..., 415861, 415862, 415862]]), + values=tensor([1732682.0000, 32540.3633, 24619.0176, ..., + 22251.7324, -97620.4609, 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