diff --git a/pytorch/batch.py b/pytorch/batch.py index 60d9a63..f391f3c 100644 --- a/pytorch/batch.py +++ b/pytorch/batch.py @@ -117,7 +117,7 @@ elif args.matrix_type == MatrixType.SYNTHETIC: parameter_list = enumerate([(size, density) for size in args.synthetic_size for density in args.synthetic_density - if size ** 2 * density < 100000000]) + if size ** 2 * density < 10000000]) #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')): for i, parameter in parameter_list: diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..8bab18f --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1770, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.45119595527649, "TIME_S_1KI": 5.904630483207056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 518.2880183124543, "W": 35.449367856062224, "J_1KI": 292.81808944206455, "W_1KI": 20.027891444102952, "W_D": 16.922367856062227, "J_D": 247.4137349045278, "W_D_1KI": 9.560659805684875, "J_D_1KI": 5.401502715076201} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..9bd0023 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.932083368301392} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 10, ..., 999982, + 999993, 1000000]), + col_indices=tensor([37897, 46445, 60989, ..., 76977, 92294, 96477]), + values=tensor([0.9469, 0.5853, 0.3833, ..., 0.6631, 0.6410, 0.8148]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0925, 0.0591, 0.1895, ..., 0.1208, 0.2736, 0.9441]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 5.932083368301392 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 synthetic csr 1770 -ss 100000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.45119595527649} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 15, ..., 999984, + 999991, 1000000]), + col_indices=tensor([ 6148, 23043, 28153, ..., 62723, 86562, 96964]), + values=tensor([0.4836, 0.5090, 0.9509, ..., 0.7452, 0.4499, 0.9407]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2481, 0.7173, 0.6398, ..., 0.9063, 0.5779, 0.5048]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.45119595527649 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 15, ..., 999984, + 999991, 1000000]), + col_indices=tensor([ 6148, 23043, 28153, ..., 62723, 86562, 96964]), + values=tensor([0.4836, 0.5090, 0.9509, ..., 0.7452, 0.4499, 0.9407]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2481, 0.7173, 0.6398, ..., 0.9063, 0.5779, 0.5048]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.45119595527649 seconds + +[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88] +[20.88, 21.44, 21.2, 22.44, 23.6, 27.28, 34.76, 40.76, 46.84, 51.32, 53.0, 52.92, 53.08, 52.84] +14.62051510810852 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224} +[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88, 20.56, 20.52, 20.64, 20.64, 20.52, 20.52, 20.6, 20.68, 20.6, 20.72] +370.53999999999996 +18.526999999999997 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224, 'J_1KI': 292.81808944206455, 'W_1KI': 20.027891444102952, 'W_D': 16.922367856062227, 'J_D': 247.4137349045278, 'W_D_1KI': 9.560659805684875, 'J_D_1KI': 5.401502715076201} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..f3aace9 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.278456687927246, "TIME_S_1KI": 0.8709818394989616, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 437.5742044067384, "W": 32.19958635623455, "J_1KI": 37.0794173719802, "W_1KI": 2.728547271945983, "W_D": 13.391586356234548, "J_D": 181.9841000671388, "W_D_1KI": 1.1347840315426274, "J_D_1KI": 0.09615998911470446} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..d71dde9 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.063995361328125} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 99997, 99999, + 100000]), + col_indices=tensor([67343, 31299, 81155, ..., 33224, 88457, 24576]), + values=tensor([0.5842, 0.8218, 0.6188, ..., 0.3932, 0.6826, 0.0146]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.9733, 0.2979, 0.3395, ..., 0.2786, 0.7488, 0.6423]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 1.063995361328125 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 synthetic csr 9868 -ss 100000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.779469966888428} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 4, ..., 99997, 99999, + 100000]), + col_indices=tensor([14435, 22527, 43950, ..., 8583, 8872, 18967]), + values=tensor([0.6873, 0.0224, 0.4938, ..., 0.6581, 0.7037, 0.6316]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2290, 0.1645, 0.1242, ..., 0.3445, 0.2954, 0.7059]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 8.779469966888428 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 synthetic csr 11801 -ss 100000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.278456687927246} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 100000, 100000, + 100000]), + col_indices=tensor([88946, 66534, 50450, ..., 63020, 21924, 98776]), + values=tensor([0.0165, 0.3102, 0.5959, ..., 0.2885, 0.2555, 0.6064]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1483, 0.9193, 0.9702, ..., 0.6151, 0.3023, 0.2526]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.278456687927246 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 100000, 100000, + 100000]), + col_indices=tensor([88946, 66534, 50450, ..., 63020, 21924, 98776]), + values=tensor([0.0165, 0.3102, 0.5959, ..., 0.2885, 0.2555, 0.6064]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1483, 0.9193, 0.9702, ..., 0.6151, 0.3023, 0.2526]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.278456687927246 seconds + +[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2] +[21.36, 21.64, 21.64, 23.32, 23.96, 29.24, 34.28, 39.64, 43.16, 45.96, 45.88, 46.84, 47.12] +13.589435577392578 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455} +[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2, 21.04, 20.92, 20.64, 20.52, 20.52, 20.4, 20.72, 20.96, 21.24, 21.32] +376.16 +18.808 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455, 'J_1KI': 37.0794173719802, 'W_1KI': 2.728547271945983, 'W_D': 13.391586356234548, 'J_D': 181.9841000671388, 'W_D_1KI': 1.1347840315426274, 'J_D_1KI': 0.09615998911470446} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..ddd2b32 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 33464, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.751937627792358, "TIME_S_1KI": 0.321298638172136, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.9264199829101, "W": 23.099563679174377, "J_1KI": 9.799379033675296, "W_1KI": 0.6902810088206544, "W_D": 4.345563679174376, "J_D": 61.690565237998875, "W_D_1KI": 0.12985786753449605, "J_D_1KI": 0.0038805243705025113} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..c550e8c --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.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 synthetic csr 1000 -ss 10000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.358994722366333} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 9999, 9999, 10000]), + col_indices=tensor([4769, 2640, 4731, ..., 7727, 9096, 344]), + values=tensor([0.5549, 0.8764, 0.0270, ..., 0.0575, 0.5131, 0.9423]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.2724, 0.3491, 0.1026, ..., 0.4580, 0.8295, 0.5142]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.358994722366333 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 synthetic csr 29248 -ss 10000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.177036046981812} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 9997, 9998, 10000]), + col_indices=tensor([8143, 7461, 5162, ..., 7740, 5053, 9684]), + values=tensor([0.7267, 0.3238, 0.0105, ..., 0.5150, 0.5465, 0.0983]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.8883, 0.6326, 0.2674, ..., 0.1564, 0.2088, 0.8392]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 9.177036046981812 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 synthetic csr 33464 -ss 10000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.751937627792358} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 5, ..., 9994, 9997, 10000]), + col_indices=tensor([1608, 4931, 8613, ..., 2107, 3637, 7054]), + values=tensor([0.4097, 0.1049, 0.8257, ..., 0.2263, 0.1754, 0.1229]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.9092, 0.1064, 0.7261, ..., 0.1695, 0.8231, 0.3389]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.751937627792358 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 5, ..., 9994, 9997, 10000]), + col_indices=tensor([1608, 4931, 8613, ..., 2107, 3637, 7054]), + values=tensor([0.4097, 0.1049, 0.8257, ..., 0.2263, 0.1754, 0.1229]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.9092, 0.1064, 0.7261, ..., 0.1695, 0.8231, 0.3389]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.751937627792358 seconds + +[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28] +[21.52, 20.68, 23.48, 24.56, 27.0, 27.0, 27.6, 28.4, 25.44, 25.08, 23.88, 23.84, 23.72, 23.68] +14.196217060089111 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377} +[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28, 20.28, 20.68, 20.64, 20.84, 20.84, 20.88, 20.6, 20.6, 20.48, 20.24] +375.08000000000004 +18.754 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377, 'J_1KI': 9.799379033675296, 'W_1KI': 0.6902810088206544, 'W_D': 4.345563679174376, 'J_D': 61.690565237998875, 'W_D_1KI': 0.12985786753449605, 'J_D_1KI': 0.0038805243705025113} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..91eebfb --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.608984231948853, "TIME_S_1KI": 2.260597535041307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.61959093093867, "W": 23.443356307834602, "J_1KI": 71.08876857680346, "W_1KI": 4.995388090312082, "W_D": 4.929356307834599, "J_D": 70.14907820272437, "W_D_1KI": 1.0503635857307905, "J_D_1KI": 0.223814955408223} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..bec4359 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2371175289154053} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 13, ..., 99981, 99991, + 100000]), + col_indices=tensor([ 11, 880, 2486, ..., 7621, 8410, 9572]), + values=tensor([0.7919, 0.7111, 0.9252, ..., 0.0051, 0.9566, 0.6694]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8227, 0.5043, 0.0669, ..., 0.5765, 0.9663, 0.4234]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 2.2371175289154053 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 synthetic csr 4693 -ss 10000 -sd 0.001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.608984231948853} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 14, 27, ..., 99982, 99994, + 100000]), + col_indices=tensor([ 135, 2132, 2413, ..., 7244, 7277, 8789]), + values=tensor([0.8089, 0.0016, 0.7063, ..., 0.2204, 0.7876, 0.4440]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2483, 0.7850, 0.0043, ..., 0.4009, 0.1492, 0.4510]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.608984231948853 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 14, 27, ..., 99982, 99994, + 100000]), + col_indices=tensor([ 135, 2132, 2413, ..., 7244, 7277, 8789]), + values=tensor([0.8089, 0.0016, 0.7063, ..., 0.2204, 0.7876, 0.4440]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2483, 0.7850, 0.0043, ..., 0.4009, 0.1492, 0.4510]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.608984231948853 seconds + +[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84] +[20.84, 20.52, 23.32, 24.96, 27.48, 27.48, 28.36, 28.96, 25.92, 25.2, 24.36, 24.56, 24.48, 24.08] +14.23088002204895 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602} +[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84, 20.68, 20.8, 20.52, 20.64, 20.64, 20.68, 20.4, 20.48, 20.36, 20.24] +370.28000000000003 +18.514000000000003 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602, 'J_1KI': 71.08876857680346, 'W_1KI': 4.995388090312082, 'W_D': 4.929356307834599, 'J_D': 70.14907820272437, 'W_D_1KI': 1.0503635857307905, 'J_D_1KI': 0.223814955408223} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..dd61d28 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.223905086517334, "TIME_S_1KI": 21.223905086517334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 606.5871645927429, "W": 23.902485146880146, "J_1KI": 606.5871645927429, "W_1KI": 23.902485146880146, "W_D": 5.469485146880146, "J_D": 138.80228213262555, "W_D_1KI": 5.469485146880146, "J_D_1KI": 5.469485146880146} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..dbf2821 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.223905086517334} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 100, 193, ..., 999807, + 999898, 1000000]), + col_indices=tensor([ 45, 67, 78, ..., 9873, 9905, 9941]), + values=tensor([0.2793, 0.5501, 0.9236, ..., 0.0106, 0.8963, 0.7259]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2312, 0.2281, 0.2895, ..., 0.4123, 0.5947, 0.5960]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.223905086517334 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 100, 193, ..., 999807, + 999898, 1000000]), + col_indices=tensor([ 45, 67, 78, ..., 9873, 9905, 9941]), + values=tensor([0.2793, 0.5501, 0.9236, ..., 0.0106, 0.8963, 0.7259]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2312, 0.2281, 0.2895, ..., 0.4123, 0.5947, 0.5960]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.223905086517334 seconds + +[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44] +[20.44, 20.64, 23.68, 24.76, 27.96, 27.96, 29.28, 30.08, 27.32, 27.04, 23.96, 23.92, 23.72, 23.6, 23.72, 23.92, 24.08, 24.24, 24.24, 24.36, 24.24, 24.12, 24.4, 23.96, 24.12] +25.377577304840088 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146} +[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44, 20.2, 20.32, 20.32, 20.52, 20.52, 20.8, 20.8, 20.72, 20.68, 20.76] +368.65999999999997 +18.433 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146, 'J_1KI': 606.5871645927429, 'W_1KI': 23.902485146880146, 'W_D': 5.469485146880146, 'J_D': 138.80228213262555, 'W_D_1KI': 5.469485146880146, 'J_D_1KI': 5.469485146880146} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..af8ffc0 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 112.2527105808258, "TIME_S_1KI": 112.2527105808258, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2847.1031341934195, "W": 24.02975891792854, "J_1KI": 2847.1031341934195, "W_1KI": 24.02975891792854, "W_D": 5.456758917928539, "J_D": 646.5298079283226, "W_D_1KI": 5.456758917928539, "J_D_1KI": 5.456758917928539} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..d49413b --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 112.2527105808258} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 493, 999, ..., 4999078, + 4999538, 5000000]), + col_indices=tensor([ 9, 32, 79, ..., 9948, 9954, 9975]), + values=tensor([0.7230, 0.3394, 0.4856, ..., 0.5860, 0.3031, 0.1676]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5227, 0.7065, 0.1059, ..., 0.0574, 0.9985, 0.1783]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 112.2527105808258 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 493, 999, ..., 4999078, + 4999538, 5000000]), + col_indices=tensor([ 9, 32, 79, ..., 9948, 9954, 9975]), + values=tensor([0.7230, 0.3394, 0.4856, ..., 0.5860, 0.3031, 0.1676]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5227, 0.7065, 0.1059, ..., 0.0574, 0.9985, 0.1783]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 112.2527105808258 seconds + +[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52] +[20.84, 20.72, 21.32, 21.96, 24.12, 27.04, 27.04, 28.68, 28.56, 28.24, 25.72, 24.44, 24.36, 24.24, 24.24, 24.56, 24.28, 24.4, 24.4, 24.44, 24.56, 24.2, 24.24, 24.04, 24.28, 24.12, 24.12, 24.28, 24.32, 24.24, 24.56, 24.56, 24.6, 24.44, 24.6, 24.6, 24.44, 24.44, 24.44, 24.4, 24.36, 24.36, 24.28, 24.28, 24.32, 24.24, 24.28, 24.08, 24.04, 24.04, 24.2, 24.24, 24.32, 24.6, 24.68, 24.36, 24.36, 24.28, 24.24, 24.08, 24.24, 24.32, 24.36, 24.6, 24.6, 24.64, 24.68, 24.6, 24.6, 24.4, 24.28, 24.4, 24.4, 24.2, 24.32, 24.36, 24.4, 24.44, 24.56, 24.44, 24.44, 24.4, 24.28, 24.4, 24.56, 24.56, 24.64, 24.76, 24.68, 24.44, 24.44, 24.36, 24.32, 24.32, 24.16, 24.24, 24.2, 24.12, 23.8, 23.88, 23.88, 23.76, 24.08, 24.24, 24.4, 24.4, 24.6, 24.52, 24.4, 24.56, 24.48, 24.4, 24.68, 24.72, 24.68, 24.8, 24.8] +118.48238444328308 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854} +[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52, 20.52, 20.56, 20.56, 20.56, 20.8, 20.88, 20.8, 20.8, 20.68, 20.56] +371.46000000000004 +18.573 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854, 'J_1KI': 2847.1031341934195, 'W_1KI': 24.02975891792854, 'W_D': 5.456758917928539, 'J_D': 646.5298079283226, 'W_D_1KI': 5.456758917928539, 'J_D_1KI': 5.456758917928539} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..b09b822 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 141369, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.808244943618774, "TIME_S_1KI": 0.0764541373541496, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.87872554779057, "W": 22.096174468711904, "J_1KI": 2.2273534194044706, "W_1KI": 0.15630141310125914, "W_D": 3.7551744687119033, "J_D": 53.51263643360139, "W_D_1KI": 0.02656292729461129, "J_D_1KI": 0.00018789782268114857} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..ea3d934 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09768295288085938} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([9792, 3011, 8315, 6730, 9843, 4902, 9114, 233, 327, + 1077, 5110, 4341, 9618, 1148, 4296, 9363, 2120, 5266, + 1510, 7695, 5476, 9179, 3305, 25, 5170, 9334, 9520, + 18, 2410, 8122, 6722, 5458, 1189, 9940, 135, 139, + 2746, 4302, 817, 8119, 9183, 5557, 7078, 7532, 5204, + 9640, 2857, 2903, 8250, 2446, 4645, 4964, 6111, 2787, + 4305, 8541, 2087, 6834, 9039, 5610, 449, 6263, 9809, + 5478, 8383, 5854, 2328, 3230, 867, 3772, 2544, 739, + 5716, 4182, 7270, 9111, 2105, 2273, 7055, 6308, 4091, + 9837, 2327, 5713, 7469, 8593, 3004, 1329, 1982, 6739, + 2484, 7531, 861, 603, 3312, 9947, 2174, 8338, 554, + 5146, 610, 333, 2059, 2323, 8214, 7253, 520, 2440, + 6747, 6391, 6453, 8692, 6979, 9688, 8514, 2146, 5042, + 9573, 4252, 1574, 2537, 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0.2619, ..., 0.1829, 0.3048, 0.7863]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.09768295288085938 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 synthetic csr 107490 -ss 10000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.983633518218994} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 999, 1000, 1000]), + col_indices=tensor([9808, 7333, 2761, 6984, 2618, 1200, 1681, 6432, 1908, + 9155, 6399, 6173, 7686, 5356, 3993, 7981, 7425, 5593, + 4850, 5052, 8961, 8336, 7112, 619, 2135, 8626, 2607, + 2216, 9694, 4189, 2998, 8335, 3027, 1032, 6652, 9026, + 1450, 3086, 8168, 3872, 2284, 9839, 7872, 6967, 7777, + 5855, 4560, 4411, 6083, 1505, 2302, 7162, 8721, 8884, + 3749, 7643, 9696, 5850, 2249, 8244, 1919, 8048, 1342, + 7317, 2490, 6808, 7315, 7726, 4785, 4921, 9956, 4443, + 4480, 6691, 8417, 161, 5555, 404, 8581, 5792, 8301, + 5318, 292, 5134, 8928, 8066, 453, 7458, 9510, 289, + 5180, 4317, 3606, 3627, 1876, 624, 3722, 3159, 4377, + 5013, 9349, 2667, 1785, 8678, 5026, 4888, 9756, 9607, + 8469, 7716, 8606, 3083, 1563, 1434, 2738, 7289, 1978, + 700, 6478, 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0.9437, 0.0129, ..., 0.9247, 0.1049, 0.8510]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 7.983633518218994 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 synthetic csr 141369 -ss 10000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.808244943618774} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7315, 1858, 1670, 5364, 1184, 3689, 9574, 1136, 5558, + 3491, 589, 9091, 159, 766, 502, 9929, 4846, 9650, + 2563, 3405, 2322, 3115, 8463, 8330, 9642, 7938, 1757, + 7149, 4012, 8129, 197, 2039, 5706, 3549, 7371, 2993, + 1668, 5510, 7702, 9196, 8429, 6070, 2662, 4013, 9415, + 6857, 7829, 189, 1980, 6763, 6718, 1267, 4257, 3542, + 1839, 9352, 3880, 4065, 5790, 6525, 9847, 6167, 4814, + 6341, 2068, 662, 5058, 1944, 658, 6063, 9056, 9925, + 2964, 8244, 282, 3473, 7406, 8810, 4236, 886, 9762, + 8425, 8800, 4778, 5281, 3283, 4118, 9078, 3169, 8457, + 9924, 2720, 1304, 4941, 3743, 4847, 8299, 4889, 214, + 6275, 5734, 2313, 2745, 5305, 3623, 13, 2937, 2995, + 6172, 9968, 1311, 5504, 8279, 7545, 3069, 7648, 5567, + 8268, 1055, 3660, 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0.6141, ..., 0.3243, 0.1158, 0.5219]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.808244943618774 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7315, 1858, 1670, 5364, 1184, 3689, 9574, 1136, 5558, + 3491, 589, 9091, 159, 766, 502, 9929, 4846, 9650, + 2563, 3405, 2322, 3115, 8463, 8330, 9642, 7938, 1757, + 7149, 4012, 8129, 197, 2039, 5706, 3549, 7371, 2993, + 1668, 5510, 7702, 9196, 8429, 6070, 2662, 4013, 9415, + 6857, 7829, 189, 1980, 6763, 6718, 1267, 4257, 3542, + 1839, 9352, 3880, 4065, 5790, 6525, 9847, 6167, 4814, + 6341, 2068, 662, 5058, 1944, 658, 6063, 9056, 9925, + 2964, 8244, 282, 3473, 7406, 8810, 4236, 886, 9762, + 8425, 8800, 4778, 5281, 3283, 4118, 9078, 3169, 8457, + 9924, 2720, 1304, 4941, 3743, 4847, 8299, 4889, 214, + 6275, 5734, 2313, 2745, 5305, 3623, 13, 2937, 2995, + 6172, 9968, 1311, 5504, 8279, 7545, 3069, 7648, 5567, + 8268, 1055, 3660, 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0.6141, ..., 0.3243, 0.1158, 0.5219]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.808244943618774 seconds + +[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08] +[20.0, 19.96, 20.64, 22.76, 24.36, 26.04, 26.44, 25.96, 25.96, 24.84, 23.4, 23.52, 23.44, 23.4] +14.250372886657715 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904} +[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08, 20.64, 20.44, 20.4, 20.6, 20.64, 20.48, 20.36, 20.32, 20.32, 20.52] +366.82000000000005 +18.341 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904, 'J_1KI': 2.2273534194044706, 'W_1KI': 0.15630141310125914, 'W_D': 3.7551744687119033, 'J_D': 53.51263643360139, 'W_D_1KI': 0.02656292729461129, 'J_D_1KI': 0.00018789782268114857} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..3cf2612 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1458, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.73922610282898, "TIME_S_1KI": 7.365724350362812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 524.544666223526, "W": 35.90986855086994, "J_1KI": 359.77000426853635, "W_1KI": 24.629539472475955, "W_D": 17.579868550869936, "J_D": 256.7936518120765, "W_D_1KI": 12.0575230115706, "J_D_1KI": 8.269906043601233} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..ac4d6c8 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.201478004455566} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 6, ..., 2499994, + 2499998, 2500000]), + col_indices=tensor([111852, 327751, 365150, ..., 493517, 11445, + 207886]), + values=tensor([0.9407, 0.2669, 0.8671, ..., 0.7942, 0.4760, 0.2816]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4423, 0.4635, 0.1741, ..., 0.0346, 0.7600, 0.4318]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 7.201478004455566 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 synthetic csr 1458 -ss 500000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.73922610282898} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 4, ..., 2499994, + 2500000, 2500000]), + col_indices=tensor([198857, 399888, 193187, ..., 179513, 216653, + 450880]), + values=tensor([0.4554, 0.7901, 0.7135, ..., 0.0158, 0.9399, 0.2709]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8645, 0.3649, 0.9819, ..., 0.4118, 0.2155, 0.1417]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.73922610282898 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 4, ..., 2499994, + 2500000, 2500000]), + col_indices=tensor([198857, 399888, 193187, ..., 179513, 216653, + 450880]), + values=tensor([0.4554, 0.7901, 0.7135, ..., 0.0158, 0.9399, 0.2709]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8645, 0.3649, 0.9819, ..., 0.4118, 0.2155, 0.1417]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.73922610282898 seconds + +[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32] +[20.56, 20.48, 21.52, 22.84, 24.72, 30.76, 37.24, 43.6, 43.6, 49.32, 53.6, 53.68, 53.6, 53.56] +14.607256650924683 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994} +[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32, 20.48, 20.4, 20.32, 20.04, 20.2, 20.4, 20.36, 20.36, 20.48, 20.52] +366.6 +18.330000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994, 'J_1KI': 359.77000426853635, 'W_1KI': 24.629539472475955, 'W_D': 17.579868550869936, 'J_D': 256.7936518120765, 'W_D_1KI': 12.0575230115706, 'J_D_1KI': 8.269906043601233} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..36f1dbe --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3515, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.52223539352417, "TIME_S_1KI": 2.9935235827949276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 470.9832169723511, "W": 32.170385708153674, "J_1KI": 133.99238036197755, "W_1KI": 9.15231456846477, "W_D": 13.629385708153674, "J_D": 199.53792237424858, "W_D_1KI": 3.8774923778531076, "J_D_1KI": 1.1031272767718656} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..60bafaf --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.9865975379943848} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 13, ..., 249989, 249995, + 250000]), + col_indices=tensor([12071, 16957, 24871, ..., 32088, 41674, 47752]), + values=tensor([0.0278, 0.4403, 0.7542, ..., 0.8727, 0.3256, 0.0294]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.9906, 0.0790, 0.7013, ..., 0.2118, 0.2385, 0.3873]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 2.9865975379943848 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 synthetic csr 3515 -ss 50000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.52223539352417} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 10, ..., 249992, 249995, + 250000]), + col_indices=tensor([ 9701, 11138, 26862, ..., 20273, 37187, 48197]), + values=tensor([0.8537, 0.5403, 0.1220, ..., 0.0155, 0.7712, 0.8752]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5658, 0.7328, 0.9479, ..., 0.1014, 0.1582, 0.5663]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.52223539352417 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 10, ..., 249992, 249995, + 250000]), + col_indices=tensor([ 9701, 11138, 26862, ..., 20273, 37187, 48197]), + values=tensor([0.8537, 0.5403, 0.1220, ..., 0.0155, 0.7712, 0.8752]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5658, 0.7328, 0.9479, ..., 0.1014, 0.1582, 0.5663]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.52223539352417 seconds + +[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72] +[20.64, 20.72, 21.28, 22.68, 24.8, 29.24, 34.6, 38.2, 42.72, 43.84, 43.84, 44.32, 44.16, 44.08] +14.640272617340088 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674} +[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72, 20.32, 20.24, 20.44, 20.32, 20.48, 20.64, 20.52, 20.64, 20.96, 20.84] +370.82 +18.541 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674, 'J_1KI': 133.99238036197755, 'W_1KI': 9.15231456846477, 'W_D': 13.629385708153674, 'J_D': 199.53792237424858, 'W_D_1KI': 3.8774923778531076, 'J_D_1KI': 1.1031272767718656} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..091be62 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.268765687942505, "TIME_S_1KI": 27.268765687942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1234.21608165741, "W": 36.82999610504039, "J_1KI": 1234.21608165741, "W_1KI": 36.82999610504039, "W_D": 18.278996105040388, "J_D": 612.5504571070677, "W_D_1KI": 18.278996105040388, "J_D_1KI": 18.278996105040388} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..9980b68 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.268765687942505} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 46, 102, ..., 2499892, + 2499945, 2500000]), + col_indices=tensor([ 987, 1836, 5791, ..., 47187, 47558, 49789]), + values=tensor([0.1085, 0.8855, 0.3536, ..., 0.4174, 0.4340, 0.6085]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6397, 0.2759, 0.9232, ..., 0.5725, 0.2810, 0.6127]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 27.268765687942505 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 46, 102, ..., 2499892, + 2499945, 2500000]), + col_indices=tensor([ 987, 1836, 5791, ..., 47187, 47558, 49789]), + values=tensor([0.1085, 0.8855, 0.3536, ..., 0.4174, 0.4340, 0.6085]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6397, 0.2759, 0.9232, ..., 0.5725, 0.2810, 0.6127]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 27.268765687942505 seconds + +[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96] +[20.84, 20.76, 20.6, 24.92, 26.28, 30.48, 35.16, 37.64, 40.0, 42.72, 43.28, 43.52, 43.36, 43.36, 43.52, 42.92, 43.08, 42.76, 42.76, 42.52, 42.68, 42.8, 42.88, 43.04, 43.16, 42.96, 42.88, 42.76, 42.52, 42.72, 42.64, 42.64] +33.511165142059326 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039} +[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96, 20.44, 20.76, 20.8, 20.8, 20.72, 20.84, 20.84, 20.56, 20.68, 20.76] +371.02 +18.551 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039, 'J_1KI': 1234.21608165741, 'W_1KI': 36.82999610504039, 'W_D': 18.278996105040388, 'J_D': 612.5504571070677, 'W_D_1KI': 18.278996105040388, 'J_D_1KI': 18.278996105040388} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..02be475 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 19539, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.163734436035156, "TIME_S_1KI": 0.5201767969719615, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 495.2794138240814, "W": 33.936722825817846, "J_1KI": 25.348247803064712, "W_1KI": 1.7368710182618274, "W_D": 13.302722825817849, "J_D": 194.14263413858416, "W_D_1KI": 0.6808292556332386, "J_D_1KI": 0.03484463153862729} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..4c99396 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.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 synthetic csr 1000 -ss 50000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5373842716217041} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 24999, 25000, 25000]), + col_indices=tensor([13933, 723, 18387, ..., 22194, 38514, 2158]), + values=tensor([0.9124, 0.6353, 0.3193, ..., 0.0372, 0.2371, 0.8076]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.6534, 0.7497, 0.2436, ..., 0.0965, 0.5741, 0.5754]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.5373842716217041 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 synthetic csr 19539 -ss 50000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.163734436035156} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24996, 24998, 25000]), + col_indices=tensor([44723, 48345, 32100, ..., 22467, 28064, 29572]), + values=tensor([0.7283, 0.2640, 0.9583, ..., 0.2460, 0.4237, 0.5300]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7754, 0.9311, 0.4703, ..., 0.3816, 0.8788, 0.3934]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.163734436035156 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24996, 24998, 25000]), + col_indices=tensor([44723, 48345, 32100, ..., 22467, 28064, 29572]), + values=tensor([0.7283, 0.2640, 0.9583, ..., 0.2460, 0.4237, 0.5300]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7754, 0.9311, 0.4703, ..., 0.3816, 0.8788, 0.3934]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.163734436035156 seconds + +[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36] +[25.56, 25.6, 26.0, 26.32, 28.76, 32.48, 32.48, 37.24, 41.24, 45.24, 45.88, 45.72, 45.6, 45.64] +14.594202756881714 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846} +[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36, 20.68, 20.68, 20.6, 20.6, 20.52, 20.72, 20.52, 20.72, 20.72, 20.72] +412.67999999999995 +20.633999999999997 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846, 'J_1KI': 25.348247803064712, 'W_1KI': 1.7368710182618274, 'W_D': 13.302722825817849, 'J_D': 194.14263413858416, 'W_D_1KI': 0.6808292556332386, 'J_D_1KI': 0.03484463153862729} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..d8def16 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 9519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149, "TIME_S_1KI": 2.2894973476364626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 607.9340667724609, "W": 24.009013571692563, "J_1KI": 63.86532900225454, "W_1KI": 2.5222201462015508, "W_D": 5.522013571692561, "J_D": 139.82332749271384, "W_D_1KI": 0.5801043777384768, "J_D_1KI": 0.06094173523883567} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..91dd9e3 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.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 synthetic csr 1000 -ss 30000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.2060210704803467} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 6, ..., 89996, 89998, 90000]), + col_indices=tensor([ 2876, 4713, 6957, ..., 29701, 15647, 23288]), + values=tensor([0.6297, 0.3832, 0.4268, ..., 0.4020, 0.1713, 0.6526]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.2297, 0.3740, 0.0656, ..., 0.6156, 0.3028, 0.9303]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 2.2060210704803467 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 synthetic csr 9519 -ss 30000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 89997, 89999, 90000]), + col_indices=tensor([15244, 15936, 9998, ..., 16898, 18863, 20836]), + values=tensor([0.4356, 0.9410, 0.0325, ..., 0.8568, 0.9195, 0.8628]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.3669, 0.2405, 0.0914, ..., 0.8449, 0.6451, 0.3598]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 21.79372525215149 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 89997, 89999, 90000]), + col_indices=tensor([15244, 15936, 9998, ..., 16898, 18863, 20836]), + values=tensor([0.4356, 0.9410, 0.0325, ..., 0.8568, 0.9195, 0.8628]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.3669, 0.2405, 0.0914, ..., 0.8449, 0.6451, 0.3598]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 21.79372525215149 seconds + +[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0] +[20.96, 20.88, 23.92, 25.0, 27.36, 27.36, 28.16, 28.88, 25.92, 25.64, 24.4, 24.24, 24.2, 24.16, 24.24, 24.84, 24.92, 24.92, 24.6, 24.64, 24.64, 24.68, 24.56, 24.8, 24.96] +25.32107639312744 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563} +[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0, 20.24, 20.28, 20.44, 20.36, 20.2, 20.24, 20.24, 20.44, 20.52, 20.48] +369.74 +18.487000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563, 'J_1KI': 63.86532900225454, 'W_1KI': 2.5222201462015508, 'W_D': 5.522013571692561, 'J_D': 139.82332749271384, 'W_D_1KI': 0.5801043777384768, 'J_D_1KI': 0.06094173523883567} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..d309fe1 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796, "TIME_S_1KI": 20.699798345565796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 537.3931253051758, "W": 23.043866328673488, "J_1KI": 537.3931253051758, "W_1KI": 23.043866328673488, "W_D": 4.700866328673488, "J_D": 109.62627590250972, "W_D_1KI": 4.700866328673488, "J_D_1KI": 4.700866328673488} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..67d8d59 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 33, 63, ..., 899945, 899974, + 900000]), + col_indices=tensor([ 547, 1664, 1767, ..., 28485, 29124, 29514]), + values=tensor([0.5453, 0.3696, 0.4974, ..., 0.8638, 0.8625, 0.2546]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.4458, 0.9665, 0.0852, ..., 0.0100, 0.1262, 0.9671]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.699798345565796 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 33, 63, ..., 899945, 899974, + 900000]), + col_indices=tensor([ 547, 1664, 1767, ..., 28485, 29124, 29514]), + values=tensor([0.5453, 0.3696, 0.4974, ..., 0.8638, 0.8625, 0.2546]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.4458, 0.9665, 0.0852, ..., 0.0100, 0.1262, 0.9671]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.699798345565796 seconds + +[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16] +[20.16, 20.08, 20.2, 21.68, 22.88, 25.96, 26.92, 27.04, 26.52, 24.64, 24.28, 23.92, 24.12, 24.12, 24.48, 24.6, 24.4, 24.32, 24.28, 24.36, 24.16, 24.4, 24.32] +23.320441007614136 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488} +[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16, 20.44, 20.2, 20.16, 20.16, 20.4, 20.44, 20.56, 20.52, 20.28, 20.2] +366.86 +18.343 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488, 'J_1KI': 537.3931253051758, 'W_1KI': 23.043866328673488, 'W_D': 4.700866328673488, 'J_D': 109.62627590250972, 'W_D_1KI': 4.700866328673488, 'J_D_1KI': 4.700866328673488} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output new file mode 100644 index 0000000..054bc13 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output @@ -0,0 +1 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.01 -c 16'] diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..00f9500 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52473, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605, "TIME_S_1KI": 0.40458478333849035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 561.8060250091554, "W": 23.114903822014995, "J_1KI": 10.706573380770212, "W_1KI": 0.4405104305455185, "W_D": 4.660903822014994, "J_D": 113.28292210769662, "W_D_1KI": 0.08882480174594543, "J_D_1KI": 0.0016927715538647577} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..a0c66f4 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.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 synthetic csr 1000 -ss 30000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.4002048969268799} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8999, 9000, 9000]), + col_indices=tensor([17165, 27151, 23572, ..., 25119, 9148, 7528]), + values=tensor([0.4884, 0.2785, 0.9649, ..., 0.5831, 0.3229, 0.8447]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.9734, 0.5614, 0.1566, ..., 0.4974, 0.8204, 0.0911]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.4002048969268799 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 synthetic csr 52473 -ss 30000 -sd 1e-05 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8998, 9000, 9000]), + col_indices=tensor([25247, 22356, 16191, ..., 29211, 15014, 22819]), + values=tensor([0.9864, 0.6356, 0.7247, ..., 0.1858, 0.6120, 0.1833]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.8210, 0.2318, 0.4195, ..., 0.3881, 0.9911, 0.4380]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.229777336120605 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8998, 9000, 9000]), + col_indices=tensor([25247, 22356, 16191, ..., 29211, 15014, 22819]), + values=tensor([0.9864, 0.6356, 0.7247, ..., 0.1858, 0.6120, 0.1833]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.8210, 0.2318, 0.4195, ..., 0.3881, 0.9911, 0.4380]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.229777336120605 seconds + +[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48] +[20.48, 20.52, 20.6, 21.72, 23.52, 25.24, 26.32, 26.68, 25.64, 24.84, 24.92, 24.56, 24.64, 24.64, 24.44, 24.12, 24.32, 24.4, 24.32, 24.44, 24.28, 24.08, 24.04, 23.96] +24.3049259185791 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995} +[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48, 20.76, 20.68, 20.68, 20.44, 20.48, 20.24, 20.24, 20.32, 20.36, 20.4] +369.08000000000004 +18.454 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995, 'J_1KI': 10.706573380770212, 'W_1KI': 0.4405104305455185, 'W_D': 4.660903822014994, 'J_D': 113.28292210769662, 'W_D_1KI': 0.08882480174594543, 'J_D_1KI': 0.0016927715538647577} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..9b3aaee --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 66220, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.006447315216064, "TIME_S_1KI": 0.15110914097275843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1856.6265121269225, "W": 145.81, "J_1KI": 28.037247238401125, "W_1KI": 2.2019027484143763, "W_D": 109.5725, "J_D": 1395.2075200605393, "W_D_1KI": 1.6546738145575355, "J_D_1KI": 0.024987523626661668} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..9d496c4 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.20569086074829102} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 18, ..., 999978, + 999986, 1000000]), + col_indices=tensor([ 4321, 11912, 13631, ..., 82074, 92560, 99324]), + values=tensor([0.9071, 0.2919, 0.8193, ..., 0.7739, 0.0445, 0.1624]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6567, 0.9688, 0.9697, ..., 0.6873, 0.4864, 0.9023]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 0.20569086074829102 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '51047', '-ss', '100000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.09404468536377} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 17, ..., 999979, + 999988, 1000000]), + col_indices=tensor([15686, 48109, 49313, ..., 51931, 56127, 66767]), + values=tensor([0.4545, 0.6496, 0.9508, ..., 0.7270, 0.9957, 0.0621]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3660, 0.6002, 0.9317, ..., 0.1977, 0.4107, 0.4541]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 8.09404468536377 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66220', '-ss', '100000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.006447315216064} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 18, ..., 999980, + 999990, 1000000]), + col_indices=tensor([ 4776, 21129, 24622, ..., 75160, 86654, 97411]), + values=tensor([0.8410, 0.1609, 0.8553, ..., 0.3742, 0.0938, 0.8797]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6086, 0.7634, 0.6649, ..., 0.3430, 0.9091, 0.5785]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.006447315216064 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 18, ..., 999980, + 999990, 1000000]), + col_indices=tensor([ 4776, 21129, 24622, ..., 75160, 86654, 97411]), + values=tensor([0.8410, 0.1609, 0.8553, ..., 0.3742, 0.0938, 0.8797]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6086, 0.7634, 0.6649, ..., 0.3430, 0.9091, 0.5785]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.006447315216064 seconds + +[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35] +[145.81] +12.733190536499023 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81} +[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35, 40.83, 39.21, 39.3, 44.69, 39.3, 39.36, 39.77, 39.25, 41.13, 39.66] +724.75 +36.2375 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81, 'J_1KI': 28.037247238401125, 'W_1KI': 2.2019027484143763, 'W_D': 109.5725, 'J_D': 1395.2075200605393, 'W_D_1KI': 1.6546738145575355, 'J_D_1KI': 0.024987523626661668} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..b72a71c --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 101854, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.331042528152466, "TIME_S_1KI": 0.13088383890816724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1504.6270782256126, "W": 115.31, "J_1KI": 14.77239065943029, "W_1KI": 1.1321106682113615, "W_D": 79.84075000000001, "J_D": 1041.8051721085908, "W_D_1KI": 0.7838744673748701, "J_D_1KI": 0.007696059726420858} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..3cc7b50 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1346125602722168} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 99997, 99999, + 100000]), + col_indices=tensor([50727, 53996, 86356, ..., 6143, 63321, 22305]), + values=tensor([0.4164, 0.0014, 0.4337, ..., 0.6487, 0.2549, 0.7487]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.9720, 0.1729, 0.4503, ..., 0.2850, 0.8795, 0.9664]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.1346125602722168 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78001', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.040945768356323} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 100000, 100000, + 100000]), + col_indices=tensor([16049, 52557, 57673, ..., 90883, 73385, 65676]), + values=tensor([0.2845, 0.3961, 0.0285, ..., 0.0101, 0.6896, 0.8511]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.5851, 0.1832, 0.4128, ..., 0.6645, 0.1519, 0.8981]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 8.040945768356323 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101854', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.331042528152466} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 100000, 100000, + 100000]), + col_indices=tensor([ 641, 46150, 85524, ..., 87101, 55219, 61785]), + values=tensor([0.2560, 0.7953, 0.3517, ..., 0.8505, 0.5170, 0.2719]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1951, 0.6662, 0.1969, ..., 0.4780, 0.9904, 0.5617]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 13.331042528152466 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 100000, 100000, + 100000]), + col_indices=tensor([ 641, 46150, 85524, ..., 87101, 55219, 61785]), + values=tensor([0.2560, 0.7953, 0.3517, ..., 0.8505, 0.5170, 0.2719]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1951, 0.6662, 0.1969, ..., 0.4780, 0.9904, 0.5617]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 13.331042528152466 seconds + +[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44] +[115.31] +13.048539400100708 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31} +[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44, 39.93, 39.1, 39.15, 39.29, 39.15, 39.23, 39.19, 39.11, 39.43, 39.06] +709.3849999999999 +35.469249999999995 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31, 'J_1KI': 14.77239065943029, 'W_1KI': 1.1321106682113615, 'W_D': 79.84075000000001, 'J_D': 1041.8051721085908, 'W_D_1KI': 0.7838744673748701, 'J_D_1KI': 0.007696059726420858} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..9b9bfd5 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 282693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.381328821182251, "TIME_S_1KI": 0.0367229780050523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.42685277462, "W": 97.9, "J_1KI": 4.5364648320779795, "W_1KI": 0.3463120770588589, "W_D": 62.39075000000001, "J_D": 817.2785818666817, "W_D_1KI": 0.22070143229581213, "J_D_1KI": 0.00078071063767342} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..b8e2459 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05349230766296387} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 9999, 10000, 10000]), + col_indices=tensor([3626, 2250, 5764, ..., 7539, 8316, 7972]), + values=tensor([0.1411, 0.7419, 0.4018, ..., 0.4202, 0.3955, 0.4235]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.9736, 0.6802, 0.3390, ..., 0.1575, 0.6861, 0.0446]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.05349230766296387 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '196289', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.290691137313843} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 6, ..., 9998, 10000, 10000]), + col_indices=tensor([ 763, 7857, 9582, ..., 1442, 6306, 9133]), + values=tensor([0.7701, 0.8887, 0.1796, ..., 0.1701, 0.0666, 0.3737]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.4503, 0.2095, 0.3791, ..., 0.5528, 0.9269, 0.0093]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 7.290691137313843 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '282693', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.381328821182251} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 4, ..., 9997, 9998, 10000]), + col_indices=tensor([4956, 145, 658, ..., 4096, 6098, 6574]), + values=tensor([0.3279, 0.7076, 0.5307, ..., 0.3493, 0.0702, 0.3289]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0837, 0.8046, 0.5398, ..., 0.6704, 0.0489, 0.7610]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.381328821182251 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 4, ..., 9997, 9998, 10000]), + col_indices=tensor([4956, 145, 658, ..., 4096, 6098, 6574]), + values=tensor([0.3279, 0.7076, 0.5307, ..., 0.3493, 0.0702, 0.3289]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0837, 0.8046, 0.5398, ..., 0.6704, 0.0489, 0.7610]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.381328821182251 seconds + +[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31] +[97.9] +13.099354982376099 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9} +[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31, 39.89, 39.14, 38.98, 38.75, 41.57, 38.58, 39.15, 38.62, 39.12, 38.99] +710.185 +35.509249999999994 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9, 'J_1KI': 4.5364648320779795, 'W_1KI': 0.3463120770588589, 'W_D': 62.39075000000001, 'J_D': 817.2785818666817, 'W_D_1KI': 0.22070143229581213, 'J_D_1KI': 0.00078071063767342} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..d2eaa5f --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 189141, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.465899229049683, "TIME_S_1KI": 0.05533384738924761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1388.9149661660194, "W": 107.77, "J_1KI": 7.343278116146259, "W_1KI": 0.5697865613484121, "W_D": 72.38875, "J_D": 932.9295560643077, "W_D_1KI": 0.3827237352028381, "J_D_1KI": 0.002023483724855204} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..117af51 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06988883018493652} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 21, ..., 99977, 99988, + 100000]), + col_indices=tensor([ 768, 2423, 2910, ..., 9615, 9787, 9788]), + values=tensor([0.1330, 0.2030, 0.8709, ..., 0.6786, 0.0798, 0.8357]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0016, 0.6011, 0.7478, ..., 0.9565, 0.9755, 0.4110]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.06988883018493652 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '150238', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.34029221534729} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 21, ..., 99979, 99987, + 100000]), + col_indices=tensor([ 978, 1327, 2112, ..., 8470, 8534, 8708]), + values=tensor([0.4296, 0.3021, 0.5865, ..., 0.4657, 0.4173, 0.7957]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7639, 0.4914, 0.7736, ..., 0.7926, 0.8542, 0.7117]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 8.34029221534729 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189141', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.465899229049683} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 15, ..., 99985, 99995, + 100000]), + col_indices=tensor([ 277, 3135, 4455, ..., 4161, 8684, 9934]), + values=tensor([0.4295, 0.8999, 0.7885, ..., 0.8935, 0.6648, 0.4808]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1477, 0.6711, 0.3568, ..., 0.3604, 0.6617, 0.9866]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.465899229049683 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 15, ..., 99985, 99995, + 100000]), + col_indices=tensor([ 277, 3135, 4455, ..., 4161, 8684, 9934]), + values=tensor([0.4295, 0.8999, 0.7885, ..., 0.8935, 0.6648, 0.4808]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1477, 0.6711, 0.3568, ..., 0.3604, 0.6617, 0.9866]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.465899229049683 seconds + +[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91] +[107.77] +12.887769937515259 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77} +[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91, 39.56, 38.88, 39.03, 38.89, 39.75, 39.07, 39.18, 39.11, 38.8, 42.89] +707.6249999999999 +35.381249999999994 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77, 'J_1KI': 7.343278116146259, 'W_1KI': 0.5697865613484121, 'W_D': 72.38875, 'J_D': 932.9295560643077, 'W_D_1KI': 0.3827237352028381, 'J_D_1KI': 0.002023483724855204} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..a1716dd --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 105256, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.995163202285767, "TIME_S_1KI": 0.10446115378017184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1749.1652185320856, "W": 133.05, "J_1KI": 16.61819961362854, "W_1KI": 1.264060956145018, "W_D": 97.78125000000001, "J_D": 1285.4983955249193, "W_D_1KI": 0.928985045983127, "J_D_1KI": 0.008825958101990642} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..a290fe5 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.13490986824035645} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 107, 208, ..., 999789, + 999899, 1000000]), + col_indices=tensor([ 114, 296, 309, ..., 9749, 9750, 9977]), + values=tensor([0.3507, 0.7412, 0.8612, ..., 0.2456, 0.4049, 0.8296]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.8457, 0.6850, 0.0016, ..., 0.7234, 0.0569, 0.9899]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 0.13490986824035645 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77829', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.763918876647949} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 109, 200, ..., 999783, + 999885, 1000000]), + col_indices=tensor([ 5, 70, 184, ..., 9826, 9903, 9930]), + values=tensor([0.4822, 0.0560, 0.4645, ..., 0.7540, 0.5324, 0.2081]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2499, 0.5119, 0.0857, ..., 0.6236, 0.3822, 0.7230]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 7.763918876647949 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105256', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.995163202285767} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 112, 220, ..., 999823, + 999909, 1000000]), + col_indices=tensor([ 16, 85, 154, ..., 9645, 9832, 9858]), + values=tensor([0.5111, 0.0405, 0.8270, ..., 0.3072, 0.2885, 0.2472]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6255, 0.9183, 0.8326, ..., 0.9246, 0.2373, 0.5392]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.995163202285767 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 112, 220, ..., 999823, + 999909, 1000000]), + col_indices=tensor([ 16, 85, 154, ..., 9645, 9832, 9858]), + values=tensor([0.5111, 0.0405, 0.8270, ..., 0.3072, 0.2885, 0.2472]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6255, 0.9183, 0.8326, ..., 0.9246, 0.2373, 0.5392]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.995163202285767 seconds + +[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8] +[133.05] +13.146675825119019 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05} +[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8, 40.08, 39.28, 39.91, 38.88, 38.99, 38.83, 39.08, 39.35, 39.95, 38.73] +705.375 +35.26875 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05, 'J_1KI': 16.61819961362854, 'W_1KI': 1.264060956145018, 'W_D': 97.78125000000001, 'J_D': 1285.4983955249193, 'W_D_1KI': 0.928985045983127, 'J_D_1KI': 0.008825958101990642} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..0aa6e19 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27486, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.233055591583252, "TIME_S_1KI": 0.3723006472961963, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2077.8737717533113, "W": 151.69, "J_1KI": 75.59753226199925, "W_1KI": 5.518809575784036, "W_D": 115.92275, "J_D": 1587.9282864692211, "W_D_1KI": 4.217519828276212, "J_D_1KI": 0.1534424735602202} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..a8a40a5 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.45975399017333984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 512, 1006, ..., 4999034, + 4999489, 5000000]), + col_indices=tensor([ 23, 40, 103, ..., 9927, 9976, 9991]), + values=tensor([0.6183, 0.2980, 0.3566, ..., 0.0352, 0.5258, 0.0852]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4623, 0.5953, 0.6862, ..., 0.1082, 0.6720, 0.4260]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 0.45975399017333984 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22838', '-ss', '10000', '-sd', '0.05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.724292278289795} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 513, 1006, ..., 4998953, + 4999498, 5000000]), + col_indices=tensor([ 69, 83, 128, ..., 9917, 9953, 9972]), + values=tensor([0.6637, 0.2623, 0.2360, ..., 0.3507, 0.8119, 0.6229]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.8552, 0.8520, 0.0158, ..., 0.2551, 0.9127, 0.4905]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 8.724292278289795 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27486', '-ss', '10000', '-sd', '0.05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.233055591583252} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 518, 1031, ..., 4999018, + 4999521, 5000000]), + col_indices=tensor([ 2, 29, 76, ..., 9919, 9923, 9942]), + values=tensor([0.8327, 0.8899, 0.2406, ..., 0.0134, 0.9622, 0.2874]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5989, 0.5463, 0.4311, ..., 0.3886, 0.2295, 0.1764]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.233055591583252 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 518, 1031, ..., 4999018, + 4999521, 5000000]), + col_indices=tensor([ 2, 29, 76, ..., 9919, 9923, 9942]), + values=tensor([0.8327, 0.8899, 0.2406, ..., 0.0134, 0.9622, 0.2874]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5989, 0.5463, 0.4311, ..., 0.3886, 0.2295, 0.1764]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.233055591583252 seconds + +[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49] +[151.69] +13.698159217834473 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69} +[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49, 40.16, 40.86, 39.65, 39.13, 39.26, 39.59, 39.63, 39.11, 39.14, 39.23] +715.345 +35.767250000000004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69, 'J_1KI': 75.59753226199925, 'W_1KI': 5.518809575784036, 'W_D': 115.92275, 'J_D': 1587.9282864692211, 'W_D_1KI': 4.217519828276212, 'J_D_1KI': 0.1534424735602202} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..cda3d72 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 375977, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.999524116516113, "TIME_S_1KI": 0.029255843087518954, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.755545105934, "W": 96.41999999999999, "J_1KI": 3.5208418203930933, "W_1KI": 0.25645185742744897, "W_D": 61.132499999999986, "J_D": 839.2914941006896, "W_D_1KI": 0.16259638222550846, "J_D_1KI": 0.0004324636406628822} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..e9eebf5 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1900 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04503059387207031} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([4113, 614, 2519, 8527, 7416, 2734, 949, 6682, 484, + 5512, 2710, 4041, 4037, 3756, 4925, 4764, 8722, 3874, + 8352, 2481, 3774, 8294, 3589, 6629, 1741, 283, 3355, + 5134, 1636, 5257, 6914, 8377, 9479, 3405, 2807, 6603, + 730, 4849, 7221, 7178, 5773, 4547, 9572, 5072, 5733, + 8766, 8040, 7105, 6968, 6795, 4519, 4433, 7044, 2666, + 5807, 2089, 4272, 1275, 3276, 409, 2016, 5940, 4287, + 7005, 5810, 8597, 1286, 8246, 5523, 3085, 4475, 3444, + 5153, 3360, 5524, 9599, 3802, 5759, 6854, 9537, 9505, + 7933, 4849, 4073, 6294, 3565, 5654, 9049, 3619, 8438, + 2201, 1301, 373, 5050, 213, 3319, 2294, 9757, 2234, + 1810, 8112, 6888, 8132, 3918, 894, 916, 3277, 7303, + 4439, 8812, 5563, 6709, 2634, 805, 7224, 2711, 9378, + 7538, 3829, 8492, 5794, 788, 7855, 1497, 1779, 6847, + 7754, 9099, 5015, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.04503059387207031 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233174', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.088708162307739} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([6339, 4641, 8767, 9629, 5248, 7593, 5215, 4233, 3907, + 8989, 5838, 7934, 891, 5239, 5411, 4963, 7963, 8173, + 4172, 4464, 4601, 9580, 2260, 6961, 5855, 1265, 1349, + 2190, 6418, 252, 5026, 6780, 5825, 9726, 7411, 4666, + 5731, 7839, 3753, 9206, 4521, 3044, 7848, 4653, 8995, + 579, 4725, 4836, 8826, 2364, 9710, 713, 7544, 9065, + 7816, 8496, 3385, 1467, 5199, 2666, 5229, 7632, 1859, + 2358, 9351, 6205, 2016, 380, 3677, 1279, 8529, 4708, + 600, 8708, 262, 2780, 7779, 4205, 2568, 2645, 4928, + 4767, 5127, 4130, 3518, 487, 2778, 3925, 1657, 1278, + 7068, 3351, 3630, 7719, 3614, 1109, 142, 4337, 7018, + 7816, 7494, 2297, 4786, 6789, 6911, 449, 6163, 812, + 8883, 3887, 726, 6261, 3381, 1211, 7361, 2658, 4836, + 2934, 3551, 5047, 7903, 3714, 2712, 9272, 1649, 9481, + 7845, 4115, 8011, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 7.088708162307739 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '345384', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.645604133605957} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([8386, 6687, 1336, 4006, 2889, 63, 3271, 8876, 5809, + 2512, 7832, 2733, 3356, 4778, 2140, 779, 9751, 7240, + 1181, 7321, 2435, 1700, 1145, 7058, 2671, 9573, 9448, + 6201, 8201, 4132, 8088, 4935, 4564, 1801, 2140, 3767, + 4154, 3041, 6652, 3892, 6804, 8117, 8836, 7838, 9227, + 9815, 3957, 6041, 6513, 836, 4077, 5740, 165, 6693, + 9253, 4488, 6697, 8121, 527, 1601, 2341, 3820, 1804, + 1657, 7490, 6245, 3372, 1433, 9979, 3717, 6873, 3081, + 6306, 2907, 8882, 4044, 1805, 6070, 7397, 6632, 4430, + 9050, 4939, 4243, 9520, 9436, 8610, 3565, 1962, 5009, + 8292, 6355, 4785, 739, 8013, 464, 9981, 613, 9648, + 3885, 9065, 7010, 9621, 9406, 7765, 5572, 4542, 1690, + 8782, 2394, 9222, 6205, 3475, 4880, 6672, 2424, 2888, + 27, 9101, 629, 9556, 9408, 9624, 5180, 5403, 1419, + 4216, 6980, 2180, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 9.645604133605957 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '375977', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.999524116516113} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7315, 7011, 7905, 1028, 2803, 8634, 5420, 3714, 9961, + 9682, 9462, 3672, 9521, 6357, 2121, 3239, 5077, 5611, + 4819, 9590, 9566, 7110, 9282, 3205, 6562, 8535, 6101, + 4471, 8275, 3563, 3283, 2844, 9366, 4926, 9577, 7356, + 8518, 1230, 10, 3109, 6967, 7024, 3566, 6230, 1306, + 5778, 1783, 7611, 4767, 1036, 2386, 1905, 3222, 7598, + 3813, 6094, 6353, 9093, 5396, 1174, 7424, 6062, 4513, + 177, 8866, 7252, 2860, 4744, 8855, 2227, 299, 9342, + 3509, 1775, 3656, 5550, 9595, 6991, 8012, 9812, 5920, + 3934, 6803, 5774, 7689, 674, 5602, 3014, 6143, 7099, + 663, 4281, 4779, 9464, 8707, 8638, 8538, 5514, 6658, + 4407, 5833, 3387, 3279, 4896, 4259, 2176, 8287, 8834, + 3999, 3877, 1161, 9724, 9738, 238, 3075, 5186, 7486, + 891, 9045, 5190, 5381, 5459, 4110, 1402, 6321, 6193, + 9155, 9992, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.999524116516113 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7315, 7011, 7905, 1028, 2803, 8634, 5420, 3714, 9961, + 9682, 9462, 3672, 9521, 6357, 2121, 3239, 5077, 5611, + 4819, 9590, 9566, 7110, 9282, 3205, 6562, 8535, 6101, + 4471, 8275, 3563, 3283, 2844, 9366, 4926, 9577, 7356, + 8518, 1230, 10, 3109, 6967, 7024, 3566, 6230, 1306, + 5778, 1783, 7611, 4767, 1036, 2386, 1905, 3222, 7598, + 3813, 6094, 6353, 9093, 5396, 1174, 7424, 6062, 4513, + 177, 8866, 7252, 2860, 4744, 8855, 2227, 299, 9342, + 3509, 1775, 3656, 5550, 9595, 6991, 8012, 9812, 5920, + 3934, 6803, 5774, 7689, 674, 5602, 3014, 6143, 7099, + 663, 4281, 4779, 9464, 8707, 8638, 8538, 5514, 6658, + 4407, 5833, 3387, 3279, 4896, 4259, 2176, 8287, 8834, + 3999, 3877, 1161, 9724, 9738, 238, 3075, 5186, 7486, + 891, 9045, 5190, 5381, 5459, 4110, 1402, 6321, 6193, + 9155, 9992, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.999524116516113 seconds + +[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67] +[96.42] +13.729055643081665 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999} +[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67, 40.17, 39.41, 39.19, 38.69, 38.88, 39.53, 40.39, 39.3, 39.0, 38.88] +705.75 +35.2875 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999, 'J_1KI': 3.5208418203930933, 'W_1KI': 0.25645185742744897, 'W_D': 61.132499999999986, 'J_D': 839.2914941006896, 'W_D_1KI': 0.16259638222550846, 'J_D_1KI': 0.0004324636406628822} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..7e41519 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21375, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.636817216873169, "TIME_S_1KI": 0.4976288756431892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2053.7217113614083, "W": 152.01000000000002, "J_1KI": 96.08054789994893, "W_1KI": 7.111578947368422, "W_D": 116.32275000000001, "J_D": 1571.5713255724313, "W_D_1KI": 5.442000000000001, "J_D_1KI": 0.2545964912280702} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..e90e199 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,89 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5442898273468018} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 11, ..., 2499988, + 2499996, 2500000]), + col_indices=tensor([ 37839, 98870, 148404, ..., 161688, 445826, + 487462]), + values=tensor([0.2708, 0.4230, 0.0396, ..., 0.5012, 0.9237, 0.4084]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6604, 0.4578, 0.9008, ..., 0.1692, 0.6250, 0.2013]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 0.5442898273468018 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19291', '-ss', '500000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.475887298583984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 11, ..., 2499990, + 2499994, 2500000]), + col_indices=tensor([ 2997, 16168, 106256, ..., 284595, 359619, + 400100]), + values=tensor([0.5956, 0.5098, 0.7367, ..., 0.1293, 0.8182, 0.3844]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4741, 0.3124, 0.4103, ..., 0.8230, 0.7925, 0.1055]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 9.475887298583984 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21375', '-ss', '500000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.636817216873169} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 2499987, + 2499990, 2500000]), + col_indices=tensor([ 69634, 109368, 119504, ..., 397654, 413765, + 480494]), + values=tensor([0.1977, 0.6347, 0.9236, ..., 0.5996, 0.0558, 0.7507]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.2633, 0.4244, 0.4182, ..., 0.0717, 0.3446, 0.9616]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.636817216873169 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 2499987, + 2499990, 2500000]), + col_indices=tensor([ 69634, 109368, 119504, ..., 397654, 413765, + 480494]), + values=tensor([0.1977, 0.6347, 0.9236, ..., 0.5996, 0.0558, 0.7507]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.2633, 0.4244, 0.4182, ..., 0.0717, 0.3446, 0.9616]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.636817216873169 seconds + +[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64] +[152.01] +13.510438203811646 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002} +[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64, 39.92, 39.78, 39.31, 39.34, 39.44, 40.15, 39.73, 39.14, 39.7, 39.19] +713.7450000000001 +35.687250000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002, 'J_1KI': 96.08054789994893, 'W_1KI': 7.111578947368422, 'W_D': 116.32275000000001, 'J_D': 1571.5713255724313, 'W_D_1KI': 5.442000000000001, 'J_D_1KI': 0.2545964912280702} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..7a8ab5e --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 88993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.460110664367676, "TIME_S_1KI": 0.11753857791475371, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1554.5358743476868, "W": 116.24, "J_1KI": 17.468069110465844, "W_1KI": 1.3061701482139043, "W_D": 80.32, "J_D": 1074.1596819305419, "W_D_1KI": 0.9025428966323192, "J_D_1KI": 0.010141729086920537} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..82a28ba --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,105 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1613328456878662} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 9, ..., 249987, 249990, + 250000]), + col_indices=tensor([ 2831, 11435, 18332, ..., 36257, 39398, 40541]), + values=tensor([0.1158, 0.5239, 0.2299, ..., 0.2166, 0.7808, 0.4412]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.7586, 0.4736, 0.7326, ..., 0.5631, 0.8162, 0.2413]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.1613328456878662 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65082', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.079791784286499} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 249989, 249995, + 250000]), + col_indices=tensor([ 9506, 10457, 11174, ..., 14178, 16522, 25750]), + values=tensor([0.5729, 0.5279, 0.3744, ..., 0.1961, 0.5511, 0.6709]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0404, 0.4787, 0.7701, ..., 0.8815, 0.0868, 0.4305]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 8.079791784286499 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84576', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.978835582733154} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 7, ..., 249990, 249996, + 250000]), + col_indices=tensor([26217, 28400, 13678, ..., 15637, 35417, 48424]), + values=tensor([0.3837, 0.9571, 0.9616, ..., 0.3970, 0.1960, 0.8766]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.6737, 0.6555, 0.0878, ..., 0.0726, 0.6482, 0.1469]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 9.978835582733154 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '88993', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.460110664367676} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 12, ..., 249992, 249993, + 250000]), + col_indices=tensor([ 7470, 20811, 24121, ..., 36968, 38743, 40607]), + values=tensor([0.2685, 0.7271, 0.6618, ..., 0.0403, 0.7886, 0.4035]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0454, 0.0390, 0.3317, ..., 0.3195, 0.9524, 0.5758]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.460110664367676 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 12, ..., 249992, 249993, + 250000]), + col_indices=tensor([ 7470, 20811, 24121, ..., 36968, 38743, 40607]), + values=tensor([0.2685, 0.7271, 0.6618, ..., 0.0403, 0.7886, 0.4035]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0454, 0.0390, 0.3317, ..., 0.3195, 0.9524, 0.5758]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.460110664367676 seconds + +[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6] +[116.24] +13.373502016067505 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24} +[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6, 39.84, 40.48, 39.37, 39.22, 39.24, 38.98, 44.18, 39.09, 39.18, 39.34] +718.4 +35.92 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24, 'J_1KI': 17.468069110465844, 'W_1KI': 1.3061701482139043, 'W_D': 80.32, 'J_D': 1074.1596819305419, 'W_D_1KI': 0.9025428966323192, 'J_D_1KI': 0.010141729086920537} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..19e233d --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46287, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.077528476715088, "TIME_S_1KI": 0.23932267108940064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2013.3453536748884, "W": 148.1, "J_1KI": 43.49699383573981, "W_1KI": 3.1996024801780196, "W_D": 112.52425, "J_D": 1529.7108434385657, "W_D_1KI": 2.4310119471989973, "J_D_1KI": 0.052520404156652996} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..a9b5d6b --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2967829704284668} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 60, 108, ..., 2499894, + 2499951, 2500000]), + col_indices=tensor([ 368, 1693, 4088, ..., 44885, 46596, 47442]), + values=tensor([0.5982, 0.3592, 0.7042, ..., 0.6155, 0.2314, 0.2925]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7227, 0.5816, 0.4934, ..., 0.3583, 0.6407, 0.9822]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 0.2967829704284668 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35379', '-ss', '50000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.025555610656738} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 48, 96, ..., 2499914, + 2499959, 2500000]), + col_indices=tensor([ 123, 723, 909, ..., 47588, 48779, 49819]), + values=tensor([0.6654, 0.3505, 0.8901, ..., 0.8476, 0.5107, 0.1185]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6658, 0.6242, 0.4020, ..., 0.5009, 0.1451, 0.6481]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 8.025555610656738 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46287', '-ss', '50000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.077528476715088} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 45, 94, ..., 2499903, + 2499951, 2500000]), + col_indices=tensor([ 506, 1320, 4404, ..., 49283, 49651, 49966]), + values=tensor([0.0094, 0.0130, 0.0811, ..., 0.5846, 0.7695, 0.1584]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8952, 0.2999, 0.6108, ..., 0.3758, 0.9662, 0.9596]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 11.077528476715088 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 45, 94, ..., 2499903, + 2499951, 2500000]), + col_indices=tensor([ 506, 1320, 4404, ..., 49283, 49651, 49966]), + values=tensor([0.0094, 0.0130, 0.0811, ..., 0.5846, 0.7695, 0.1584]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8952, 0.2999, 0.6108, ..., 0.3758, 0.9662, 0.9596]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 11.077528476715088 seconds + +[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34] +[148.1] +13.594499349594116 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1} +[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34, 40.01, 40.0, 39.66, 39.57, 39.25, 39.46, 39.33, 39.3, 39.25, 40.41] +711.5149999999999 +35.57574999999999 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1, 'J_1KI': 43.49699383573981, 'W_1KI': 3.1996024801780196, 'W_D': 112.52425, 'J_D': 1529.7108434385657, 'W_D_1KI': 2.4310119471989973, 'J_D_1KI': 0.052520404156652996} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..1e1cae0 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 126164, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.496079683303833, "TIME_S_1KI": 0.08319393553869434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1339.4068178844452, "W": 103.41, "J_1KI": 10.616394675854009, "W_1KI": 0.8196474430106845, "W_D": 67.667, "J_D": 876.4494840517044, "W_D_1KI": 0.5363415871405472, "J_D_1KI": 0.0042511460253364455} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..f5dabe4 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.13353276252746582} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 25000, 25000]), + col_indices=tensor([10989, 5739, 28866, ..., 21823, 4005, 34886]), + values=tensor([0.4353, 0.4497, 0.0871, ..., 0.0925, 0.2903, 0.5435]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7562, 0.8922, 0.4564, ..., 0.1486, 0.4797, 0.4813]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.13353276252746582 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78632', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.544129848480225} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 24999, 25000]), + col_indices=tensor([34114, 35224, 10296, ..., 13464, 985, 3770]), + values=tensor([0.2384, 0.3975, 0.4000, ..., 0.4541, 0.7785, 0.5313]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.2311, 0.0634, 0.6873, ..., 0.2883, 0.1765, 0.0650]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 6.544129848480225 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '126164', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.496079683303833} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 25000, 25000, 25000]), + col_indices=tensor([ 3707, 41195, 46820, ..., 24919, 16438, 24153]), + values=tensor([0.4077, 0.2091, 0.6369, ..., 0.9924, 0.1508, 0.5036]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4831, 0.5861, 0.9166, ..., 0.7031, 0.1228, 0.1244]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.496079683303833 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 25000, 25000, 25000]), + col_indices=tensor([ 3707, 41195, 46820, ..., 24919, 16438, 24153]), + values=tensor([0.4077, 0.2091, 0.6369, ..., 0.9924, 0.1508, 0.5036]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4831, 0.5861, 0.9166, ..., 0.7031, 0.1228, 0.1244]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.496079683303833 seconds + +[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0] +[103.41] +12.952391624450684 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41} +[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0, 39.67, 39.09, 39.31, 39.83, 39.31, 39.25, 39.43, 38.89, 44.16, 39.22] +714.86 +35.743 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41, 'J_1KI': 10.616394675854009, 'W_1KI': 0.8196474430106845, 'W_D': 67.667, 'J_D': 876.4494840517044, 'W_D_1KI': 0.5363415871405472, 'J_D_1KI': 0.0042511460253364455} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..62f730b --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 250038, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168, "TIME_S_1KI": 0.08971754503154387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2606.0890457463265, "W": 109.03, "J_1KI": 10.422771921653215, "W_1KI": 0.4360537198345852, "W_D": 73.72525, "J_D": 1762.2174302477242, "W_D_1KI": 0.2948561818603573, "J_D_1KI": 0.0011792454821281456} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..3701848 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10453343391418457} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 3, ..., 89993, 89997, 90000]), + col_indices=tensor([20651, 24290, 28771, ..., 10287, 15356, 24487]), + values=tensor([0.1253, 0.8320, 0.5079, ..., 0.2152, 0.2753, 0.6533]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.9310, 0.8886, 0.9050, ..., 0.7990, 0.2751, 0.5722]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 0.10453343391418457 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200892', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 16.872318267822266} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 5, ..., 89991, 89992, 90000]), + col_indices=tensor([ 9009, 16842, 24312, ..., 27764, 28622, 29005]), + values=tensor([0.8393, 0.9269, 0.8193, ..., 0.0379, 0.8842, 0.8625]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.6604, 0.9619, 0.4104, ..., 0.2632, 0.2079, 0.2105]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 16.872318267822266 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '250038', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 6, ..., 89997, 89998, 90000]), + col_indices=tensor([12588, 20450, 20704, ..., 21668, 10676, 12342]), + values=tensor([0.6372, 0.0652, 0.9949, ..., 0.3492, 0.9239, 0.3604]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.0127, 0.8502, 0.1682, ..., 0.0608, 0.3685, 0.2970]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 22.432795524597168 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 6, ..., 89997, 89998, 90000]), + col_indices=tensor([12588, 20450, 20704, ..., 21668, 10676, 12342]), + values=tensor([0.6372, 0.0652, 0.9949, ..., 0.3492, 0.9239, 0.3604]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.0127, 0.8502, 0.1682, ..., 0.0608, 0.3685, 0.2970]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 22.432795524597168 seconds + +[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87] +[109.03] +23.90249514579773 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03} +[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87, 40.47, 39.15, 39.52, 39.41, 39.16, 39.78, 39.02, 38.95, 38.92, 38.96] +706.095 +35.30475 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03, 'J_1KI': 10.422771921653215, 'W_1KI': 0.4360537198345852, 'W_D': 73.72525, 'J_D': 1762.2174302477242, 'W_D_1KI': 0.2948561818603573, 'J_D_1KI': 0.0011792454821281456} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..8658000 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,21 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.1439976692199707} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 29, 67, ..., 899936, 899964, + 900000]), + col_indices=tensor([ 58, 341, 3959, ..., 27670, 28034, 29816]), + values=tensor([0.8286, 0.0691, 0.1730, ..., 0.2645, 0.7295, 0.5386]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.0558, 0.4553, 0.9674, ..., 0.2366, 0.6209, 0.6160]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 0.1439976692199707 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '145835', '-ss', '30000', '-sd', '0.001', '-c', '16'] diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..9b8b9bd --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 321850, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104, "TIME_S_1KI": 0.06398858278827747, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2327.1387594389917, "W": 101.41000000000001, "J_1KI": 7.230507253189348, "W_1KI": 0.3150846667702346, "W_D": 65.9145, "J_D": 1512.5942979887725, "W_D_1KI": 0.20479881932577287, "J_D_1KI": 0.0006363175992722476} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..e9d1ad8 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08333611488342285} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 9000, 9000, 9000]), + col_indices=tensor([13464, 15002, 12998, ..., 1674, 7890, 9839]), + values=tensor([0.3937, 0.5826, 0.6728, ..., 0.2443, 0.0810, 0.3168]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3767, 0.3322, 0.0921, ..., 0.4449, 0.8687, 0.6223]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.08333611488342285 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251991', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.441835403442383} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9000, 9000, 9000]), + col_indices=tensor([ 1592, 26221, 2007, ..., 5499, 7511, 18290]), + values=tensor([0.1009, 0.0773, 0.0762, ..., 0.6540, 0.2265, 0.9524]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.5719, 0.1239, 0.1698, ..., 0.8424, 0.3509, 0.9636]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 16.441835403442383 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '321850', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 9000, 9000, 9000]), + col_indices=tensor([28655, 14046, 22660, ..., 19793, 14001, 26576]), + values=tensor([0.0604, 0.3035, 0.4856, ..., 0.8323, 0.7946, 0.0096]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.2670, 0.6630, 0.3861, ..., 0.4215, 0.9031, 0.7574]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 20.594725370407104 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 9000, 9000, 9000]), + col_indices=tensor([28655, 14046, 22660, ..., 19793, 14001, 26576]), + values=tensor([0.0604, 0.3035, 0.4856, ..., 0.8323, 0.7946, 0.0096]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.2670, 0.6630, 0.3861, ..., 0.4215, 0.9031, 0.7574]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 20.594725370407104 seconds + +[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16] +[101.41] +22.94782328605652 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001} +[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16, 39.61, 44.11, 38.89, 39.27, 38.75, 38.81, 40.71, 38.62, 38.81, 38.61] +709.91 +35.4955 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001, 'J_1KI': 7.230507253189348, 'W_1KI': 0.3150846667702346, 'W_D': 65.9145, 'J_D': 1512.5942979887725, 'W_D_1KI': 0.20479881932577287, 'J_D_1KI': 0.0006363175992722476} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..092cf4b --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33012, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519692420959473, "TIME_S_1KI": 0.318662680872394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.664799463749, "W": 88.39, "J_1KI": 37.09756450574788, "W_1KI": 2.677511208045559, "W_D": 72.108, "J_D": 999.0737567567826, "W_D_1KI": 2.184296619411123, "J_D_1KI": 0.06616674601390778} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..d5ef17d --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3180568218231201} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 17, ..., 999979, + 999991, 1000000]), + col_indices=tensor([10691, 12782, 14246, ..., 70658, 88202, 93324]), + values=tensor([0.3844, 0.6658, 0.7124, ..., 0.3153, 0.8920, 0.6509]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9202, 0.9151, 0.8232, ..., 0.5628, 0.6151, 0.8368]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 0.3180568218231201 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', 'synthetic', 'csr', '33012', '-ss', '100000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519692420959473} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 15, ..., 999984, + 999990, 1000000]), + col_indices=tensor([ 7405, 49048, 69982, ..., 87685, 98650, 99933]), + values=tensor([0.6053, 0.2022, 0.4562, ..., 0.3977, 0.5709, 0.7435]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0490, 0.1129, 0.5767, ..., 0.3037, 0.9982, 0.0194]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.519692420959473 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 15, ..., 999984, + 999990, 1000000]), + col_indices=tensor([ 7405, 49048, 69982, ..., 87685, 98650, 99933]), + values=tensor([0.6053, 0.2022, 0.4562, ..., 0.3977, 0.5709, 0.7435]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0490, 0.1129, 0.5767, ..., 0.3037, 0.9982, 0.0194]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.519692420959473 seconds + +[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75] +[88.39] +13.855241537094116 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39} +[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75, 18.55, 17.94, 18.05, 17.81, 18.35, 17.79, 18.28, 18.36, 18.2, 17.83] +325.64 +16.282 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39, 'J_1KI': 37.09756450574788, 'W_1KI': 2.677511208045559, 'W_D': 72.108, 'J_D': 999.0737567567826, 'W_D_1KI': 2.184296619411123, 'J_D_1KI': 0.06616674601390778} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..d7ba913 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64591, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.66994047164917, "TIME_S_1KI": 0.16519237156336286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1159.8280617713929, "W": 82.8, "J_1KI": 17.9564964433341, "W_1KI": 1.2819123407285846, "W_D": 66.57124999999999, "J_D": 932.5024620434641, "W_D_1KI": 1.0306582960474369, "J_D_1KI": 0.015956685854800777} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..ad36eba --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.17906904220581055} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 99999, 99999, + 100000]), + col_indices=tensor([85471, 5444, 13434, ..., 17615, 87992, 83918]), + values=tensor([0.7119, 0.1219, 0.2242, ..., 0.7199, 0.3920, 0.9751]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8861, 0.1716, 0.8373, ..., 0.2826, 0.6276, 0.0027]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.17906904220581055 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', 'synthetic', 'csr', '58636', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.531909704208374} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 99999, 100000, + 100000]), + col_indices=tensor([28875, 86601, 1118, ..., 53659, 98581, 89346]), + values=tensor([0.0170, 0.0837, 0.6677, ..., 0.0775, 0.7543, 0.4196]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4702, 0.4277, 0.7376, ..., 0.9470, 0.3873, 0.6416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 9.531909704208374 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', 'synthetic', 'csr', '64591', '-ss', '100000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.66994047164917} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 100000, 100000, + 100000]), + col_indices=tensor([32373, 45973, 94969, ..., 5823, 12968, 35562]), + values=tensor([0.6698, 0.7885, 0.1863, ..., 0.4943, 0.2796, 0.7613]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4737, 0.5533, 0.8139, ..., 0.3662, 0.3156, 0.7007]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.66994047164917 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 100000, 100000, + 100000]), + col_indices=tensor([32373, 45973, 94969, ..., 5823, 12968, 35562]), + values=tensor([0.6698, 0.7885, 0.1863, ..., 0.4943, 0.2796, 0.7613]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4737, 0.5533, 0.8139, ..., 0.3662, 0.3156, 0.7007]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.66994047164917 seconds + +[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71] +[82.8] +14.007585287094116 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8} +[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71, 18.29, 17.85, 18.6, 17.8, 18.13, 17.74, 17.99, 17.83, 18.16, 17.94] +324.57500000000005 +16.22875 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8, 'J_1KI': 17.9564964433341, 'W_1KI': 1.2819123407285846, 'W_D': 66.57124999999999, 'J_D': 932.5024620434641, 'W_D_1KI': 1.0306582960474369, 'J_D_1KI': 0.015956685854800777} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..c1f8ab6 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 250193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.988550901412964, "TIME_S_1KI": 0.043920297136262665, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.7657339859009, "W": 74.48, "J_1KI": 4.267768218878628, "W_1KI": 0.29769018317858614, "W_D": 58.048, "J_D": 832.1920693664551, "W_D_1KI": 0.23201288605196788, "J_D_1KI": 0.0009273356410929478} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..15dcb35 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.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', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.06029987335205078} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 9998, 9999, 10000]), + col_indices=tensor([9584, 2249, 9621, ..., 267, 2843, 1232]), + values=tensor([0.1887, 0.8280, 0.8733, ..., 0.6422, 0.8241, 0.9503]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.2203, 0.8610, 0.9153, ..., 0.2931, 0.9983, 0.3156]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.06029987335205078 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', 'synthetic', 'csr', '174129', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.307769536972046} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 10000, 10000, 10000]), + col_indices=tensor([5050, 9096, 467, ..., 6460, 6547, 2963]), + values=tensor([0.3312, 0.9984, 0.8182, ..., 0.5509, 0.3722, 0.7285]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0543, 0.3720, 0.3677, ..., 0.5280, 0.6433, 0.3148]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 7.307769536972046 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', 'synthetic', 'csr', '250193', '-ss', '10000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.988550901412964} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 9997, 9999, 10000]), + col_indices=tensor([4233, 4275, 7541, ..., 2248, 7833, 717]), + values=tensor([0.0347, 0.7995, 0.4404, ..., 0.0217, 0.2651, 0.9390]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.4739, 0.4789, 0.6628, ..., 0.7267, 0.9323, 0.5704]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.988550901412964 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 9997, 9999, 10000]), + col_indices=tensor([4233, 4275, 7541, ..., 2248, 7833, 717]), + values=tensor([0.0347, 0.7995, 0.4404, ..., 0.0217, 0.2651, 0.9390]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.4739, 0.4789, 0.6628, ..., 0.7267, 0.9323, 0.5704]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.988550901412964 seconds + +[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0] +[74.48] +14.33627462387085 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48} +[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0, 18.31, 18.29, 18.5, 18.09, 18.0, 17.95, 17.89, 18.08, 18.14, 17.85] +328.64 +16.432 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48, 'J_1KI': 4.267768218878628, 'W_1KI': 0.29769018317858614, 'W_D': 58.048, 'J_D': 832.1920693664551, 'W_D_1KI': 0.23201288605196788, 'J_D_1KI': 0.0009273356410929478} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..dab286f --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 186516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.228839635848999, "TIME_S_1KI": 0.054841620214078145, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1075.814607758522, "W": 79.58, "J_1KI": 5.767948099672533, "W_1KI": 0.4266658088314139, "W_D": 63.054, "J_D": 852.4053063282967, "W_D_1KI": 0.338062150164061, "J_D_1KI": 0.0018125101876732344} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..fad6e92 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0709388256072998} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 17, ..., 99974, 99988, + 100000]), + col_indices=tensor([1106, 1398, 2518, ..., 6886, 7547, 8173]), + values=tensor([0.5902, 0.0057, 0.8492, ..., 0.2608, 0.7269, 0.6940]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8144, 0.0674, 0.1585, ..., 0.0850, 0.2846, 0.5370]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.0709388256072998 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', 'synthetic', 'csr', '148014', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.332475900650024} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 17, ..., 99965, 99978, + 100000]), + col_indices=tensor([ 77, 628, 3642, ..., 8176, 8481, 9600]), + values=tensor([0.7580, 0.3721, 0.0885, ..., 0.9345, 0.1388, 0.5730]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.9678, 0.5744, 0.4262, ..., 0.2115, 0.3242, 0.5272]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 8.332475900650024 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', 'synthetic', 'csr', '186516', '-ss', '10000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.228839635848999} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 30, ..., 99982, 99990, + 100000]), + col_indices=tensor([ 298, 367, 1190, ..., 3689, 6850, 7173]), + values=tensor([0.7086, 0.6908, 0.8648, ..., 0.4576, 0.3199, 0.8368]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7366, 0.0593, 0.8663, ..., 0.2557, 0.4256, 0.5242]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.228839635848999 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 30, ..., 99982, 99990, + 100000]), + col_indices=tensor([ 298, 367, 1190, ..., 3689, 6850, 7173]), + values=tensor([0.7086, 0.6908, 0.8648, ..., 0.4576, 0.3199, 0.8368]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7366, 0.0593, 0.8663, ..., 0.2557, 0.4256, 0.5242]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.228839635848999 seconds + +[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71] +[79.58] +13.51865553855896 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58} +[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71, 19.07, 18.27, 18.23, 17.94, 18.27, 18.14, 21.36, 18.53, 18.63, 18.33] +330.52 +16.526 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58, 'J_1KI': 5.767948099672533, 'W_1KI': 0.4266658088314139, 'W_D': 63.054, 'J_D': 852.4053063282967, 'W_D_1KI': 0.338062150164061, 'J_D_1KI': 0.0018125101876732344} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..27eac6d --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.399010181427002, "TIME_S_1KI": 0.18086178724849997, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1216.1904487371446, "W": 87.17000000000002, "J_1KI": 21.15224183413299, "W_1KI": 1.5160790997791191, "W_D": 70.89300000000001, "J_D": 989.0947514319422, "W_D_1KI": 1.2329860688383745, "J_D_1KI": 0.021444354815701245} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..98e81fe --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1964414119720459} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 102, 210, ..., 999804, + 999909, 1000000]), + col_indices=tensor([ 4, 297, 328, ..., 9417, 9717, 9744]), + values=tensor([0.3827, 0.2830, 0.2497, ..., 0.1291, 0.2102, 0.5312]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.7948, 0.9855, 0.6473, ..., 0.4205, 0.5296, 0.9253]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 0.1964414119720459 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', 'synthetic', 'csr', '53451', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.761078357696533} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 112, 220, ..., 999796, + 999898, 1000000]), + col_indices=tensor([ 465, 658, 715, ..., 9500, 9653, 9927]), + values=tensor([0.9513, 0.9158, 0.4499, ..., 0.0775, 0.2496, 0.9759]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5799, 0.5098, 0.6156, ..., 0.8166, 0.2331, 0.2979]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 9.761078357696533 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', 'synthetic', 'csr', '57497', '-ss', '10000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.399010181427002} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 104, 198, ..., 999802, + 999905, 1000000]), + col_indices=tensor([ 124, 157, 187, ..., 9539, 9601, 9680]), + values=tensor([0.6532, 0.0603, 0.0418, ..., 0.1935, 0.1125, 0.4778]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5307, 0.8097, 0.3092, ..., 0.4937, 0.1856, 0.7516]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.399010181427002 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 104, 198, ..., 999802, + 999905, 1000000]), + col_indices=tensor([ 124, 157, 187, ..., 9539, 9601, 9680]), + values=tensor([0.6532, 0.0603, 0.0418, ..., 0.1935, 0.1125, 0.4778]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5307, 0.8097, 0.3092, ..., 0.4937, 0.1856, 0.7516]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.399010181427002 seconds + +[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93] +[87.17] +13.951938152313232 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002} +[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93, 18.34, 17.87, 18.21, 18.15, 18.42, 17.87, 18.22, 18.25, 18.03, 17.9] +325.54 +16.277 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002, 'J_1KI': 21.15224183413299, 'W_1KI': 1.5160790997791191, 'W_D': 70.89300000000001, 'J_D': 989.0947514319422, 'W_D_1KI': 1.2329860688383745, 'J_D_1KI': 0.021444354815701245} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..ae32267 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9007, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.744792222976685, "TIME_S_1KI": 1.192937961915919, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1359.414791688919, "W": 84.86, "J_1KI": 150.92869897734195, "W_1KI": 9.421561008104806, "W_D": 68.55725000000001, "J_D": 1098.2528838971855, "W_D_1KI": 7.611552126124127, "J_D_1KI": 0.8450707367740786} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..c9ec716 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1656646728515625} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 506, 991, ..., 4998989, + 4999492, 5000000]), + col_indices=tensor([ 25, 30, 53, ..., 9970, 9993, 9995]), + values=tensor([0.0157, 0.5603, 0.3033, ..., 0.4419, 0.2413, 0.9606]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4291, 0.9468, 0.9558, ..., 0.3375, 0.0455, 0.9666]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 1.1656646728515625 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', 'synthetic', 'csr', '9007', '-ss', '10000', '-sd', '0.05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.744792222976685} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 491, 1013, ..., 4998981, + 4999517, 5000000]), + col_indices=tensor([ 61, 62, 77, ..., 9979, 9982, 9988]), + values=tensor([0.6511, 0.9070, 0.7175, ..., 0.4257, 0.4784, 0.0096]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.7046, 0.2172, 0.5779, ..., 0.4690, 0.0165, 0.6122]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.744792222976685 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 491, 1013, ..., 4998981, + 4999517, 5000000]), + col_indices=tensor([ 61, 62, 77, ..., 9979, 9982, 9988]), + values=tensor([0.6511, 0.9070, 0.7175, ..., 0.4257, 0.4784, 0.0096]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.7046, 0.2172, 0.5779, ..., 0.4690, 0.0165, 0.6122]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.744792222976685 seconds + +[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1] +[84.86] +16.019500255584717 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86} +[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1, 18.25, 17.97, 18.05, 17.84, 17.92, 18.2, 17.96, 17.89, 18.19, 18.51] +326.05499999999995 +16.302749999999996 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86, 'J_1KI': 150.92869897734195, 'W_1KI': 9.421561008104806, 'W_D': 68.55725000000001, 'J_D': 1098.2528838971855, 'W_D_1KI': 7.611552126124127, 'J_D_1KI': 0.8450707367740786} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..3d91d0f --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 279705, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.192691802978516, "TIME_S_1KI": 0.03644086377783206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1002.2923643112182, "W": 73.24, "J_1KI": 3.5833909451429835, "W_1KI": 0.2618473034089487, "W_D": 56.983999999999995, "J_D": 779.8283463668822, "W_D_1KI": 0.20372892869272982, "J_D_1KI": 0.0007283707073263969} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..d0774e1 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05549430847167969} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([3370, 8033, 9994, 2466, 6901, 8760, 7929, 6009, 6694, + 5159, 1569, 4321, 2884, 3593, 7087, 277, 9865, 708, + 219, 1957, 2394, 9752, 9341, 4634, 7106, 8670, 5732, + 279, 8294, 2115, 4730, 6630, 1087, 3467, 99, 364, + 8115, 4267, 1834, 5621, 1569, 7117, 9388, 1669, 5931, + 9264, 3811, 5721, 3727, 135, 5730, 2995, 3406, 9737, + 8203, 4619, 3682, 7347, 200, 8973, 7753, 580, 2253, + 5338, 9810, 8027, 181, 7440, 8883, 5987, 8971, 592, + 4310, 5459, 5555, 5982, 2912, 5657, 5155, 5158, 2575, + 4534, 5426, 285, 2313, 564, 416, 9640, 2595, 4194, + 651, 1798, 5551, 7426, 7258, 3436, 2400, 6562, 5104, + 7010, 536, 2620, 9757, 68, 4487, 1288, 1752, 3582, + 4320, 2874, 3544, 5364, 8870, 570, 876, 9095, 9069, + 7054, 4172, 1984, 9030, 5728, 1404, 5844, 3846, 641, + 8291, 9336, 3061, 3478, 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csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.05549430847167969 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', 'synthetic', 'csr', '189208', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.102759838104248} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 669, 3273, 5792, 9781, 3401, 2980, 8019, 7237, 2538, + 4477, 7482, 497, 1165, 1179, 8217, 7349, 5272, 9560, + 8988, 3708, 8899, 64, 4583, 4111, 7600, 3699, 4957, + 769, 1357, 74, 9202, 6103, 1121, 2235, 1229, 8638, + 4717, 7116, 8280, 5249, 1887, 4346, 1096, 6559, 370, + 5939, 6193, 450, 6742, 1437, 140, 922, 1107, 8788, + 4404, 1605, 3671, 5699, 8839, 9178, 3700, 366, 8176, + 7767, 6692, 8391, 2732, 4473, 1417, 6192, 3118, 3857, + 273, 6678, 6556, 2830, 9847, 1396, 4143, 8999, 1311, + 8607, 1524, 3289, 5756, 8868, 728, 8554, 1884, 115, + 5427, 9570, 4892, 5097, 1696, 9631, 4966, 79, 3458, + 519, 9574, 1822, 5669, 9689, 8411, 558, 8678, 6709, + 5081, 7029, 1222, 8895, 1768, 2808, 3701, 4049, 5985, + 6253, 6668, 8422, 3407, 5174, 7407, 2942, 436, 8501, + 6672, 4879, 4449, 8978, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 7.102759838104248 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', 'synthetic', 'csr', '279705', '-ss', '10000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.192691802978516} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 999, 999, 1000]), + col_indices=tensor([8952, 4000, 8166, 6597, 532, 6376, 6026, 9869, 7520, + 7179, 9261, 3880, 1825, 3183, 7673, 9449, 3683, 5956, + 1175, 9188, 3143, 3107, 7411, 4454, 602, 8234, 1772, + 7128, 697, 2579, 6192, 4803, 5677, 9960, 6436, 8271, + 7262, 970, 7301, 4426, 5443, 1245, 6562, 2078, 17, + 5156, 8485, 7276, 8067, 1486, 267, 1867, 2441, 2368, + 9094, 5268, 7382, 3883, 3736, 9730, 4478, 9182, 3080, + 3707, 1066, 4867, 2125, 6033, 2824, 3938, 8278, 1321, + 9817, 7979, 8727, 7687, 7915, 1214, 440, 5708, 5546, + 1111, 6567, 4866, 6297, 7245, 887, 2038, 4920, 2063, + 7927, 3268, 9646, 7587, 1863, 7946, 3596, 8591, 6781, + 7806, 9483, 1512, 3170, 9606, 4349, 2224, 451, 5245, + 4275, 2218, 1928, 3938, 364, 232, 3259, 3441, 8386, + 7579, 4888, 5900, 1901, 64, 199, 7448, 6195, 3174, + 3236, 8078, 6653, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.192691802978516 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 999, 999, 1000]), + col_indices=tensor([8952, 4000, 8166, 6597, 532, 6376, 6026, 9869, 7520, + 7179, 9261, 3880, 1825, 3183, 7673, 9449, 3683, 5956, + 1175, 9188, 3143, 3107, 7411, 4454, 602, 8234, 1772, + 7128, 697, 2579, 6192, 4803, 5677, 9960, 6436, 8271, + 7262, 970, 7301, 4426, 5443, 1245, 6562, 2078, 17, + 5156, 8485, 7276, 8067, 1486, 267, 1867, 2441, 2368, + 9094, 5268, 7382, 3883, 3736, 9730, 4478, 9182, 3080, + 3707, 1066, 4867, 2125, 6033, 2824, 3938, 8278, 1321, + 9817, 7979, 8727, 7687, 7915, 1214, 440, 5708, 5546, + 1111, 6567, 4866, 6297, 7245, 887, 2038, 4920, 2063, + 7927, 3268, 9646, 7587, 1863, 7946, 3596, 8591, 6781, + 7806, 9483, 1512, 3170, 9606, 4349, 2224, 451, 5245, + 4275, 2218, 1928, 3938, 364, 232, 3259, 3441, 8386, + 7579, 4888, 5900, 1901, 64, 199, 7448, 6195, 3174, + 3236, 8078, 6653, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.192691802978516 seconds + +[18.55, 17.99, 18.16, 18.03, 18.22, 17.84, 17.97, 17.8, 18.22, 18.03] +[73.24] +13.685040473937988 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 279705, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.192691802978516, 'TIME_S_1KI': 0.03644086377783206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1002.2923643112182, 'W': 73.24} +[18.55, 17.99, 18.16, 18.03, 18.22, 17.84, 17.97, 17.8, 18.22, 18.03, 18.98, 18.15, 18.06, 17.9, 17.97, 18.02, 18.2, 18.05, 17.87, 17.78] +325.11999999999995 +16.255999999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 279705, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.192691802978516, 'TIME_S_1KI': 0.03644086377783206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1002.2923643112182, 'W': 73.24, 'J_1KI': 3.5833909451429835, 'W_1KI': 0.2618473034089487, 'W_D': 56.983999999999995, 'J_D': 779.8283463668822, 'W_D_1KI': 0.20372892869272982, 'J_D_1KI': 0.0007283707073263969} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..8eef901 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8355, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.90480637550354, "TIME_S_1KI": 1.305183288510298, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1307.563270778656, "W": 87.44, "J_1KI": 156.5006906976249, "W_1KI": 10.4655894673848, "W_D": 70.932, "J_D": 1060.7053742322921, "W_D_1KI": 8.489766606822261, "J_D_1KI": 1.0161300546765126} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..03135d2 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2567212581634521} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 9, ..., 2499992, + 2499996, 2500000]), + col_indices=tensor([164554, 277712, 289036, ..., 389470, 409865, + 491502]), + values=tensor([0.0126, 0.9348, 0.8595, ..., 0.3584, 0.7345, 0.5238]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0175, 0.7668, 0.4852, ..., 0.2657, 0.5513, 0.9738]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 1.2567212581634521 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', 'synthetic', 'csr', '8355', '-ss', '500000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.90480637550354} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 12, ..., 2499995, + 2499997, 2500000]), + col_indices=tensor([ 72448, 73110, 121261, ..., 13350, 176428, + 278854]), + values=tensor([0.1918, 0.3445, 0.8471, ..., 0.5873, 0.4603, 0.6922]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8369, 0.9252, 0.2721, ..., 0.2352, 0.7861, 0.2173]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.90480637550354 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 12, ..., 2499995, + 2499997, 2500000]), + col_indices=tensor([ 72448, 73110, 121261, ..., 13350, 176428, + 278854]), + values=tensor([0.1918, 0.3445, 0.8471, ..., 0.5873, 0.4603, 0.6922]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8369, 0.9252, 0.2721, ..., 0.2352, 0.7861, 0.2173]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.90480637550354 seconds + +[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78] +[87.44] +14.953834295272827 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44} +[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78, 18.48, 18.09, 18.1, 17.9, 18.11, 18.02, 17.99, 17.84, 18.15, 18.16] +330.15999999999997 +16.508 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44, 'J_1KI': 156.5006906976249, 'W_1KI': 10.4655894673848, 'W_D': 70.932, 'J_D': 1060.7053742322921, 'W_D_1KI': 8.489766606822261, 'J_D_1KI': 1.0161300546765126} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..035895d --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 77922, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570462703704834, "TIME_S_1KI": 0.13565440701861906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.7638923931122, "W": 83.24, "J_1KI": 15.525318811030418, "W_1KI": 1.0682477349144015, "W_D": 66.53899999999999, "J_D": 967.0408413736818, "W_D_1KI": 0.8539180205846871, "J_D_1KI": 0.010958625556129042} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..70de4f5 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.14919304847717285} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 9, ..., 249989, 249995, + 250000]), + col_indices=tensor([ 8787, 10800, 12548, ..., 22776, 32520, 35593]), + values=tensor([0.0395, 0.0216, 0.0459, ..., 0.9233, 0.0886, 0.1442]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0084, 0.2765, 0.2672, ..., 0.0856, 0.1416, 0.8826]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.14919304847717285 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', 'synthetic', 'csr', '70378', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.483437538146973} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 9, ..., 249988, 249997, + 250000]), + col_indices=tensor([ 1665, 9567, 9654, ..., 4112, 18670, 38091]), + values=tensor([0.4890, 0.0494, 0.7903, ..., 0.9513, 0.0590, 0.1377]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5003, 0.9747, 0.2176, ..., 0.9666, 0.4758, 0.9002]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 9.483437538146973 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', 'synthetic', 'csr', '77922', '-ss', '50000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570462703704834} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 7, ..., 249995, 249999, + 250000]), + col_indices=tensor([18420, 40988, 3727, ..., 33621, 36384, 44487]), + values=tensor([0.1861, 0.8144, 0.1628, ..., 0.4774, 0.5715, 0.3216]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.6272, 0.7644, 0.0884, ..., 0.9496, 0.3089, 0.8679]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.570462703704834 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 7, ..., 249995, 249999, + 250000]), + col_indices=tensor([18420, 40988, 3727, ..., 33621, 36384, 44487]), + values=tensor([0.1861, 0.8144, 0.1628, ..., 0.4774, 0.5715, 0.3216]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.6272, 0.7644, 0.0884, ..., 0.9496, 0.3089, 0.8679]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.570462703704834 seconds + +[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89] +[83.24] +14.533444166183472 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24} +[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89, 18.2, 17.85, 17.83, 21.57, 17.96, 18.06, 18.27, 18.3, 18.38, 18.04] +334.02 +16.701 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24, 'J_1KI': 15.525318811030418, 'W_1KI': 1.0682477349144015, 'W_D': 66.53899999999999, 'J_D': 967.0408413736818, 'W_D_1KI': 0.8539180205846871, 'J_D_1KI': 0.010958625556129042} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..f58be3b --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.690638303756714, "TIME_S_1KI": 0.6159266177194627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2395059108735, "W": 87.56, "J_1KI": 74.62346637730447, "W_1KI": 5.044650573255747, "W_D": 71.326, "J_D": 1055.0965394997595, "W_D_1KI": 4.109350694244396, "J_D_1KI": 0.23675466349279234} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..ec20a71 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6049323081970215} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 46, 103, ..., 2499893, + 2499950, 2500000]), + col_indices=tensor([ 214, 217, 3424, ..., 47339, 47927, 48505]), + values=tensor([0.8463, 0.5755, 0.1058, ..., 0.4565, 0.0843, 0.4040]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.2070, 0.0126, 0.4112, ..., 0.3463, 0.8132, 0.3234]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 0.6049323081970215 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', 'synthetic', 'csr', '17357', '-ss', '50000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.690638303756714} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 47, 101, ..., 2499901, + 2499949, 2500000]), + col_indices=tensor([ 511, 725, 819, ..., 47217, 48788, 49222]), + values=tensor([0.0511, 0.3894, 0.2647, ..., 0.8233, 0.9615, 0.4045]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7118, 0.9063, 0.7110, ..., 0.7333, 0.4959, 0.7807]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.690638303756714 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 47, 101, ..., 2499901, + 2499949, 2500000]), + col_indices=tensor([ 511, 725, 819, ..., 47217, 48788, 49222]), + values=tensor([0.0511, 0.3894, 0.2647, ..., 0.8233, 0.9615, 0.4045]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7118, 0.9063, 0.7110, ..., 0.7333, 0.4959, 0.7807]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.690638303756714 seconds + +[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88] +[87.56] +14.792593717575073 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56} +[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88, 18.2, 18.34, 18.01, 17.89, 18.13, 18.46, 18.22, 17.9, 17.95, 18.15] +324.68 +16.234 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56, 'J_1KI': 74.62346637730447, 'W_1KI': 5.044650573255747, 'W_D': 71.326, 'J_D': 1055.0965394997595, 'W_D_1KI': 4.109350694244396, 'J_D_1KI': 0.23675466349279234} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..3c6e8be --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 112508, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.720516443252563, "TIME_S_1KI": 0.09528670355221462, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1090.2722121667862, "W": 75.82, "J_1KI": 9.69061944187779, "W_1KI": 0.6739076332349699, "W_D": 59.38549999999999, "J_D": 853.9483046113252, "W_D_1KI": 0.5278335762790201, "J_D_1KI": 0.004691520392141182} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..b66333e --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.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', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11333847045898438} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 24996, 24999, 25000]), + col_indices=tensor([ 9502, 18497, 7204, ..., 33396, 45910, 109]), + values=tensor([0.5325, 0.6011, 0.4727, ..., 0.6967, 0.0269, 0.7415]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7210, 0.8240, 0.5786, ..., 0.5702, 0.4441, 0.2533]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.11333847045898438 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', 'synthetic', 'csr', '92642', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.645956993103027} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([35285, 1305, 12700, ..., 6399, 17561, 45264]), + values=tensor([0.6896, 0.7157, 0.5414, ..., 0.3157, 0.2585, 0.8046]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8892, 0.5178, 0.0901, ..., 0.0600, 0.1718, 0.0275]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 8.645956993103027 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', 'synthetic', 'csr', '112508', '-ss', '50000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.720516443252563} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([27684, 39939, 2715, ..., 47308, 11944, 42221]), + values=tensor([0.6561, 0.5911, 0.5622, ..., 0.2806, 0.4491, 0.6100]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8957, 0.2813, 0.1993, ..., 0.7019, 0.9944, 0.8970]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.720516443252563 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([27684, 39939, 2715, ..., 47308, 11944, 42221]), + values=tensor([0.6561, 0.5911, 0.5622, ..., 0.2806, 0.4491, 0.6100]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8957, 0.2813, 0.1993, ..., 0.7019, 0.9944, 0.8970]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.720516443252563 seconds + +[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04] +[75.82] +14.379744291305542 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82} +[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04, 18.62, 18.47, 18.04, 18.06, 17.8, 18.08, 18.08, 18.67, 17.82, 18.04] +328.69000000000005 +16.434500000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82, 'J_1KI': 9.69061944187779, 'W_1KI': 0.6739076332349699, 'W_D': 59.38549999999999, 'J_D': 853.9483046113252, 'W_D_1KI': 0.5278335762790201, 'J_D_1KI': 0.004691520392141182} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..1a2b755 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 234425, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618, "TIME_S_1KI": 0.09091280843322434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2054.109435153008, "W": 83.45000000000002, "J_1KI": 8.762330959381499, "W_1KI": 0.3559773914898156, "W_D": 67.21450000000002, "J_D": 1654.4749985511307, "W_D_1KI": 0.2867206995840888, "J_D_1KI": 0.0012230807276702091} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..06a3a12 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.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', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10643196105957031} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 9, ..., 89992, 89998, 90000]), + col_indices=tensor([ 7924, 12206, 12582, ..., 21107, 10373, 19571]), + values=tensor([0.8274, 0.6462, 0.9289, ..., 0.2542, 0.4328, 0.6143]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.4141, 0.4229, 0.5665, ..., 0.1440, 0.7095, 0.1472]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 0.10643196105957031 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', 'synthetic', 'csr', '197309', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 17.675063133239746} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 9, ..., 89992, 89999, 90000]), + col_indices=tensor([ 929, 2315, 11088, ..., 21381, 23338, 19838]), + values=tensor([0.3872, 0.2873, 0.0227, ..., 0.4746, 0.4839, 0.3522]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.1013, 0.5431, 0.3309, ..., 0.2751, 0.1147, 0.0007]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 17.675063133239746 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', 'synthetic', 'csr', '234425', '-ss', '30000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 10, ..., 89994, 89997, 90000]), + col_indices=tensor([ 6200, 14122, 21980, ..., 11781, 19689, 21155]), + values=tensor([0.5859, 0.7824, 0.3581, ..., 0.7747, 0.1479, 0.5181]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.3266, 0.0767, 0.6789, ..., 0.9087, 0.9799, 0.8849]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 21.312235116958618 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 10, ..., 89994, 89997, 90000]), + col_indices=tensor([ 6200, 14122, 21980, ..., 11781, 19689, 21155]), + values=tensor([0.5859, 0.7824, 0.3581, ..., 0.7747, 0.1479, 0.5181]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.3266, 0.0767, 0.6789, ..., 0.9087, 0.9799, 0.8849]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 21.312235116958618 seconds + +[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81] +[83.45] +24.61485242843628 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002} +[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81, 18.28, 18.06, 18.16, 17.86, 18.06, 17.97, 18.34, 17.89, 18.12, 18.1] +324.71 +16.2355 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002, 'J_1KI': 8.762330959381499, 'W_1KI': 0.3559773914898156, 'W_D': 67.21450000000002, 'J_D': 1654.4749985511307, 'W_D_1KI': 0.2867206995840888, 'J_D_1KI': 0.0012230807276702091} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..355226c --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,77 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2140212059020996} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 34, 62, ..., 899934, 899967, + 900000]), + col_indices=tensor([ 1559, 1711, 3295, ..., 29804, 29893, 29964]), + values=tensor([0.7225, 0.7366, 0.0675, ..., 0.3495, 0.2204, 0.5611]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.1783, 0.4759, 0.5239, ..., 0.8363, 0.1566, 0.5506]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 0.2140212059020996 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', 'synthetic', 'csr', '98121', '-ss', '30000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 19.3143093585968} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 29, 64, ..., 899940, 899966, + 900000]), + col_indices=tensor([ 612, 701, 1017, ..., 29770, 29777, 29834]), + values=tensor([0.4034, 0.5977, 0.8788, ..., 0.6466, 0.3405, 0.9207]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.7678, 0.0123, 0.5496, ..., 0.4589, 0.2646, 0.8857]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 19.3143093585968 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', 'synthetic', 'csr', '106684', '-ss', '30000', '-sd', '0.001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.90600872039795} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 21, 51, ..., 899936, 899963, + 900000]), + col_indices=tensor([ 855, 2329, 2453, ..., 28070, 28293, 29379]), + values=tensor([0.2478, 0.6443, 0.3087, ..., 0.2033, 0.4619, 0.6203]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.3161, 0.0015, 0.4480, ..., 0.6517, 0.7843, 0.6370]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.90600872039795 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 21, 51, ..., 899936, 899963, + 900000]), + col_indices=tensor([ 855, 2329, 2453, ..., 28070, 28293, 29379]), + values=tensor([0.2478, 0.6443, 0.3087, ..., 0.2033, 0.4619, 0.6203]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.3161, 0.0015, 0.4480, ..., 0.6517, 0.7843, 0.6370]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.90600872039795 seconds + diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..21aeca6 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 303288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195, "TIME_S_1KI": 0.06950319478375404, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1922.1091361045835, "W": 78.6, "J_1KI": 6.337570679039671, "W_1KI": 0.2591596106670887, "W_D": 62.18274999999999, "J_D": 1520.636537953019, "W_D_1KI": 0.20502871857772148, "J_D_1KI": 0.0006760198839971298} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..d8910fb --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.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', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08539462089538574} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 9000, 9000, 9000]), + col_indices=tensor([ 8168, 26166, 15021, ..., 3965, 14348, 3180]), + values=tensor([0.0414, 0.9204, 0.6909, ..., 0.5705, 0.2524, 0.4947]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.9721, 0.7014, 0.8881, ..., 0.4193, 0.5170, 0.9013]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.08539462089538574 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', 'synthetic', 'csr', '245917', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 17.02755308151245} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9000, 9000, 9000]), + col_indices=tensor([ 9352, 11930, 17471, ..., 19597, 20552, 1111]), + values=tensor([0.4298, 0.4908, 0.5157, ..., 0.6454, 0.4570, 0.2738]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3622, 0.2189, 0.3857, ..., 0.2935, 0.6447, 0.7890]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 17.02755308151245 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', 'synthetic', 'csr', '303288', '-ss', '30000', '-sd', '1e-05', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 8999, 9000, 9000]), + col_indices=tensor([20060, 11216, 16521, ..., 22127, 15786, 9820]), + values=tensor([0.3604, 0.7216, 0.9721, ..., 0.8443, 0.2707, 0.5761]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.4467, 0.9917, 0.2567, ..., 0.4911, 0.0150, 0.9779]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.079484939575195 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 8999, 9000, 9000]), + col_indices=tensor([20060, 11216, 16521, ..., 22127, 15786, 9820]), + values=tensor([0.3604, 0.7216, 0.9721, ..., 0.8443, 0.2707, 0.5761]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.4467, 0.9917, 0.2567, ..., 0.4911, 0.0150, 0.9779]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.079484939575195 seconds + +[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85] +[78.6] +24.454314708709717 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6} +[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85, 18.57, 17.96, 18.0, 18.17, 18.14, 18.05, 18.17, 20.58, 18.66, 18.18] +328.345 +16.417250000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6, 'J_1KI': 6.337570679039671, 'W_1KI': 0.2591596106670887, 'W_D': 62.18274999999999, 'J_D': 1520.636537953019, 'W_D_1KI': 0.20502871857772148, 'J_D_1KI': 0.0006760198839971298} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..27d9946 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.691206455230713, "TIME_S_1KI": 24.691206455230713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 647.4072245025635, "W": 22.90104020202067, "J_1KI": 647.4072245025635, "W_1KI": 22.90104020202067, "W_D": 3.140040202020675, "J_D": 88.76822598814977, "W_D_1KI": 3.140040202020675, "J_D_1KI": 3.140040202020675} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..d5446f7 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.691206455230713} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 21, ..., 999980, + 999990, 1000000]), + col_indices=tensor([ 5106, 13656, 15471, ..., 68202, 79637, 95576]), + values=tensor([0.9862, 0.5796, 0.7870, ..., 0.3201, 0.7080, 0.2748]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6105, 0.8083, 0.7150, ..., 0.7011, 0.0810, 0.6416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 24.691206455230713 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 21, ..., 999980, + 999990, 1000000]), + col_indices=tensor([ 5106, 13656, 15471, ..., 68202, 79637, 95576]), + values=tensor([0.9862, 0.5796, 0.7870, ..., 0.3201, 0.7080, 0.2748]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6105, 0.8083, 0.7150, ..., 0.7011, 0.0810, 0.6416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 24.691206455230713 seconds + +[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36] +[20.64, 20.84, 21.24, 24.56, 26.2, 27.16, 28.16, 26.08, 25.68, 24.72, 24.72, 24.48, 24.6, 24.6, 24.72, 24.68, 24.6, 24.52, 24.52, 24.8, 24.72, 24.6, 24.48, 24.48, 24.52, 24.44, 24.64] +28.269773721694946 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067} +[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36, 20.6, 20.56, 20.64, 22.68, 24.64, 25.4, 25.4, 25.36, 24.48, 22.68] +395.2199999999999 +19.760999999999996 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067, 'J_1KI': 647.4072245025635, 'W_1KI': 22.90104020202067, 'W_D': 3.140040202020675, 'J_D': 88.76822598814977, 'W_D_1KI': 3.140040202020675, 'J_D_1KI': 3.140040202020675} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..75a4f33 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.487157583236694, "TIME_S_1KI": 3.3082516035446985, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.83638416290285, "W": 22.27932516765204, "J_1KI": 102.78750289050564, "W_1KI": 7.028178286325565, "W_D": 3.710325167652041, "J_D": 54.263714344978354, "W_D_1KI": 1.170449579700959, "J_D_1KI": 0.36922699675109116} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..2086467 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.3119447231292725} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 99999, 99999, + 100000]), + col_indices=tensor([34080, 20424, 38945, ..., 64155, 47978, 44736]), + values=tensor([0.5824, 0.7466, 0.8758, ..., 0.8278, 0.8938, 0.7712]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.9015, 0.6308, 0.7799, ..., 0.6045, 0.4908, 0.8218]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 3.3119447231292725 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 synthetic csr 3170 -ss 100000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.487157583236694} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 99997, 99999, + 100000]), + col_indices=tensor([62540, 50524, 43651, ..., 12394, 59846, 74659]), + values=tensor([0.6601, 0.8101, 0.4564, ..., 0.4320, 0.9061, 0.8749]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2783, 0.8812, 0.6091, ..., 0.5557, 0.0745, 0.6879]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.487157583236694 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 99997, 99999, + 100000]), + col_indices=tensor([62540, 50524, 43651, ..., 12394, 59846, 74659]), + values=tensor([0.6601, 0.8101, 0.4564, ..., 0.4320, 0.9061, 0.8749]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2783, 0.8812, 0.6091, ..., 0.5557, 0.0745, 0.6879]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.487157583236694 seconds + +[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32] +[20.32, 20.36, 20.48, 22.0, 23.24, 25.44, 26.04, 26.48, 26.08, 24.6, 24.44, 24.44, 24.4, 24.6] +14.625056266784668 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204} +[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32, 20.04, 20.16, 20.24, 20.6, 20.72, 20.72, 20.88, 20.72, 21.08, 21.0] +371.38 +18.569 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204, 'J_1KI': 102.78750289050564, 'W_1KI': 7.028178286325565, 'W_D': 3.710325167652041, 'J_D': 54.263714344978354, 'W_D_1KI': 1.170449579700959, 'J_D_1KI': 0.36922699675109116} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..25fcf70 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.42804479598999, "TIME_S_1KI": 0.32415432999658034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 291.8528582954407, "W": 21.456480473872652, "J_1KI": 9.07220572879828, "W_1KI": 0.666971727506144, "W_D": 3.1474804738726547, "J_D": 42.81229504752161, "W_D_1KI": 0.09783899514680307, "J_D_1KI": 0.0030413116303016183} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..034e4a3 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.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 synthetic csr 1000 -ss 10000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3263826370239258} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 10000, 10000, 10000]), + col_indices=tensor([1982, 558, 3662, ..., 629, 5634, 6549]), + values=tensor([0.5250, 0.9307, 0.0448, ..., 0.0150, 0.4421, 0.4831]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.5546, 0.0630, 0.8785, ..., 0.4779, 0.8090, 0.6189]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.3263826370239258 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 synthetic csr 32170 -ss 10000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.42804479598999} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 9996, 9999, 10000]), + col_indices=tensor([8155, 9480, 4094, ..., 6796, 6921, 3902]), + values=tensor([0.0915, 0.3699, 0.5728, ..., 0.9057, 0.8661, 0.7356]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3327, 0.6532, 0.3155, ..., 0.1421, 0.0155, 0.6755]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.42804479598999 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 9996, 9999, 10000]), + col_indices=tensor([8155, 9480, 4094, ..., 6796, 6921, 3902]), + values=tensor([0.0915, 0.3699, 0.5728, ..., 0.9057, 0.8661, 0.7356]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3327, 0.6532, 0.3155, ..., 0.1421, 0.0155, 0.6755]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.42804479598999 seconds + +[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36] +[20.36, 20.24, 20.48, 22.52, 23.04, 24.76, 25.6, 25.52, 24.28, 23.12, 23.12, 23.16, 23.44] +13.602084398269653 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652} +[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36, 20.52, 20.48, 20.64, 20.48, 20.48, 20.32, 20.48, 20.36, 20.32, 20.2] +366.17999999999995 +18.308999999999997 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652, 'J_1KI': 9.07220572879828, 'W_1KI': 0.666971727506144, 'W_D': 3.1474804738726547, 'J_D': 42.81229504752161, 'W_D_1KI': 0.09783899514680307, 'J_D_1KI': 0.0030413116303016183} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..6de6107 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4747, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.586360931396484, "TIME_S_1KI": 2.2301160588574858, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.0044006347656, "W": 22.3162197944069, "J_1KI": 68.67587963656321, "W_1KI": 4.701120664505352, "W_D": 3.9862197944068996, "J_D": 58.23231742858882, "W_D_1KI": 0.8397345258914893, "J_D_1KI": 0.17689794099251935} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..0876b19 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2116076946258545} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 19, ..., 99984, 99990, + 100000]), + col_indices=tensor([ 365, 990, 1421, ..., 6204, 7506, 8345]), + values=tensor([0.4012, 0.2163, 0.0214, ..., 0.4427, 0.7190, 0.8381]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6373, 0.6560, 0.2779, ..., 0.6662, 0.5919, 0.8676]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 2.2116076946258545 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 synthetic csr 4747 -ss 10000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.586360931396484} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 23, ..., 99976, 99989, + 100000]), + col_indices=tensor([ 145, 447, 695, ..., 7955, 8009, 9128]), + values=tensor([0.3182, 0.0478, 0.7097, ..., 0.3986, 0.2793, 0.7202]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.3264, 0.5290, 0.7390, ..., 0.4961, 0.6761, 0.4965]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.586360931396484 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 23, ..., 99976, 99989, + 100000]), + col_indices=tensor([ 145, 447, 695, ..., 7955, 8009, 9128]), + values=tensor([0.3182, 0.0478, 0.7097, ..., 0.3986, 0.2793, 0.7202]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.3264, 0.5290, 0.7390, ..., 0.4961, 0.6761, 0.4965]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.586360931396484 seconds + +[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6] +[20.44, 20.44, 20.44, 21.8, 24.32, 26.12, 27.12, 27.16, 25.36, 24.28, 24.24, 24.12, 23.96, 23.84] +14.608406066894531 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069} +[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6, 20.28, 20.08, 20.4, 20.32, 20.2, 20.36, 20.36, 20.4, 20.28, 20.48] +366.6 +18.330000000000002 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069, 'J_1KI': 68.67587963656321, 'W_1KI': 4.701120664505352, 'W_D': 3.9862197944068996, 'J_D': 58.23231742858882, 'W_D_1KI': 0.8397345258914893, 'J_D_1KI': 0.17689794099251935} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..9af62e4 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.214847326278687, "TIME_S_1KI": 21.214847326278687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 593.507265138626, "W": 22.813207511083125, "J_1KI": 593.507265138626, "W_1KI": 22.813207511083125, "W_D": 4.622207511083129, "J_D": 120.25111933398253, "W_D_1KI": 4.622207511083129, "J_D_1KI": 4.622207511083129} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..a8afaf9 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.214847326278687} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 111, 190, ..., 999805, + 999902, 1000000]), + col_indices=tensor([ 3, 255, 407, ..., 9480, 9499, 9966]), + values=tensor([0.6179, 0.1045, 0.6429, ..., 0.5216, 0.7550, 0.7148]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6572, 0.8503, 0.2699, ..., 0.6176, 0.8577, 0.2518]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.214847326278687 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 111, 190, ..., 999805, + 999902, 1000000]), + col_indices=tensor([ 3, 255, 407, ..., 9480, 9499, 9966]), + values=tensor([0.6179, 0.1045, 0.6429, ..., 0.5216, 0.7550, 0.7148]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6572, 0.8503, 0.2699, ..., 0.6176, 0.8577, 0.2518]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.214847326278687 seconds + +[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08] +[20.04, 20.08, 23.16, 25.4, 27.72, 28.8, 28.8, 29.48, 25.64, 25.4, 24.0, 23.96, 23.64, 23.72, 23.92, 24.04, 24.32, 24.36, 24.04, 24.0, 23.84, 24.08, 24.28, 24.28, 24.28] +26.015949964523315 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125} +[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08, 19.76, 19.76, 19.96, 20.28, 20.4, 20.36, 20.4, 20.0, 19.92, 20.2] +363.81999999999994 +18.190999999999995 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125, 'J_1KI': 593.507265138626, 'W_1KI': 22.813207511083125, 'W_D': 4.622207511083129, 'J_D': 120.25111933398253, 'W_D_1KI': 4.622207511083129, 'J_D_1KI': 4.622207511083129} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..4f5ecb3 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.68757820129395, "TIME_S_1KI": 106.68757820129395, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2614.243714923859, "W": 23.06903562044379, "J_1KI": 2614.243714923859, "W_1KI": 23.06903562044379, "W_D": 4.456035620443789, "J_D": 504.9696617529395, "W_D_1KI": 4.456035620443789, "J_D_1KI": 4.456035620443789} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..4e8e799 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.68757820129395} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 529, 1008, ..., 4999026, + 4999478, 5000000]), + col_indices=tensor([ 75, 122, 128, ..., 9908, 9909, 9916]), + values=tensor([0.8571, 0.2596, 0.0411, ..., 0.7048, 0.9398, 0.3732]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2354, 0.1436, 0.6485, ..., 0.5167, 0.9065, 0.2719]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 106.68757820129395 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 529, 1008, ..., 4999026, + 4999478, 5000000]), + col_indices=tensor([ 75, 122, 128, ..., 9908, 9909, 9916]), + values=tensor([0.8571, 0.2596, 0.0411, ..., 0.7048, 0.9398, 0.3732]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2354, 0.1436, 0.6485, ..., 0.5167, 0.9065, 0.2719]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 106.68757820129395 seconds + +[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56] +[20.56, 20.64, 20.92, 22.0, 23.8, 25.56, 26.6, 26.56, 26.4, 25.36, 24.52, 24.6, 24.56, 24.6, 24.32, 24.4, 24.4, 24.32, 24.24, 24.44, 24.6, 24.36, 24.32, 24.36, 24.48, 24.6, 24.76, 24.72, 24.64, 24.6, 24.48, 24.56, 24.64, 24.64, 24.44, 24.48, 24.36, 24.16, 24.16, 24.24, 24.28, 24.16, 24.2, 24.36, 24.44, 24.44, 24.32, 24.0, 24.0, 24.0, 24.28, 24.44, 24.56, 24.48, 24.48, 24.32, 24.52, 24.52, 24.36, 24.4, 24.4, 24.32, 24.36, 24.32, 24.68, 24.72, 24.6, 24.6, 24.64, 24.6, 24.72, 24.64, 24.64, 24.68, 24.68, 24.52, 24.4, 24.32, 24.2, 24.16, 24.24, 24.2, 24.2, 24.4, 24.52, 24.56, 24.8, 24.8, 24.56, 24.44, 24.4, 23.84, 23.76, 23.88, 24.0, 24.0, 24.16, 24.2, 24.36, 24.2, 24.16, 24.2, 24.24, 24.16, 24.16, 24.4, 24.32, 24.56] +113.32262682914734 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379} +[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56, 20.4, 20.52, 20.72, 20.84, 20.96, 20.84, 20.8, 20.52, 20.64, 20.56] +372.26 +18.613 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379, 'J_1KI': 2614.243714923859, 'W_1KI': 23.06903562044379, 'W_D': 4.456035620443789, 'J_D': 504.9696617529395, 'W_D_1KI': 4.456035620443789, 'J_D_1KI': 4.456035620443789} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..69bee1d --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 145400, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.376285076141357, "TIME_S_1KI": 0.07136372129395707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.9616888427734, "W": 22.159348523127505, "J_1KI": 2.2349497169379187, "W_1KI": 0.15240267209853856, "W_D": 3.711348523127505, "J_D": 54.42606233215331, "W_D_1KI": 0.02552509300637899, "J_D_1KI": 0.00017555084598610036} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..d251499 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08243966102600098} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 252, 7839, 5648, 3465, 7349, 4902, 9434, 7529, 7692, + 165, 3611, 104, 550, 6486, 7084, 9069, 7958, 6919, + 690, 9278, 3067, 6601, 7528, 1640, 3373, 4102, 2924, + 2640, 1739, 407, 8622, 7009, 7252, 6788, 1851, 3757, + 6304, 8203, 5332, 7635, 594, 3806, 4878, 4044, 1441, + 999, 1148, 5958, 9975, 4945, 2434, 1204, 59, 181, + 7425, 800, 8678, 5796, 5760, 120, 6846, 442, 3920, + 1463, 5374, 6614, 1071, 5654, 6755, 4329, 2096, 3557, + 3459, 2406, 5557, 9403, 8210, 6660, 740, 4513, 3423, + 2395, 8647, 3341, 136, 1978, 4301, 975, 3977, 9483, + 1644, 1238, 3590, 4407, 378, 953, 4885, 3832, 7590, + 727, 9280, 2092, 6016, 2681, 4198, 2877, 6915, 4242, + 6915, 8581, 5016, 2122, 9650, 9146, 4295, 9411, 1035, + 3607, 4089, 1201, 5045, 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0.9071, ..., 0.3290, 0.2447, 0.6100]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.08243966102600098 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 synthetic csr 127365 -ss 10000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.197555303573608} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([8951, 5667, 956, 7950, 5201, 1331, 1765, 3006, 5900, + 2081, 5366, 8255, 7412, 6448, 5104, 6260, 9166, 2113, + 8971, 6174, 6836, 879, 9072, 156, 6527, 5601, 2012, + 6002, 4221, 7765, 3990, 7258, 2865, 1967, 7820, 9862, + 418, 17, 3074, 2165, 8428, 6171, 6497, 2053, 5484, + 4943, 9733, 4335, 9186, 435, 7561, 757, 7593, 4461, + 1964, 3289, 5697, 8181, 6697, 6346, 2540, 5038, 6182, + 7579, 9304, 3023, 5138, 7682, 8029, 1723, 4898, 3727, + 6168, 1394, 4633, 3134, 3220, 8290, 4361, 8659, 8749, + 6471, 4502, 765, 2454, 7851, 4423, 6861, 3263, 4149, + 6309, 6921, 8089, 1483, 3889, 3348, 1563, 5080, 5924, + 9985, 5924, 9061, 9701, 1918, 9154, 1454, 7379, 1012, + 5960, 5244, 7249, 1042, 5782, 1289, 7395, 9762, 5609, + 6097, 7610, 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0.6681, 0.9732, 0.3908]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 9.197555303573608 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 synthetic csr 145400 -ss 10000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.376285076141357} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([2248, 4486, 2578, 3740, 621, 6003, 5803, 7740, 8017, + 8357, 2886, 8788, 9848, 2845, 3345, 9526, 6879, 849, + 9475, 1600, 5380, 5334, 6629, 9937, 1676, 3949, 9759, + 1297, 1271, 554, 8126, 7607, 6824, 8955, 3784, 6636, + 6716, 7362, 236, 4770, 377, 1035, 7094, 4817, 9140, + 2937, 60, 7489, 6793, 9918, 3932, 6069, 5062, 5030, + 1223, 3975, 150, 7966, 1822, 242, 7431, 4532, 9014, + 8126, 915, 7358, 2001, 3806, 564, 5560, 6173, 620, + 8900, 1133, 6344, 486, 265, 5173, 6593, 9511, 1972, + 6657, 9996, 3207, 27, 7301, 9620, 504, 7560, 1601, + 7424, 6685, 9645, 8602, 1386, 2669, 7610, 3723, 4006, + 2340, 4530, 2647, 5701, 4426, 8272, 3355, 7800, 1132, + 6460, 5948, 6002, 5599, 7637, 1754, 3726, 7844, 4922, + 6626, 3071, 5112, 9488, 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0.6492]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.376285076141357 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([2248, 4486, 2578, 3740, 621, 6003, 5803, 7740, 8017, + 8357, 2886, 8788, 9848, 2845, 3345, 9526, 6879, 849, + 9475, 1600, 5380, 5334, 6629, 9937, 1676, 3949, 9759, + 1297, 1271, 554, 8126, 7607, 6824, 8955, 3784, 6636, + 6716, 7362, 236, 4770, 377, 1035, 7094, 4817, 9140, + 2937, 60, 7489, 6793, 9918, 3932, 6069, 5062, 5030, + 1223, 3975, 150, 7966, 1822, 242, 7431, 4532, 9014, + 8126, 915, 7358, 2001, 3806, 564, 5560, 6173, 620, + 8900, 1133, 6344, 486, 265, 5173, 6593, 9511, 1972, + 6657, 9996, 3207, 27, 7301, 9620, 504, 7560, 1601, + 7424, 6685, 9645, 8602, 1386, 2669, 7610, 3723, 4006, + 2340, 4530, 2647, 5701, 4426, 8272, 3355, 7800, 1132, + 6460, 5948, 6002, 5599, 7637, 1754, 3726, 7844, 4922, + 6626, 3071, 5112, 9488, 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0.6492]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.376285076141357 seconds + +[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36] +[21.4, 21.52, 21.84, 22.84, 24.24, 24.88, 25.24, 25.24, 24.84, 24.56, 23.72, 23.8, 23.96, 23.68] +14.664767265319824 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505} +[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36, 20.68, 20.64, 20.6, 20.52, 20.32, 20.28, 20.32, 20.32, 20.6, 20.68] +368.96000000000004 +18.448 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505, 'J_1KI': 2.2349497169379187, 'W_1KI': 0.15240267209853856, 'W_D': 3.711348523127505, 'J_D': 54.42606233215331, 'W_D_1KI': 0.02552509300637899, 'J_D_1KI': 0.00017555084598610036} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..c0e7e6f --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 98.37349367141724, "TIME_S_1KI": 98.37349367141724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2530.5661976623533, "W": 23.87864246176097, "J_1KI": 2530.5661976623533, "W_1KI": 23.87864246176097, "W_D": 5.392642461760968, "J_D": 571.491396617889, "W_D_1KI": 5.392642461760968, "J_D_1KI": 5.392642461760968} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..3a0a0f2 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 98.37349367141724} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 6, ..., 2499994, + 2499997, 2500000]), + col_indices=tensor([ 13104, 56490, 58201, ..., 30329, 136735, + 267614]), + values=tensor([0.2415, 0.0022, 0.5702, ..., 0.5534, 0.4567, 0.6374]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.5993, 0.2850, 0.9957, ..., 0.8791, 0.8991, 0.2848]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 98.37349367141724 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 6, ..., 2499994, + 2499997, 2500000]), + col_indices=tensor([ 13104, 56490, 58201, ..., 30329, 136735, + 267614]), + values=tensor([0.2415, 0.0022, 0.5702, ..., 0.5534, 0.4567, 0.6374]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.5993, 0.2850, 0.9957, ..., 0.8791, 0.8991, 0.2848]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 98.37349367141724 seconds + +[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52] +[20.56, 20.56, 20.56, 21.88, 23.2, 25.36, 26.72, 27.0, 26.4, 25.72, 25.04, 25.04, 25.2, 25.32, 25.2, 25.0, 25.12, 24.96, 24.88, 25.0, 24.92, 24.92, 24.96, 25.08, 25.24, 25.28, 25.48, 25.28, 25.28, 25.44, 25.44, 25.4, 25.2, 25.12, 24.96, 25.12, 25.32, 25.52, 25.8, 25.72, 25.44, 25.08, 25.0, 25.0, 24.96, 25.0, 25.04, 25.12, 25.12, 25.2, 25.2, 25.16, 25.04, 24.88, 24.96, 25.16, 25.16, 25.24, 25.24, 25.4, 25.2, 25.32, 25.16, 25.16, 25.2, 25.2, 25.0, 25.16, 25.28, 25.28, 25.28, 25.16, 25.2, 25.2, 25.04, 25.2, 25.36, 25.32, 25.32, 25.52, 25.44, 25.4, 25.36, 25.4, 25.32, 25.24, 25.04, 25.04, 24.92, 24.96, 24.96, 25.12, 25.32, 25.24, 25.2, 25.04, 24.92, 25.08, 25.0, 24.96, 24.88] +105.97613334655762 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097} +[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52, 20.68, 20.4, 20.36, 20.52, 20.4, 20.6, 20.68, 20.68, 20.64, 20.76] +369.72 +18.486 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097, 'J_1KI': 2530.5661976623533, 'W_1KI': 23.87864246176097, 'W_D': 5.392642461760968, 'J_D': 571.491396617889, 'W_D_1KI': 5.392642461760968, 'J_D_1KI': 5.392642461760968} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..05b637f --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1803, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.916261672973633, "TIME_S_1KI": 6.054498986674227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.36130602836613, "W": 23.871230542861202, "J_1KI": 193.21203883991467, "W_1KI": 13.239728531814311, "W_D": 5.286230542861201, "J_D": 77.14383104681971, "W_D_1KI": 2.931908232313478, "J_D_1KI": 1.6261276940174587} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..a1e0ae8 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.821210145950317} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 11, ..., 249996, 249998, + 250000]), + col_indices=tensor([12413, 12946, 15415, ..., 25881, 14227, 42249]), + values=tensor([0.3226, 0.4714, 0.3498, ..., 0.9478, 0.5271, 0.1593]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.8728, 0.8759, 0.3915, ..., 0.5486, 0.7678, 0.2723]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 5.821210145950317 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 synthetic csr 1803 -ss 50000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.916261672973633} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 11, ..., 249988, 249991, + 250000]), + col_indices=tensor([ 7415, 12339, 19287, ..., 32647, 33814, 45500]), + values=tensor([0.8370, 0.0969, 0.8316, ..., 0.1944, 0.4025, 0.6344]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.2154, 0.6825, 0.0342, ..., 0.6227, 0.4225, 0.9397]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.916261672973633 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 11, ..., 249988, 249991, + 250000]), + col_indices=tensor([ 7415, 12339, 19287, ..., 32647, 33814, 45500]), + values=tensor([0.8370, 0.0969, 0.8316, ..., 0.1944, 0.4025, 0.6344]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.2154, 0.6825, 0.0342, ..., 0.6227, 0.4225, 0.9397]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.916261672973633 seconds + +[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44] +[20.44, 20.32, 23.6, 25.4, 27.36, 28.36, 28.36, 29.32, 26.76, 26.0, 25.28, 24.92, 25.04, 25.08] +14.593353509902954 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202} +[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44, 20.2, 20.24, 20.32, 20.44, 20.72, 20.92, 20.92, 21.36, 21.28, 21.28] +371.7 +18.585 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202, 'J_1KI': 193.21203883991467, 'W_1KI': 13.239728531814311, 'W_D': 5.286230542861201, 'J_D': 77.14383104681971, 'W_D_1KI': 2.931908232313478, 'J_D_1KI': 1.6261276940174587} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..62a0664 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 53.93572449684143, "TIME_S_1KI": 53.93572449684143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1405.257185211182, "W": 23.49413775375655, "J_1KI": 1405.257185211182, "W_1KI": 23.49413775375655, "W_D": 4.945137753756551, "J_D": 295.78401357650813, "W_D_1KI": 4.945137753756551, "J_D_1KI": 4.945137753756551} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..cbf5bdc --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 53.93572449684143} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 63, 121, ..., 2499897, + 2499959, 2500000]), + col_indices=tensor([ 158, 1232, 2736, ..., 48449, 48581, 49575]), + values=tensor([0.0263, 0.9327, 0.9651, ..., 0.1558, 0.2228, 0.0301]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0174, 0.1708, 0.2801, ..., 0.8892, 0.6468, 0.1800]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 53.93572449684143 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 63, 121, ..., 2499897, + 2499959, 2500000]), + col_indices=tensor([ 158, 1232, 2736, ..., 48449, 48581, 49575]), + values=tensor([0.0263, 0.9327, 0.9651, ..., 0.1558, 0.2228, 0.0301]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0174, 0.1708, 0.2801, ..., 0.8892, 0.6468, 0.1800]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 53.93572449684143 seconds + +[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72] +[20.76, 21.16, 21.32, 25.56, 26.68, 28.72, 29.48, 27.44, 25.8, 24.96, 24.56, 24.52, 24.56, 24.64, 24.6, 24.52, 24.56, 24.56, 24.6, 24.6, 24.68, 24.68, 24.4, 24.48, 24.4, 24.56, 24.52, 24.8, 24.64, 24.8, 24.68, 24.88, 24.56, 24.56, 24.36, 24.48, 24.64, 24.68, 24.56, 24.72, 24.48, 24.48, 24.44, 24.92, 25.08, 24.92, 24.96, 24.76, 24.76, 24.56, 24.44, 24.36, 24.36, 24.44, 24.36, 24.44, 24.52] +59.8130989074707 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655} +[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72, 20.2, 20.6, 20.56, 20.68, 20.8, 20.8, 20.64, 20.56, 20.36, 20.16] +370.97999999999996 +18.549 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655, 'J_1KI': 1405.257185211182, 'W_1KI': 23.49413775375655, 'W_D': 4.945137753756551, 'J_D': 295.78401357650813, 'W_D_1KI': 4.945137753756551, 'J_D_1KI': 4.945137753756551} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..8adab40 --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10285, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.171167612075806, "TIME_S_1KI": 0.9889321936874872, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.4937496185303, "W": 23.99041156544644, "J_1KI": 31.647423395092883, "W_1KI": 2.332563107967568, "W_D": 5.591411565446439, "J_D": 75.86237156176564, "W_D_1KI": 0.5436472110302809, "J_D_1KI": 0.052858260673824105} diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..12e8b6c --- /dev/null +++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.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 synthetic csr 1000 -ss 50000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.020900011062622} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 24999, 25000]), + col_indices=tensor([43592, 45763, 41730, ..., 2923, 32227, 39553]), + values=tensor([0.0398, 0.4210, 0.0283, ..., 0.1409, 0.8695, 0.8837]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8554, 0.8486, 0.8747, ..., 0.5244, 0.7497, 0.0831]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 1.020900011062622 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 synthetic csr 10285 -ss 50000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.171167612075806} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([14664, 43703, 46520, ..., 7061, 31497, 43987]), + values=tensor([0.1911, 0.5487, 0.9416, ..., 0.5242, 0.5616, 0.0900]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7733, 0.9528, 0.6124, ..., 0.0354, 0.2670, 0.0752]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.171167612075806 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([14664, 43703, 46520, ..., 7061, 31497, 43987]), + values=tensor([0.1911, 0.5487, 0.9416, ..., 0.5242, 0.5616, 0.0900]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7733, 0.9528, 0.6124, ..., 0.0354, 0.2670, 0.0752]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.171167612075806 seconds + +[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52] +[20.52, 20.72, 24.24, 25.88, 27.44, 27.44, 28.48, 29.32, 25.96, 26.28, 25.8, 26.0, 25.92] +13.567660093307495 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644} +[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52, 20.56, 20.36, 20.28, 20.12, 20.2, 20.6, 20.92, 20.8, 20.6, 20.6] +367.98 +18.399 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644, 'J_1KI': 31.647423395092883, 'W_1KI': 2.332563107967568, 'W_D': 5.591411565446439, 'J_D': 75.86237156176564, 'W_D_1KI': 0.5436472110302809, 'J_D_1KI': 0.052858260673824105} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..e1dfb7e --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6038, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.267276763916016, "TIME_S_1KI": 1.7004433196283564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.7928693008422, "W": 66.02, "J_1KI": 141.9001108481024, "W_1KI": 10.934084133819145, "W_D": 30.906499999999994, "J_D": 401.09767971897116, "W_D_1KI": 5.1186651209009595, "J_D_1KI": 0.8477418219445113} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..ba4be77 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.7388477325439453} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 28, ..., 999983, + 999988, 1000000]), + col_indices=tensor([ 2300, 3196, 10757, ..., 92248, 95895, 96660]), + values=tensor([0.0937, 0.5944, 0.4639, ..., 0.5292, 0.3684, 0.5963]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9252, 0.4601, 0.1039, ..., 0.3841, 0.9664, 0.4740]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 1.7388477325439453 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6038', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.267276763916016} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 25, ..., 999976, + 999990, 1000000]), + col_indices=tensor([ 7337, 9006, 37341, ..., 86240, 86867, 93776]), + values=tensor([0.1177, 0.4165, 0.7590, ..., 0.7494, 0.7065, 0.3766]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.1410, 0.1591, 0.3967, ..., 0.8959, 0.7085, 0.3739]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.267276763916016 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 25, ..., 999976, + 999990, 1000000]), + col_indices=tensor([ 7337, 9006, 37341, ..., 86240, 86867, 93776]), + values=tensor([0.1177, 0.4165, 0.7590, ..., 0.7494, 0.7065, 0.3766]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.1410, 0.1591, 0.3967, ..., 0.8959, 0.7085, 0.3739]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.267276763916016 seconds + +[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76] +[66.02] +12.977777481079102 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02} +[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76, 39.02, 38.96, 38.6, 38.78, 38.41, 38.79, 38.39, 38.7, 43.94, 38.8] +702.27 +35.1135 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02, 'J_1KI': 141.9001108481024, 'W_1KI': 10.934084133819145, 'W_D': 30.906499999999994, 'J_D': 401.09767971897116, 'W_D_1KI': 5.1186651209009595, 'J_D_1KI': 0.8477418219445113} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..fbd54c6 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12169, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.374937295913696, "TIME_S_1KI": 0.8525710654871967, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 840.8859812259674, "W": 64.58, "J_1KI": 69.10066408299511, "W_1KI": 5.306927438573425, "W_D": 29.637499999999996, "J_D": 385.90520700812334, "W_D_1KI": 2.4354918234859064, "J_D_1KI": 0.20013902732236885} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..cc678f8 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8628060817718506} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 99999, 100000, + 100000]), + col_indices=tensor([15542, 51530, 32014, ..., 17183, 69417, 75150]), + values=tensor([0.6948, 0.1030, 0.8530, ..., 0.6511, 0.2631, 0.7718]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.3720, 0.8026, 0.8839, ..., 0.1725, 0.9607, 0.0788]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.8628060817718506 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12169', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.374937295913696} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 99998, 99998, + 100000]), + col_indices=tensor([38450, 44184, 11395, ..., 3206, 2272, 42747]), + values=tensor([0.8156, 0.6388, 0.3060, ..., 0.5932, 0.6977, 0.4008]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7706, 0.5998, 0.9728, ..., 0.9827, 0.6551, 0.5654]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.374937295913696 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 99998, 99998, + 100000]), + col_indices=tensor([38450, 44184, 11395, ..., 3206, 2272, 42747]), + values=tensor([0.8156, 0.6388, 0.3060, ..., 0.5932, 0.6977, 0.4008]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7706, 0.5998, 0.9728, ..., 0.9827, 0.6551, 0.5654]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.374937295913696 seconds + +[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83] +[64.58] +13.0208420753479 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58} +[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83, 40.06, 39.01, 39.04, 38.86, 38.59, 38.53, 38.46, 38.45, 40.01, 38.66] +698.85 +34.9425 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58, 'J_1KI': 69.10066408299511, 'W_1KI': 5.306927438573425, 'W_D': 29.637499999999996, 'J_D': 385.90520700812334, 'W_D_1KI': 2.4354918234859064, 'J_D_1KI': 0.20013902732236885} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..3886b8d --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237950, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.479950666427612, "TIME_S_1KI": 0.044042658820876705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.0966372108459, "W": 64.88, "J_1KI": 3.5599774625377005, "W_1KI": 0.2726623240176507, "W_D": 29.35949999999999, "J_D": 383.32820160591587, "W_D_1KI": 0.12338516495061984, "J_D_1KI": 0.0005185339985317077} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..4b3b779 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.055043935775756836} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9999, 10000, 10000]), + col_indices=tensor([2489, 8082, 1798, ..., 7687, 8784, 7173]), + values=tensor([0.0419, 0.2217, 0.5372, ..., 0.9380, 0.6037, 0.5878]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.6995, 0.5522, 0.6987, ..., 0.2479, 0.0646, 0.0677]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.055043935775756836 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '190756', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.417458295822144} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 10000, 10000, 10000]), + col_indices=tensor([6369, 8699, 1454, ..., 3376, 4538, 4463]), + values=tensor([0.7752, 0.1565, 0.1050, ..., 0.8742, 0.0228, 0.3625]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.2878, 0.2325, 0.9670, ..., 0.8581, 0.8156, 0.4801]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 8.417458295822144 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237950', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.479950666427612} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 9997, 9998, 10000]), + col_indices=tensor([2568, 4888, 9428, ..., 1921, 2148, 9872]), + values=tensor([0.1473, 0.4194, 0.4025, ..., 0.4119, 0.3062, 0.3667]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.9989, 0.1547, 0.2140, ..., 0.5569, 0.3690, 0.8580]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.479950666427612 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 9997, 9998, 10000]), + col_indices=tensor([2568, 4888, 9428, ..., 1921, 2148, 9872]), + values=tensor([0.1473, 0.4194, 0.4025, ..., 0.4119, 0.3062, 0.3667]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.9989, 0.1547, 0.2140, ..., 0.5569, 0.3690, 0.8580]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.479950666427612 seconds + +[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53] +[64.88] +13.056360006332397 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88} +[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53, 39.54, 38.39, 38.83, 43.63, 38.88, 38.94, 39.01, 39.89, 38.82, 38.68] +710.4100000000001 +35.520500000000006 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88, 'J_1KI': 3.5599774625377005, 'W_1KI': 0.2726623240176507, 'W_D': 29.35949999999999, 'J_D': 383.32820160591587, 'W_D_1KI': 0.12338516495061984, 'J_D_1KI': 0.0005185339985317077} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..4c5c61e --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75505, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.45979928970337, "TIME_S_1KI": 0.15177536970668656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 869.2745451879501, "W": 66.09, "J_1KI": 11.512807697343886, "W_1KI": 0.8753062711078737, "W_D": 31.095250000000007, "J_D": 408.9924239863158, "W_D_1KI": 0.4118303423614331, "J_D_1KI": 0.00545434530642253} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..afe840d --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.15292811393737793} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 10, ..., 99983, 99994, + 100000]), + col_indices=tensor([ 267, 3923, 5616, ..., 4271, 7755, 9973]), + values=tensor([0.9283, 0.7846, 0.2151, ..., 0.9447, 0.6120, 0.1119]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.9192, 0.5849, 0.9579, ..., 0.9586, 0.7879, 0.6201]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.15292811393737793 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68659', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.547868490219116} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 21, ..., 99972, 99985, + 100000]), + col_indices=tensor([ 305, 380, 962, ..., 8769, 9180, 9915]), + values=tensor([0.5782, 0.8638, 0.8069, ..., 0.1223, 0.7033, 0.9891]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4135, 0.4566, 0.8532, ..., 0.7837, 0.5944, 0.7679]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 9.547868490219116 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75505', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.45979928970337} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 21, ..., 99982, 99992, + 100000]), + col_indices=tensor([1022, 1138, 1407, ..., 6223, 7233, 9402]), + values=tensor([0.9484, 0.5958, 0.7782, ..., 0.0863, 0.6723, 0.0562]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0389, 0.1147, 0.5260, ..., 0.1033, 0.1694, 0.2810]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 11.45979928970337 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 21, ..., 99982, 99992, + 100000]), + col_indices=tensor([1022, 1138, 1407, ..., 6223, 7233, 9402]), + values=tensor([0.9484, 0.5958, 0.7782, ..., 0.0863, 0.6723, 0.0562]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0389, 0.1147, 0.5260, ..., 0.1033, 0.1694, 0.2810]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 11.45979928970337 seconds + +[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45] +[66.09] +13.152890682220459 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09} +[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45, 39.1, 38.46, 38.46, 39.62, 38.45, 39.31, 38.8, 38.73, 38.89, 38.78] +699.895 +34.994749999999996 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09, 'J_1KI': 11.512807697343886, 'W_1KI': 0.8753062711078737, 'W_D': 31.095250000000007, 'J_D': 408.9924239863158, 'W_D_1KI': 0.4118303423614331, 'J_D_1KI': 0.00545434530642253} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..4467311 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10051, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.396085023880005, "TIME_S_1KI": 1.0343334020376087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.9663576483727, "W": 66.05, "J_1KI": 86.1572338720896, "W_1KI": 6.571485424335887, "W_D": 30.772, "J_D": 403.44461404323573, "W_D_1KI": 3.061585911849567, "J_D_1KI": 0.3046051051486983} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..ec9215b --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.0446221828460693} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 93, 186, ..., 999791, + 999885, 1000000]), + col_indices=tensor([ 85, 646, 706, ..., 9852, 9875, 9886]), + values=tensor([0.7433, 0.1282, 0.1316, ..., 0.9681, 0.9495, 0.6187]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5006, 0.3207, 0.7634, ..., 0.1693, 0.2023, 0.9705]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 1.0446221828460693 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10051', '-ss', '10000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.396085023880005} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 82, 194, ..., 999830, + 999924, 1000000]), + col_indices=tensor([ 207, 248, 391, ..., 9735, 9842, 9886]), + values=tensor([0.2382, 0.1304, 0.8275, ..., 0.9132, 0.3101, 0.1677]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0491, 0.4304, 0.0195, ..., 0.4012, 0.5324, 0.0059]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.396085023880005 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 82, 194, ..., 999830, + 999924, 1000000]), + col_indices=tensor([ 207, 248, 391, ..., 9735, 9842, 9886]), + values=tensor([0.2382, 0.1304, 0.8275, ..., 0.9132, 0.3101, 0.1677]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0491, 0.4304, 0.0195, ..., 0.4012, 0.5324, 0.0059]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.396085023880005 seconds + +[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48] +[66.05] +13.110769987106323 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05} +[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48, 42.6, 39.8, 38.39, 38.73, 39.03, 38.72, 38.63, 38.42, 38.48, 38.3] +705.56 +35.278 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05, 'J_1KI': 86.1572338720896, 'W_1KI': 6.571485424335887, 'W_D': 30.772, 'J_D': 403.44461404323573, 'W_D_1KI': 3.061585911849567, 'J_D_1KI': 0.3046051051486983} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..9a92bbf --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496549844741821, "TIME_S_1KI": 5.963948775421489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1063.6957297325134, "W": 75.3, "J_1KI": 604.3725737116553, "W_1KI": 42.784090909090914, "W_D": 40.305749999999996, "J_D": 569.3632690393924, "W_D_1KI": 22.900994318181816, "J_D_1KI": 13.011928589876032} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..f82f04e --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.962578296661377} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 507, 983, ..., 4998985, + 4999482, 5000000]), + col_indices=tensor([ 6, 14, 63, ..., 9975, 9976, 9988]), + values=tensor([0.1343, 0.9147, 0.2964, ..., 0.8307, 0.6480, 0.1778]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4820, 0.9526, 0.2470, ..., 0.0414, 0.1724, 0.7388]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 5.962578296661377 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1760', '-ss', '10000', '-sd', '0.05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496549844741821} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 494, 1024, ..., 4999026, + 4999505, 5000000]), + col_indices=tensor([ 14, 81, 111, ..., 9976, 9994, 9996]), + values=tensor([0.8750, 0.2097, 0.6973, ..., 0.7142, 0.2835, 0.0523]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.6644, 0.2536, 0.5514, ..., 0.5924, 0.6712, 0.0391]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.496549844741821 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 494, 1024, ..., 4999026, + 4999505, 5000000]), + col_indices=tensor([ 14, 81, 111, ..., 9976, 9994, 9996]), + values=tensor([0.8750, 0.2097, 0.6973, ..., 0.7142, 0.2835, 0.0523]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.6644, 0.2536, 0.5514, ..., 0.5924, 0.6712, 0.0391]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.496549844741821 seconds + +[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82] +[75.3] +14.126105308532715 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3} +[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82, 39.55, 38.59, 39.72, 38.89, 38.53, 38.82, 38.62, 39.33, 38.84, 38.85] +699.885 +34.99425 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3, 'J_1KI': 604.3725737116553, 'W_1KI': 42.784090909090914, 'W_D': 40.305749999999996, 'J_D': 569.3632690393924, 'W_D_1KI': 22.900994318181816, 'J_D_1KI': 13.011928589876032} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..5067ef9 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 363782, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.585279941558838, "TIME_S_1KI": 0.029097866143896176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3318356752395, "W": 64.69, "J_1KI": 2.3512208841428097, "W_1KI": 0.17782628057462985, "W_D": 29.81474999999999, "J_D": 394.2109266918896, "W_D_1KI": 0.08195773842576046, "J_D_1KI": 0.00022529355060382441} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..198dd99 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08438587188720703} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 654, 6772, 3481, 2535, 125, 3792, 8070, 9757, 6184, + 2668, 22, 1611, 8038, 5477, 9185, 5993, 9592, 3939, + 8639, 7553, 398, 5715, 4399, 2570, 9973, 3035, 1537, + 7004, 5611, 9891, 2622, 9818, 312, 6105, 1848, 1339, + 7382, 4214, 8955, 9892, 6474, 3909, 9732, 690, 3371, + 4869, 387, 3460, 9149, 5467, 6478, 5618, 6583, 2381, + 1542, 8342, 3787, 7463, 3823, 6427, 315, 6985, 523, + 5901, 9665, 9643, 5095, 3067, 2951, 9816, 6719, 6640, + 4349, 9622, 9227, 394, 8600, 2210, 9007, 6794, 6193, + 3591, 3763, 8848, 712, 2600, 6953, 345, 8176, 4284, + 2762, 1429, 3052, 9077, 9247, 8084, 9368, 8295, 4882, + 1029, 4128, 2221, 4457, 136, 1060, 5650, 2149, 979, + 879, 252, 4258, 4991, 6954, 9684, 5762, 3304, 4194, + 5738, 4881, 2067, 4630, 3102, 4373, 4364, 3467, 3904, + 2703, 9367, 5744, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.08438587188720703 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '124428', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.5914108753204346} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([3597, 9, 1044, 3716, 98, 3551, 3965, 3920, 2369, + 6622, 2889, 2733, 9619, 8879, 214, 1498, 5628, 5050, + 1612, 1432, 5924, 4223, 5673, 5024, 1571, 9554, 4117, + 6172, 4152, 1650, 6284, 8764, 3734, 5467, 6144, 9907, + 2415, 89, 4907, 3890, 4658, 8223, 3917, 3024, 6323, + 7419, 1781, 9076, 2972, 6710, 7400, 4605, 3762, 446, + 1387, 7068, 5435, 7232, 4568, 2187, 5029, 6733, 5022, + 6175, 6496, 4875, 4881, 4574, 9860, 7187, 9416, 1923, + 1194, 94, 6450, 120, 3556, 662, 3588, 5897, 9345, + 8674, 1514, 9592, 2980, 1401, 6049, 8787, 9171, 3495, + 9181, 919, 8930, 6135, 9408, 4922, 56, 574, 8860, + 478, 6298, 1874, 6479, 9220, 412, 8498, 4958, 3548, + 7785, 9175, 8108, 7647, 1805, 8157, 6171, 3362, 8230, + 6430, 7487, 1385, 3551, 2958, 7149, 4586, 8471, 1688, + 6329, 9764, 2504, 67, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 3.5914108753204346 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '363782', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.585279941558838} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7943, 4488, 6154, 9391, 155, 1146, 4006, 8134, 8781, + 7101, 3276, 7191, 9320, 2859, 4578, 2750, 6596, 4201, + 2541, 6640, 9961, 1286, 5879, 9740, 3536, 2151, 9582, + 2021, 7827, 8693, 2313, 555, 3377, 7363, 334, 2888, + 9782, 3162, 5677, 4519, 3889, 4828, 247, 2616, 279, + 8565, 2538, 9525, 8485, 2616, 1166, 2089, 7055, 6468, + 9499, 1310, 5525, 2540, 8419, 935, 4661, 2785, 1947, + 1602, 2918, 4726, 3718, 3716, 5417, 2404, 2572, 1793, + 4269, 7015, 419, 4336, 5223, 1709, 8875, 645, 5198, + 3752, 5677, 5777, 9470, 6191, 7729, 3008, 6984, 7165, + 5063, 8482, 7789, 9298, 6624, 3445, 4654, 5489, 7051, + 2026, 5766, 3319, 8576, 4863, 735, 6400, 8243, 4596, + 9136, 5453, 8094, 6731, 4592, 6080, 2446, 2152, 9189, + 7168, 5575, 8736, 8708, 188, 2747, 5830, 9269, 8804, + 3159, 3201, 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csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.585279941558838 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([7943, 4488, 6154, 9391, 155, 1146, 4006, 8134, 8781, + 7101, 3276, 7191, 9320, 2859, 4578, 2750, 6596, 4201, + 2541, 6640, 9961, 1286, 5879, 9740, 3536, 2151, 9582, + 2021, 7827, 8693, 2313, 555, 3377, 7363, 334, 2888, + 9782, 3162, 5677, 4519, 3889, 4828, 247, 2616, 279, + 8565, 2538, 9525, 8485, 2616, 1166, 2089, 7055, 6468, + 9499, 1310, 5525, 2540, 8419, 935, 4661, 2785, 1947, + 1602, 2918, 4726, 3718, 3716, 5417, 2404, 2572, 1793, + 4269, 7015, 419, 4336, 5223, 1709, 8875, 645, 5198, + 3752, 5677, 5777, 9470, 6191, 7729, 3008, 6984, 7165, + 5063, 8482, 7789, 9298, 6624, 3445, 4654, 5489, 7051, + 2026, 5766, 3319, 8576, 4863, 735, 6400, 8243, 4596, + 9136, 5453, 8094, 6731, 4592, 6080, 2446, 2152, 9189, + 7168, 5575, 8736, 8708, 188, 2747, 5830, 9269, 8804, + 3159, 3201, 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csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.585279941558838 seconds + +[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42] +[64.69] +13.222010135650635 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69} +[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42, 40.2, 38.44, 38.35, 38.82, 38.9, 38.53, 38.83, 38.37, 38.44, 38.39] +697.5050000000001 +34.87525000000001 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69, 'J_1KI': 2.3512208841428097, 'W_1KI': 0.17782628057462985, 'W_D': 29.81474999999999, 'J_D': 394.2109266918896, 'W_D_1KI': 0.08195773842576046, 'J_D_1KI': 0.00022529355060382441} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..f3fd74b --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1366, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.481968879699707, "TIME_S_1KI": 7.673476485870942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 980.9324438238144, "W": 72.78, "J_1KI": 718.1057421843444, "W_1KI": 53.2796486090776, "W_D": 37.9805, "J_D": 511.9030596681833, "W_D_1KI": 27.804172767203514, "J_D_1KI": 20.35444565681077} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..170f45c --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.686005115509033} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 7, ..., 2499986, + 2499989, 2500000]), + col_indices=tensor([176994, 249617, 373837, ..., 283997, 343168, + 447931]), + values=tensor([0.4576, 0.5348, 0.2572, ..., 0.1314, 0.2229, 0.5974]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1382, 0.9782, 0.8741, ..., 0.2337, 0.6569, 0.8329]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 7.686005115509033 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1366', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.481968879699707} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 9, ..., 2499988, + 2499995, 2500000]), + col_indices=tensor([ 13301, 29016, 299078, ..., 480591, 481476, + 496604]), + values=tensor([0.4578, 0.5414, 0.1917, ..., 0.8449, 0.5002, 0.9459]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0252, 0.3938, 0.2908, ..., 0.4459, 0.5549, 0.8752]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.481968879699707 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 9, ..., 2499988, + 2499995, 2500000]), + col_indices=tensor([ 13301, 29016, 299078, ..., 480591, 481476, + 496604]), + values=tensor([0.4578, 0.5414, 0.1917, ..., 0.8449, 0.5002, 0.9459]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0252, 0.3938, 0.2908, ..., 0.4459, 0.5549, 0.8752]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.481968879699707 seconds + +[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83] +[72.78] +13.478049516677856 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78} +[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83, 39.07, 38.47, 38.63, 38.57, 38.58, 38.77, 38.83, 38.82, 38.46, 38.73] +695.99 +34.7995 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78, 'J_1KI': 718.1057421843444, 'W_1KI': 53.2796486090776, 'W_D': 37.9805, 'J_D': 511.9030596681833, 'W_D_1KI': 27.804172767203514, 'J_D_1KI': 20.35444565681077} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..3949dab --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15344, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396661281585693, "TIME_S_1KI": 0.6775717727832178, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.4673494148254, "W": 65.08, "J_1KI": 55.166015994188314, "W_1KI": 4.241397288842545, "W_D": 30.18325, "J_D": 392.5804490507841, "W_D_1KI": 1.9671044056308655, "J_D_1KI": 0.1282002349863703} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..4ae3041 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6842620372772217} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 10, ..., 249991, 249995, + 250000]), + col_indices=tensor([ 5258, 47122, 48422, ..., 30033, 41208, 46342]), + values=tensor([0.6499, 0.7211, 0.6182, ..., 0.7244, 0.8782, 0.8107]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.4190, 0.1278, 0.1748, ..., 0.3464, 0.8679, 0.1666]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.6842620372772217 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15344', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396661281585693} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 9, ..., 249992, 249997, + 250000]), + col_indices=tensor([10534, 13796, 13942, ..., 20381, 35132, 47921]), + values=tensor([0.7820, 0.3755, 0.2967, ..., 0.2418, 0.5762, 0.2824]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5105, 0.5604, 0.4598, ..., 0.4891, 0.0194, 0.7500]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.396661281585693 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 9, ..., 249992, 249997, + 250000]), + col_indices=tensor([10534, 13796, 13942, ..., 20381, 35132, 47921]), + values=tensor([0.7820, 0.3755, 0.2967, ..., 0.2418, 0.5762, 0.2824]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5105, 0.5604, 0.4598, ..., 0.4891, 0.0194, 0.7500]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.396661281585693 seconds + +[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28] +[65.08] +13.006566524505615 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08} +[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28, 39.19, 38.51, 38.7, 38.61, 39.0, 38.52, 39.05, 38.49, 38.55, 38.46] +697.935 +34.89675 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08, 'J_1KI': 55.166015994188314, 'W_1KI': 4.241397288842545, 'W_D': 30.18325, 'J_D': 392.5804490507841, 'W_D_1KI': 1.9671044056308655, 'J_D_1KI': 0.1282002349863703} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..bde3932 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3489, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.437294721603394, "TIME_S_1KI": 2.991486019376152, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 933.3928768968582, "W": 69.22, "J_1KI": 267.52447030577764, "W_1KI": 19.83949555746632, "W_D": 34.06175, "J_D": 459.30359469288595, "W_D_1KI": 9.762611063341934, "J_D_1KI": 2.798111511419299} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..4299e13 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0088562965393066} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 34, 93, ..., 2499916, + 2499957, 2500000]), + col_indices=tensor([ 603, 3952, 4942, ..., 45684, 45744, 47378]), + values=tensor([0.2755, 0.3359, 0.2897, ..., 0.6537, 0.9903, 0.6398]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0226, 0.9900, 0.4586, ..., 0.9619, 0.5778, 0.7456]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 3.0088562965393066 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3489', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.437294721603394} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 60, 107, ..., 2499916, + 2499955, 2500000]), + col_indices=tensor([ 84, 88, 1962, ..., 43229, 45310, 46070]), + values=tensor([0.8625, 0.0720, 0.1202, ..., 0.4148, 0.7410, 0.3059]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1590, 0.6012, 0.6850, ..., 0.6120, 0.4384, 0.7195]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.437294721603394 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 60, 107, ..., 2499916, + 2499955, 2500000]), + col_indices=tensor([ 84, 88, 1962, ..., 43229, 45310, 46070]), + values=tensor([0.8625, 0.0720, 0.1202, ..., 0.4148, 0.7410, 0.3059]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1590, 0.6012, 0.6850, ..., 0.6120, 0.4384, 0.7195]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.437294721603394 seconds + +[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35] +[69.22] +13.484439134597778 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22} +[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35, 39.37, 38.65, 38.68, 38.35, 38.4, 38.76, 38.81, 38.76, 38.48, 38.39] +703.165 +35.158249999999995 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22, 'J_1KI': 267.52447030577764, 'W_1KI': 19.83949555746632, 'W_D': 34.06175, 'J_D': 459.30359469288595, 'W_D_1KI': 9.762611063341934, 'J_D_1KI': 2.798111511419299} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..eb380f5 --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35734, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471917629241943, "TIME_S_1KI": 0.2930519289539918, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 841.987050704956, "W": 64.44, "J_1KI": 23.562630847510942, "W_1KI": 1.803324564840208, "W_D": 29.634499999999996, "J_D": 387.2108202066421, "W_D_1KI": 0.8293082218615323, "J_D_1KI": 0.023207819495761246} diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..96eed5a --- /dev/null +++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3157460689544678} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([ 4062, 9525, 48228, ..., 39550, 26780, 46383]), + values=tensor([0.9682, 0.2653, 0.7546, ..., 0.8059, 0.5876, 0.9597]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.6423, 0.4854, 0.6493, ..., 0.6821, 0.6803, 0.2283]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.3157460689544678 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33254', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.771223545074463} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([27980, 12083, 1659, ..., 17852, 35908, 47898]), + values=tensor([0.9789, 0.4410, 0.2389, ..., 0.6711, 0.3630, 0.6906]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.9986, 0.5860, 0.4640, ..., 0.2646, 0.6800, 0.7666]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 9.771223545074463 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35734', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471917629241943} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 24999, 25000, 25000]), + col_indices=tensor([14210, 9782, 13262, ..., 32699, 48019, 38373]), + values=tensor([0.8162, 0.2704, 0.1597, ..., 0.7469, 0.6704, 0.2691]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.5208, 0.6581, 0.5659, ..., 0.1337, 0.4152, 0.4244]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.471917629241943 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 24999, 25000, 25000]), + col_indices=tensor([14210, 9782, 13262, ..., 32699, 48019, 38373]), + values=tensor([0.8162, 0.2704, 0.1597, ..., 0.7469, 0.6704, 0.2691]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.5208, 0.6581, 0.5659, ..., 0.1337, 0.4152, 0.4244]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.471917629241943 seconds + +[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4] +[64.44] +13.066217422485352 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44} +[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4, 39.76, 38.41, 38.52, 38.56, 38.6, 38.96, 38.81, 38.77, 38.7, 38.54] +696.11 +34.8055 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44, 'J_1KI': 23.562630847510942, 'W_1KI': 1.803324564840208, 'W_D': 29.634499999999996, 'J_D': 387.2108202066421, 'W_D_1KI': 0.8293082218615323, 'J_D_1KI': 0.023207819495761246} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..2a06eb8 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.427528619766235, "TIME_S_1KI": 2.8599913932436194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.8558839225768, "W": 47.94, "J_1KI": 190.03178385150215, "W_1KI": 13.14865606143719, "W_D": 31.4125, "J_D": 453.99114421606066, "W_D_1KI": 8.615606143719145, "J_D_1KI": 2.3630296609213235} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..cc7a205 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.879791498184204} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 23, ..., 999979, + 999989, 1000000]), + col_indices=tensor([ 5015, 13201, 16372, ..., 56043, 65196, 77096]), + values=tensor([0.8877, 0.8022, 0.3967, ..., 0.7199, 0.8399, 0.8151]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6595, 0.9245, 0.4951, ..., 0.4587, 0.0765, 0.0892]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 2.879791498184204 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', 'synthetic', 'csr', '3646', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.427528619766235} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 24, ..., 999984, + 999992, 1000000]), + col_indices=tensor([12724, 24596, 29019, ..., 72798, 83516, 98300]), + values=tensor([0.5582, 0.8508, 0.8777, ..., 0.7164, 0.8705, 0.2253]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.8849, 0.3552, 0.8045, ..., 0.9875, 0.5127, 0.0107]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.427528619766235 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 24, ..., 999984, + 999992, 1000000]), + col_indices=tensor([12724, 24596, 29019, ..., 72798, 83516, 98300]), + values=tensor([0.5582, 0.8508, 0.8777, ..., 0.7164, 0.8705, 0.2253]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.8849, 0.3552, 0.8045, ..., 0.9875, 0.5127, 0.0107]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.427528619766235 seconds + +[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09] +[47.94] +14.452563285827637 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94} +[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09, 18.01, 18.32, 17.86, 17.98, 18.22, 18.07, 19.34, 21.72, 17.99, 18.27] +330.54999999999995 +16.527499999999996 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94, 'J_1KI': 190.03178385150215, 'W_1KI': 13.14865606143719, 'W_D': 31.4125, 'J_D': 453.99114421606066, 'W_D_1KI': 8.615606143719145, 'J_D_1KI': 2.3630296609213235} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..1871367 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8006, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406643390655518, "TIME_S_1KI": 1.2998555321828025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.0740174865723, "W": 46.720000000000006, "J_1KI": 82.82213558413343, "W_1KI": 5.835623282538097, "W_D": 30.276250000000005, "J_D": 429.69594867140063, "W_D_1KI": 3.7816949787659264, "J_D_1KI": 0.47235760414263384} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..b29167b --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3114714622497559} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 99998, 99998, + 100000]), + col_indices=tensor([15714, 63018, 47083, ..., 95898, 11433, 73543]), + values=tensor([0.8298, 0.7556, 0.0451, ..., 0.9622, 0.2125, 0.4932]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8440, 0.1023, 0.7738, ..., 0.5206, 0.7518, 0.6360]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 1.3114714622497559 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', 'synthetic', 'csr', '8006', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406643390655518} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 100000, 100000, + 100000]), + col_indices=tensor([38549, 23010, 96204, ..., 15384, 78128, 94145]), + values=tensor([0.9276, 0.2040, 0.0329, ..., 0.0402, 0.0179, 0.0490]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1445, 0.8456, 0.7445, ..., 0.5274, 0.1855, 0.5940]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.406643390655518 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 100000, 100000, + 100000]), + col_indices=tensor([38549, 23010, 96204, ..., 15384, 78128, 94145]), + values=tensor([0.9276, 0.2040, 0.0329, ..., 0.0402, 0.0179, 0.0490]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1445, 0.8456, 0.7445, ..., 0.5274, 0.1855, 0.5940]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.406643390655518 seconds + +[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59] +[46.72] +14.192508935928345 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006} +[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59, 18.4, 18.11, 18.06, 18.12, 17.9, 18.34, 18.04, 17.94, 17.82, 18.39] +328.875 +16.44375 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006, 'J_1KI': 82.82213558413343, 'W_1KI': 5.835623282538097, 'W_D': 30.276250000000005, 'J_D': 429.69594867140063, 'W_D_1KI': 3.7816949787659264, 'J_D_1KI': 0.47235760414263384} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..eaa436a --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 85057, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.436183214187622, "TIME_S_1KI": 0.12269634732223829, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.123476600647, "W": 46.56, "J_1KI": 7.796224609387199, "W_1KI": 0.5473976274733414, "W_D": 8.939750000000004, "J_D": 127.32298324614769, "W_D_1KI": 0.10510304854391765, "J_D_1KI": 0.0012356778224475076} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..0f19044 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.138319730758667} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9998, 9999, 10000]), + col_indices=tensor([6848, 9607, 2682, ..., 9449, 6129, 3470]), + values=tensor([0.4694, 0.9529, 0.1463, ..., 0.1268, 0.1399, 0.3765]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.5998, 0.7790, 0.8385, ..., 0.1561, 0.5420, 0.2267]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.138319730758667 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', 'synthetic', 'csr', '75911', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.370872497558594} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 10000, 10000, 10000]), + col_indices=tensor([2414, 5580, 2005, ..., 9768, 442, 1851]), + values=tensor([0.7205, 0.5630, 0.0022, ..., 0.3635, 0.2630, 0.6566]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.5071, 0.1792, 0.6304, ..., 0.9432, 0.9596, 0.2753]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 9.370872497558594 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', 'synthetic', 'csr', '85057', '-ss', '10000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.436183214187622} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 10000, 10000, 10000]), + col_indices=tensor([2255, 7580, 9802, ..., 6433, 5292, 8461]), + values=tensor([0.3444, 0.5478, 0.9067, ..., 0.7957, 0.9972, 0.7349]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.8453, 0.7973, 0.9010, ..., 0.7504, 0.8828, 0.5942]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.436183214187622 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 10000, 10000, 10000]), + col_indices=tensor([2255, 7580, 9802, ..., 6433, 5292, 8461]), + values=tensor([0.3444, 0.5478, 0.9067, ..., 0.7957, 0.9972, 0.7349]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.8453, 0.7973, 0.9010, ..., 0.7504, 0.8828, 0.5942]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.436183214187622 seconds + +[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53] +[46.56] +14.242342710494995 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56} +[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53, 45.01, 44.41, 42.1, 40.98, 40.92, 39.18, 41.09, 39.73, 39.61, 39.6] +752.405 +37.62025 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56, 'J_1KI': 7.796224609387199, 'W_1KI': 0.5473976274733414, 'W_D': 8.939750000000004, 'J_D': 127.32298324614769, 'W_D_1KI': 0.10510304854391765, 'J_D_1KI': 0.0012356778224475076} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..aaafc55 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34558, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.457140684127808, "TIME_S_1KI": 0.3025968135924477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 669.6752694511413, "W": 47.19, "J_1KI": 19.37829936486895, "W_1KI": 1.3655304126396204, "W_D": 30.795499999999997, "J_D": 437.020232260704, "W_D_1KI": 0.8911250651079344, "J_D_1KI": 0.02578636104832266} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..0fdae4e --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3263256549835205} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 22, ..., 99981, 99992, + 100000]), + col_indices=tensor([ 85, 1274, 1422, ..., 6599, 6784, 7278]), + values=tensor([0.2164, 0.2550, 1.0000, ..., 0.9260, 0.0708, 0.0725]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6027, 0.7133, 0.6416, ..., 0.5356, 0.1307, 0.5576]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.3263256549835205 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', 'synthetic', 'csr', '32176', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.776132822036743} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 20, ..., 99981, 99994, + 100000]), + col_indices=tensor([ 544, 706, 2472, ..., 6055, 7261, 9945]), + values=tensor([0.4979, 0.3488, 0.7538, ..., 0.1989, 0.3068, 0.3191]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.5709, 0.1010, 0.9044, ..., 0.7157, 0.3275, 0.4556]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 9.776132822036743 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', 'synthetic', 'csr', '34558', '-ss', '10000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.457140684127808} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 24, ..., 99980, 99989, + 100000]), + col_indices=tensor([ 44, 4326, 6855, ..., 8487, 8731, 9188]), + values=tensor([0.5894, 0.7815, 0.8660, ..., 0.0108, 0.2427, 0.5894]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0531, 0.8679, 0.3068, ..., 0.5318, 0.1294, 0.3589]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.457140684127808 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 24, ..., 99980, 99989, + 100000]), + col_indices=tensor([ 44, 4326, 6855, ..., 8487, 8731, 9188]), + values=tensor([0.5894, 0.7815, 0.8660, ..., 0.0108, 0.2427, 0.5894]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0531, 0.8679, 0.3068, ..., 0.5318, 0.1294, 0.3589]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.457140684127808 seconds + +[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09] +[47.19] +14.191041946411133 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19} +[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09, 18.43, 17.95, 18.1, 18.53, 18.09, 18.56, 18.03, 17.91, 18.04, 18.04] +327.89 +16.3945 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19, 'J_1KI': 19.37829936486895, 'W_1KI': 1.3655304126396204, 'W_D': 30.795499999999997, 'J_D': 437.020232260704, 'W_D_1KI': 0.8911250651079344, 'J_D_1KI': 0.02578636104832266} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..b18c025 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5537, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.417654037475586, "TIME_S_1KI": 1.8814618091882944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 687.7580058908463, "W": 47.74000000000001, "J_1KI": 124.21130682514833, "W_1KI": 8.621997471554996, "W_D": 31.48425000000001, "J_D": 453.5723711137177, "W_D_1KI": 5.686156763590393, "J_D_1KI": 1.0269381910042248} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..42d7009 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.8961181640625} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 96, 195, ..., 999806, + 999906, 1000000]), + col_indices=tensor([ 19, 113, 151, ..., 9681, 9759, 9836]), + values=tensor([0.1144, 0.7732, 0.9749, ..., 0.1321, 0.3947, 0.2714]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.1354, 0.8257, 0.6569, ..., 0.0257, 0.7874, 0.8457]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 1.8961181640625 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', 'synthetic', 'csr', '5537', '-ss', '10000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.417654037475586} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 103, 198, ..., 999795, + 999893, 1000000]), + col_indices=tensor([ 194, 313, 451, ..., 9690, 9776, 9879]), + values=tensor([0.2779, 0.8250, 0.2083, ..., 0.7384, 0.0572, 0.6638]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5010, 0.3969, 0.7780, ..., 0.5969, 0.2345, 0.7915]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.417654037475586 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 103, 198, ..., 999795, + 999893, 1000000]), + col_indices=tensor([ 194, 313, 451, ..., 9690, 9776, 9879]), + values=tensor([0.2779, 0.8250, 0.2083, ..., 0.7384, 0.0572, 0.6638]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.5010, 0.3969, 0.7780, ..., 0.5969, 0.2345, 0.7915]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.417654037475586 seconds + +[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06] +[47.74] +14.406326055526733 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001} +[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06, 18.38, 17.81, 18.07, 18.25, 18.24, 17.94, 18.02, 18.06, 18.02, 17.93] +325.115 +16.25575 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001, 'J_1KI': 124.21130682514833, 'W_1KI': 8.621997471554996, 'W_D': 31.48425000000001, 'J_D': 453.5723711137177, 'W_D_1KI': 5.686156763590393, 'J_D_1KI': 1.0269381910042248} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..8a616ae --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.884310483932495, "TIME_S_1KI": 10.884310483932495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 825.0359781861306, "W": 47.81, "J_1KI": 825.0359781861306, "W_1KI": 47.81, "W_D": 31.353, "J_D": 541.0448237621785, "W_D_1KI": 31.352999999999998, "J_D_1KI": 31.352999999999998} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..aad2263 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.884310483932495} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 518, 1026, ..., 4999007, + 4999483, 5000000]), + col_indices=tensor([ 3, 39, 78, ..., 9968, 9975, 9994]), + values=tensor([0.2142, 0.4373, 0.1249, ..., 0.9529, 0.9095, 0.5518]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2404, 0.3133, 0.0015, ..., 0.7254, 0.6117, 0.4995]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.884310483932495 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 518, 1026, ..., 4999007, + 4999483, 5000000]), + col_indices=tensor([ 3, 39, 78, ..., 9968, 9975, 9994]), + values=tensor([0.2142, 0.4373, 0.1249, ..., 0.9529, 0.9095, 0.5518]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2404, 0.3133, 0.0015, ..., 0.7254, 0.6117, 0.4995]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.884310483932495 seconds + +[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87] +[47.81] +17.256556749343872 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81} +[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87, 18.49, 18.1, 18.43, 18.06, 18.42, 17.93, 18.03, 17.8, 18.13, 18.11] +329.14 +16.457 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81, 'J_1KI': 825.0359781861306, 'W_1KI': 47.81, 'W_D': 31.353, 'J_D': 541.0448237621785, 'W_D_1KI': 31.352999999999998, 'J_D_1KI': 31.352999999999998} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..a0720a2 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 225343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.386851072311401, "TIME_S_1KI": 0.04609351553991649, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.4945677185059, "W": 46.24, "J_1KI": 2.935500848566434, "W_1KI": 0.20519829770616352, "W_D": 30.072000000000003, "J_D": 430.2003598709107, "W_D_1KI": 0.1334498963801849, "J_D_1KI": 0.0005922078625925141} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..6742334 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06630802154541016} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([6116, 9123, 2230, 4007, 2708, 6506, 8700, 3316, 2761, + 1361, 1958, 5168, 9287, 8535, 3319, 5344, 902, 1975, + 488, 7509, 3585, 6731, 5003, 3621, 9227, 602, 6973, + 8702, 9039, 2485, 4067, 2477, 9061, 2388, 1777, 7081, + 5954, 215, 9598, 6942, 5591, 9010, 7196, 7714, 5337, + 1993, 6247, 2446, 6356, 9820, 7249, 3274, 1887, 2531, + 135, 4906, 4233, 322, 743, 3780, 3850, 995, 6910, + 9173, 1426, 5060, 4803, 1325, 8341, 4031, 7649, 3889, + 2513, 8971, 7759, 3358, 2558, 8091, 7627, 5455, 9323, + 4647, 1893, 5017, 4607, 6431, 7258, 1502, 6846, 4712, + 6760, 477, 7596, 524, 2899, 8608, 9797, 2612, 3584, + 7461, 3936, 7937, 8808, 4443, 6151, 2930, 8931, 2432, + 2320, 4314, 7498, 4175, 5649, 4525, 3428, 2414, 2246, + 8311, 112, 444, 1035, 4057, 4976, 2482, 1046, 9577, + 2837, 2113, 6259, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.06630802154541016 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', 'synthetic', 'csr', '158351', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.378459692001343} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([3030, 59, 6550, 5129, 1609, 5117, 7383, 9965, 3582, + 3502, 6345, 6436, 6545, 1264, 2983, 1876, 8807, 2513, + 1370, 9845, 3410, 1788, 9280, 9662, 1023, 5271, 4153, + 4966, 4311, 2499, 1351, 830, 9219, 80, 3996, 2842, + 7114, 2706, 7320, 1382, 918, 2923, 9877, 4768, 3727, + 9013, 967, 4451, 7441, 5152, 1538, 6863, 4268, 9001, + 4281, 9503, 6429, 8410, 3672, 4516, 1695, 339, 7612, + 3853, 503, 5817, 6729, 1224, 5432, 764, 7789, 9927, + 4207, 9375, 7672, 5553, 2923, 8869, 3033, 248, 9790, + 3596, 455, 6400, 8397, 9560, 6512, 4381, 185, 4100, + 9584, 4048, 7087, 5738, 4019, 9557, 6085, 6728, 6633, + 101, 4333, 6438, 6166, 4315, 8383, 4034, 9750, 3066, + 2471, 5789, 4395, 2815, 7182, 6690, 2540, 8742, 1904, + 5243, 4296, 5959, 4343, 4260, 4115, 6532, 9325, 6153, + 9591, 8540, 3207, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 7.378459692001343 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', 'synthetic', 'csr', '225343', '-ss', '10000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.386851072311401} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([5090, 5520, 6854, 5240, 6540, 5414, 9539, 8466, 479, + 3119, 5272, 7854, 2035, 3254, 301, 9387, 5412, 9403, + 3029, 6749, 4880, 1485, 7157, 5629, 6151, 4478, 7699, + 7698, 6954, 7468, 2219, 5639, 6353, 506, 8308, 5821, + 2487, 7627, 3842, 7369, 6744, 2867, 7111, 7921, 1986, + 380, 9961, 4202, 9024, 661, 5897, 7449, 9845, 9461, + 9917, 234, 7756, 4104, 195, 2757, 4588, 1755, 600, + 3208, 9769, 495, 8241, 6634, 6218, 247, 649, 2255, + 6934, 5056, 3570, 5404, 4033, 4528, 6168, 3330, 5154, + 6668, 8969, 4990, 5914, 7294, 7798, 8937, 1984, 811, + 8267, 6000, 8441, 2901, 6504, 2951, 6191, 5592, 9657, + 5206, 4311, 4344, 6838, 4035, 8212, 9827, 8714, 8242, + 2597, 1268, 6941, 152, 4041, 7546, 5546, 9553, 8677, + 3838, 1475, 6605, 2849, 8979, 1585, 9524, 5732, 668, + 8770, 2014, 4555, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.386851072311401 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 1000, 1000, 1000]), + col_indices=tensor([5090, 5520, 6854, 5240, 6540, 5414, 9539, 8466, 479, + 3119, 5272, 7854, 2035, 3254, 301, 9387, 5412, 9403, + 3029, 6749, 4880, 1485, 7157, 5629, 6151, 4478, 7699, + 7698, 6954, 7468, 2219, 5639, 6353, 506, 8308, 5821, + 2487, 7627, 3842, 7369, 6744, 2867, 7111, 7921, 1986, + 380, 9961, 4202, 9024, 661, 5897, 7449, 9845, 9461, + 9917, 234, 7756, 4104, 195, 2757, 4588, 1755, 600, + 3208, 9769, 495, 8241, 6634, 6218, 247, 649, 2255, + 6934, 5056, 3570, 5404, 4033, 4528, 6168, 3330, 5154, + 6668, 8969, 4990, 5914, 7294, 7798, 8937, 1984, 811, + 8267, 6000, 8441, 2901, 6504, 2951, 6191, 5592, 9657, + 5206, 4311, 4344, 6838, 4035, 8212, 9827, 8714, 8242, + 2597, 1268, 6941, 152, 4041, 7546, 5546, 9553, 8677, + 3838, 1475, 6605, 2849, 8979, 1585, 9524, 5732, 668, + 8770, 2014, 4555, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.386851072311401 seconds + +[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88] +[46.24] +14.305678367614746 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24} +[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88, 18.01, 17.95, 18.03, 17.68, 17.83, 17.84, 17.98, 17.81, 17.89, 18.12] +323.35999999999996 +16.168 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24, 'J_1KI': 2.935500848566434, 'W_1KI': 0.20519829770616352, 'W_D': 30.072000000000003, 'J_D': 430.2003598709107, 'W_D_1KI': 0.1334498963801849, 'J_D_1KI': 0.0005922078625925141} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..d7d5020 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.995913743972778, "TIME_S_1KI": 13.995913743972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 871.8022893977165, "W": 48.71, "J_1KI": 871.8022893977165, "W_1KI": 48.71, "W_D": 32.347, "J_D": 578.9404363610745, "W_D_1KI": 32.347, "J_D_1KI": 32.347} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..530a6a0 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.995913743972778} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 10, ..., 2499992, + 2499997, 2500000]), + col_indices=tensor([ 17718, 235055, 35243, ..., 14166, 348855, + 416543]), + values=tensor([0.0021, 0.9166, 0.2725, ..., 0.7498, 0.6792, 0.5299]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0136, 0.8273, 0.9896, ..., 0.5941, 0.9828, 0.6210]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 13.995913743972778 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 10, ..., 2499992, + 2499997, 2500000]), + col_indices=tensor([ 17718, 235055, 35243, ..., 14166, 348855, + 416543]), + values=tensor([0.0021, 0.9166, 0.2725, ..., 0.7498, 0.6792, 0.5299]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0136, 0.8273, 0.9896, ..., 0.5941, 0.9828, 0.6210]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 13.995913743972778 seconds + +[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99] +[48.71] +17.897809267044067 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71} +[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99, 18.27, 17.72, 18.53, 17.6, 18.07, 17.75, 17.78, 17.88, 17.93, 17.86] +327.26 +16.363 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71, 'J_1KI': 871.8022893977165, 'W_1KI': 48.71, 'W_D': 32.347, 'J_D': 578.9404363610745, 'W_D_1KI': 32.347, 'J_D_1KI': 32.347} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..d6c035a --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8984, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.250720024108887, "TIME_S_1KI": 1.1409973312676855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 667.435348098278, "W": 47.13, "J_1KI": 74.29155700114404, "W_1KI": 5.245992876224399, "W_D": 30.525750000000002, "J_D": 432.29290424805885, "W_D_1KI": 3.3977905164737314, "J_D_1KI": 0.37820464341871457} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..eb4a2d8 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1686315536499023} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 12, ..., 249984, 249988, + 250000]), + col_indices=tensor([ 9222, 11801, 17371, ..., 41613, 43396, 49641]), + values=tensor([0.5050, 0.7653, 0.0671, ..., 0.1421, 0.6855, 0.0275]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.2330, 0.0304, 0.5518, ..., 0.1557, 0.6263, 0.0730]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 1.1686315536499023 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', 'synthetic', 'csr', '8984', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.250720024108887} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 7, ..., 249994, 249997, + 250000]), + col_indices=tensor([ 1146, 2450, 11327, ..., 241, 2629, 25085]), + values=tensor([0.2696, 0.3732, 0.9366, ..., 0.5943, 0.0784, 0.3144]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.7229, 0.2746, 0.7643, ..., 0.7812, 0.8470, 0.7243]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.250720024108887 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 7, ..., 249994, 249997, + 250000]), + col_indices=tensor([ 1146, 2450, 11327, ..., 241, 2629, 25085]), + values=tensor([0.2696, 0.3732, 0.9366, ..., 0.5943, 0.0784, 0.3144]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.7229, 0.2746, 0.7643, ..., 0.7812, 0.8470, 0.7243]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.250720024108887 seconds + +[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1] +[47.13] +14.161581754684448 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13} +[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1, 21.63, 17.85, 18.41, 18.24, 17.96, 18.09, 18.1, 17.94, 18.92, 17.83] +332.08500000000004 +16.60425 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13, 'J_1KI': 74.29155700114404, 'W_1KI': 5.245992876224399, 'W_D': 30.525750000000002, 'J_D': 432.29290424805885, 'W_D_1KI': 3.3977905164737314, 'J_D_1KI': 0.37820464341871457} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..6747ace --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1969, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.35115671157837, "TIME_S_1KI": 5.25706282964874, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.7024494171142, "W": 48.48, "J_1KI": 369.5797102169193, "W_1KI": 24.62163534789233, "W_D": 32.2385, "J_D": 483.9116216075421, "W_D_1KI": 16.373031995937023, "J_D_1KI": 8.315404771933482} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..edef138 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.33142876625061} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 50, 102, ..., 2499894, + 2499942, 2500000]), + col_indices=tensor([ 362, 476, 734, ..., 42817, 42901, 48624]), + values=tensor([0.1861, 0.1141, 0.9529, ..., 0.0521, 0.7769, 0.3485]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6800, 0.1652, 0.7606, ..., 0.1973, 0.6571, 0.7552]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 5.33142876625061 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', 'synthetic', 'csr', '1969', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.35115671157837} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 59, 106, ..., 2499882, + 2499933, 2500000]), + col_indices=tensor([ 752, 1386, 1561, ..., 49182, 49404, 49846]), + values=tensor([0.0219, 0.4602, 0.8212, ..., 0.9720, 0.3228, 0.9373]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3378, 0.8054, 0.7422, ..., 0.6857, 0.1927, 0.4134]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.35115671157837 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 59, 106, ..., 2499882, + 2499933, 2500000]), + col_indices=tensor([ 752, 1386, 1561, ..., 49182, 49404, 49846]), + values=tensor([0.0219, 0.4602, 0.8212, ..., 0.9720, 0.3228, 0.9373]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3378, 0.8054, 0.7422, ..., 0.6857, 0.1927, 0.4134]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.35115671157837 seconds + +[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51] +[48.48] +15.010364055633545 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48} +[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51, 18.08, 17.97, 18.18, 18.15, 17.97, 18.08, 18.16, 17.86, 18.05, 17.82] +324.83 +16.2415 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48, 'J_1KI': 369.5797102169193, 'W_1KI': 24.62163534789233, 'W_D': 32.2385, 'J_D': 483.9116216075421, 'W_D_1KI': 16.373031995937023, 'J_D_1KI': 8.315404771933482} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..91b7ae2 --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21352, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.102294206619263, "TIME_S_1KI": 0.47313105126542065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 644.554582464695, "W": 46.45, "J_1KI": 30.187082355971103, "W_1KI": 2.1754402397901837, "W_D": 30.18325, "J_D": 418.83212273794413, "W_D_1KI": 1.4136029411764706, "J_D_1KI": 0.066204708747493} diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..c1ed6fd --- /dev/null +++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.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', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.49173688888549805} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([ 851, 39596, 1204, ..., 6262, 34652, 46359]), + values=tensor([0.1009, 0.2308, 0.6894, ..., 0.4766, 0.7010, 0.2687]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.2088, 0.1405, 0.6063, ..., 0.1063, 0.3954, 0.8044]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.49173688888549805 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', 'synthetic', 'csr', '21352', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.102294206619263} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24998, 24998, 25000]), + col_indices=tensor([15528, 30130, 16433, ..., 30917, 35420, 44166]), + values=tensor([0.6196, 0.0183, 0.2015, ..., 0.9265, 0.2661, 0.3216]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.3139, 0.2113, 0.1225, ..., 0.3436, 0.4255, 0.1892]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.102294206619263 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24998, 24998, 25000]), + col_indices=tensor([15528, 30130, 16433, ..., 30917, 35420, 44166]), + values=tensor([0.6196, 0.0183, 0.2015, ..., 0.9265, 0.2661, 0.3216]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.3139, 0.2113, 0.1225, ..., 0.3436, 0.4255, 0.1892]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.102294206619263 seconds + +[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12] +[46.45] +13.876309633255005 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45} +[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12, 18.35, 17.9, 17.93, 17.87, 17.99, 17.82, 18.04, 18.01, 19.31, 17.99] +325.33500000000004 +16.266750000000002 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45, 'J_1KI': 30.187082355971103, 'W_1KI': 2.1754402397901837, 'W_D': 30.18325, 'J_D': 418.83212273794413, 'W_D_1KI': 1.4136029411764706, 'J_D_1KI': 0.066204708747493} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..024170e --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.361413717269897, "TIME_S_1KI": 24.361413717269897, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 676.3945213317871, "W": 23.115576130225318, "J_1KI": 676.3945213317871, "W_1KI": 23.115576130225318, "W_D": 4.813576130225318, "J_D": 140.85206027984617, "W_D_1KI": 4.813576130225318, "J_D_1KI": 4.813576130225318} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..f9e5f4b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.361413717269897} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 21, ..., 999974, + 999988, 1000000]), + col_indices=tensor([ 9500, 9994, 42112, ..., 68909, 84086, 93735]), + values=tensor([0.6307, 0.9197, 0.7409, ..., 0.9841, 0.2812, 0.5553]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0568, 0.5322, 0.2500, ..., 0.3574, 0.0150, 0.2325]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 24.361413717269897 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 21, ..., 999974, + 999988, 1000000]), + col_indices=tensor([ 9500, 9994, 42112, ..., 68909, 84086, 93735]), + values=tensor([0.6307, 0.9197, 0.7409, ..., 0.9841, 0.2812, 0.5553]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0568, 0.5322, 0.2500, ..., 0.3574, 0.0150, 0.2325]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 24.361413717269897 seconds + +[20.76, 20.76, 20.72, 20.72, 20.52, 20.4, 20.8, 20.84, 20.72, 21.04] +[20.92, 20.48, 23.92, 23.92, 25.2, 26.52, 27.64, 28.16, 25.08, 24.44, 24.2, 24.28, 24.28, 24.36, 24.6, 24.64, 24.84, 24.92, 25.08, 24.84, 24.8, 25.16, 25.12, 25.12, 24.92, 25.2, 25.24, 24.92] +29.261417388916016 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.361413717269897, 'TIME_S_1KI': 24.361413717269897, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 676.3945213317871, 'W': 23.115576130225318} +[20.76, 20.76, 20.72, 20.72, 20.52, 20.4, 20.8, 20.84, 20.72, 21.04, 20.12, 20.08, 20.12, 20.04, 19.88, 19.88, 19.92, 19.88, 19.8, 20.0] +366.04 +18.302 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.361413717269897, 'TIME_S_1KI': 24.361413717269897, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 676.3945213317871, 'W': 23.115576130225318, 'J_1KI': 676.3945213317871, 'W_1KI': 23.115576130225318, 'W_D': 4.813576130225318, 'J_D': 140.85206027984617, 'W_D_1KI': 4.813576130225318, 'J_D_1KI': 4.813576130225318} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json new file mode 100644 index 0000000..def7a28 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 117.5588891506195, "TIME_S_1KI": 117.5588891506195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2923.375952148438, "W": 23.010596725488792, "J_1KI": 2923.375952148438, "W_1KI": 23.010596725488792, "W_D": 4.534596725488793, "J_D": 576.0967947998054, "W_D_1KI": 4.534596725488793, "J_D_1KI": 4.534596725488793} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output new file mode 100644 index 0000000..1edb066 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 117.5588891506195} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 59, 119, ..., 4999916, + 4999957, 5000000]), + col_indices=tensor([ 1403, 2005, 2494, ..., 97036, 97364, 98409]), + values=tensor([0.0186, 0.2433, 0.9960, ..., 0.9635, 0.2941, 0.9283]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.8104, 0.5597, 0.5404, ..., 0.7369, 0.5622, 0.9637]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 117.5588891506195 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 59, 119, ..., 4999916, + 4999957, 5000000]), + col_indices=tensor([ 1403, 2005, 2494, ..., 97036, 97364, 98409]), + values=tensor([0.0186, 0.2433, 0.9960, ..., 0.9635, 0.2941, 0.9283]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.8104, 0.5597, 0.5404, ..., 0.7369, 0.5622, 0.9637]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 117.5588891506195 seconds + +[20.12, 20.28, 20.08, 20.04, 20.04, 20.04, 20.32, 20.4, 20.48, 20.6] +[20.4, 20.36, 21.2, 22.2, 24.2, 26.08, 27.2, 26.48, 26.32, 25.16, 24.32, 24.48, 24.48, 24.52, 24.56, 24.4, 24.36, 24.28, 24.48, 24.48, 24.48, 24.2, 24.2, 24.32, 24.2, 24.36, 24.48, 24.2, 24.24, 24.2, 24.24, 24.36, 24.36, 24.44, 24.44, 24.56, 24.56, 24.56, 24.64, 24.84, 24.68, 24.72, 24.52, 24.6, 24.64, 24.36, 24.4, 24.36, 24.28, 24.16, 24.2, 24.48, 24.64, 24.56, 24.44, 24.44, 24.44, 24.48, 24.4, 24.68, 24.44, 24.28, 24.2, 24.24, 24.28, 24.32, 24.2, 24.48, 24.32, 24.24, 24.36, 24.32, 24.16, 24.0, 23.92, 23.72, 23.64, 23.68, 23.68, 23.68, 23.76, 24.0, 24.04, 24.16, 24.4, 24.36, 24.48, 24.76, 24.76, 24.76, 24.64, 24.48, 24.28, 24.44, 24.48, 24.6, 24.52, 24.52, 24.44, 24.4, 24.4, 24.32, 24.36, 24.24, 24.2, 24.2, 24.48, 24.64, 24.48, 24.72, 24.52, 24.24, 24.36, 24.24, 24.24, 24.52, 24.6, 24.44, 24.68, 24.6, 24.48] +127.04476928710938 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 117.5588891506195, 'TIME_S_1KI': 117.5588891506195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2923.375952148438, 'W': 23.010596725488792} +[20.12, 20.28, 20.08, 20.04, 20.04, 20.04, 20.32, 20.4, 20.48, 20.6, 21.08, 20.92, 20.92, 21.08, 20.84, 20.88, 20.8, 20.84, 20.44, 20.44] +369.52 +18.476 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 117.5588891506195, 'TIME_S_1KI': 117.5588891506195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2923.375952148438, 'W': 23.010596725488792, 'J_1KI': 2923.375952148438, 'W_1KI': 23.010596725488792, 'W_D': 4.534596725488793, 'J_D': 576.0967947998054, 'W_D_1KI': 4.534596725488793, 'J_D_1KI': 4.534596725488793} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json new file mode 100644 index 0000000..78b2e2b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 230.9039385318756, "TIME_S_1KI": 230.9039385318756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5761.600697250372, "W": 23.455047609770336, "J_1KI": 5761.600697250372, "W_1KI": 23.455047609770336, "W_D": 5.099047609770334, "J_D": 1252.5524037532862, "W_D_1KI": 5.099047609770334, "J_D_1KI": 5.099047609770334} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output new file mode 100644 index 0000000..1a5d909 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 230.9039385318756} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 89, 166, ..., 9999787, + 9999913, 10000000]), + col_indices=tensor([ 196, 231, 588, ..., 93210, 94069, 96596]), + values=tensor([0.2369, 0.0996, 0.5969, ..., 0.6003, 0.9136, 0.6152]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9669, 0.8246, 0.8261, ..., 0.7936, 0.5607, 0.9848]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 230.9039385318756 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 89, 166, ..., 9999787, + 9999913, 10000000]), + col_indices=tensor([ 196, 231, 588, ..., 93210, 94069, 96596]), + values=tensor([0.2369, 0.0996, 0.5969, ..., 0.6003, 0.9136, 0.6152]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9669, 0.8246, 0.8261, ..., 0.7936, 0.5607, 0.9848]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 230.9039385318756 seconds + +[20.44, 20.56, 20.52, 20.52, 20.52, 20.6, 20.56, 20.68, 20.84, 20.88] +[21.08, 20.84, 21.6, 22.44, 24.12, 25.68, 27.16, 27.72, 27.68, 27.68, 26.88, 25.92, 25.32, 24.84, 24.64, 24.56, 24.56, 24.6, 24.64, 24.64, 24.8, 24.64, 24.44, 24.44, 24.44, 24.28, 24.6, 24.8, 24.68, 24.56, 24.44, 24.44, 24.6, 24.52, 24.72, 24.48, 24.48, 24.36, 24.24, 24.24, 24.4, 24.32, 24.64, 24.68, 24.64, 24.84, 24.4, 24.44, 24.52, 24.32, 24.48, 24.52, 24.6, 24.6, 24.48, 24.72, 24.56, 24.56, 24.64, 24.76, 24.72, 24.84, 25.04, 24.84, 24.92, 24.72, 24.4, 24.4, 24.6, 24.52, 24.4, 24.6, 24.32, 24.4, 24.48, 24.48, 24.6, 24.76, 25.0, 25.04, 25.0, 24.68, 24.32, 24.44, 24.4, 24.4, 24.56, 24.52, 24.56, 24.52, 24.56, 24.6, 24.84, 24.8, 24.92, 24.8, 24.52, 24.52, 24.84, 24.8, 24.8, 24.6, 24.36, 24.36, 24.56, 24.44, 24.72, 24.76, 24.52, 24.56, 24.72, 24.76, 24.84, 24.96, 25.0, 24.76, 24.96, 25.08, 24.72, 24.72, 24.68, 24.6, 24.52, 24.6, 24.68, 24.88, 24.8, 24.84, 24.88, 24.8, 24.88, 24.92, 24.96, 24.96, 24.8, 24.72, 24.88, 24.96, 25.28, 25.12, 25.08, 25.08, 25.0, 24.52, 24.56, 24.48, 24.52, 24.56, 24.6, 24.6, 24.56, 24.56, 24.52, 24.6, 24.8, 24.88, 24.76, 24.64, 24.52, 24.68, 24.6, 24.6, 24.52, 24.52, 24.44, 24.44, 24.24, 24.28, 24.32, 24.28, 24.6, 24.6, 24.76, 24.88, 24.88, 24.72, 24.72, 24.52, 24.6, 24.6, 24.56, 24.68, 24.92, 24.8, 24.84, 24.84, 24.8, 24.8, 24.44, 24.48, 24.48, 24.48, 24.52, 24.64, 24.56, 24.6, 24.52, 24.56, 24.48, 24.56, 24.48, 24.56, 24.48, 24.56, 24.64, 24.84, 24.88, 24.88, 24.72, 24.36, 24.48, 24.56, 24.8, 24.88, 24.96, 24.8, 24.48, 24.32, 24.2, 24.24, 24.2, 24.44, 24.6, 24.56, 24.88, 24.92, 24.84, 24.76, 24.48, 24.48, 24.52, 24.48, 24.72, 24.8] +245.6443829536438 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 230.9039385318756, 'TIME_S_1KI': 230.9039385318756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5761.600697250372, 'W': 23.455047609770336} +[20.44, 20.56, 20.52, 20.52, 20.52, 20.6, 20.56, 20.68, 20.84, 20.88, 20.84, 20.68, 20.52, 20.24, 19.96, 20.0, 19.76, 19.84, 20.12, 20.24] +367.12 +18.356 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 230.9039385318756, 'TIME_S_1KI': 230.9039385318756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5761.600697250372, 'W': 23.455047609770336, 'J_1KI': 5761.600697250372, 'W_1KI': 23.455047609770336, 'W_D': 5.099047609770334, 'J_D': 1252.5524037532862, 'W_D_1KI': 5.099047609770334, 'J_D_1KI': 5.099047609770334} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json new file mode 100644 index 0000000..19d74a9 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 1091.9541580677032, "TIME_S_1KI": 1091.9541580677032, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 27099.232660408, "W": 23.65811028389948, "J_1KI": 27099.232660408, "W_1KI": 23.65811028389948, "W_D": 5.30711028389948, "J_D": 6079.0407438294715, "W_D_1KI": 5.30711028389948, "J_D_1KI": 5.30711028389948} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output new file mode 100644 index 0000000..a379597 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 1091.9541580677032} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 520, 1009, ..., 49998961, + 49999487, 50000000]), + col_indices=tensor([ 172, 626, 631, ..., 99749, 99860, 99985]), + values=tensor([0.6669, 0.0843, 0.5498, ..., 0.0965, 0.4666, 0.0259]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.4596, 0.0337, 0.7880, ..., 0.9862, 0.8105, 0.6593]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 1091.9541580677032 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 520, 1009, ..., 49998961, + 49999487, 50000000]), + col_indices=tensor([ 172, 626, 631, ..., 99749, 99860, 99985]), + values=tensor([0.6669, 0.0843, 0.5498, ..., 0.0965, 0.4666, 0.0259]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.4596, 0.0337, 0.7880, ..., 0.9862, 0.8105, 0.6593]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 1091.9541580677032 seconds + +[20.56, 20.52, 20.4, 20.4, 20.44, 20.64, 20.52, 20.56, 20.56, 20.48] +[20.6, 20.68, 21.28, 23.72, 25.32, 26.16, 27.04, 28.16, 28.16, 27.72, 27.08, 27.72, 28.88, 28.92, 29.44, 29.92, 29.8, 28.28, 28.0, 27.84, 28.08, 28.48, 27.88, 27.44, 26.56, 25.84, 24.92, 24.92, 24.92, 24.72, 24.76, 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'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 1091.9541580677032, 'TIME_S_1KI': 1091.9541580677032, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 27099.232660408, 'W': 23.65811028389948, 'J_1KI': 27099.232660408, 'W_1KI': 23.65811028389948, 'W_D': 5.30711028389948, 'J_D': 6079.0407438294715, 'W_D_1KI': 5.30711028389948, 'J_D_1KI': 5.30711028389948} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..044dbd0 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.78993320465088, "TIME_S_1KI": 3.490857610485562, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 587.0853565216064, "W": 23.400196384120708, "J_1KI": 94.05404622262198, "W_1KI": 3.748829923761728, "W_D": 4.996196384120708, "J_D": 125.34910764312737, "W_D_1KI": 0.8004159538802801, "J_D_1KI": 0.12823068790135855} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..0c82a6f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.3640708923339844} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 99997, 99997, + 100000]), + col_indices=tensor([49077, 61829, 75773, ..., 9180, 24382, 73621]), + values=tensor([0.5511, 0.7896, 0.6815, ..., 0.2019, 0.1356, 0.1654]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.3810, 0.9981, 0.5438, ..., 0.4984, 0.5897, 0.5823]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 3.3640708923339844 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 synthetic csr 6242 -ss 100000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.78993320465088} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 99999, 100000, + 100000]), + col_indices=tensor([26056, 65660, 94841, ..., 43126, 80704, 3094]), + values=tensor([0.6558, 0.9729, 0.1332, ..., 0.9607, 0.6686, 0.0556]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0734, 0.1949, 0.9405, ..., 0.2088, 0.6775, 0.6290]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 21.78993320465088 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 99999, 100000, + 100000]), + col_indices=tensor([26056, 65660, 94841, ..., 43126, 80704, 3094]), + values=tensor([0.6558, 0.9729, 0.1332, ..., 0.9607, 0.6686, 0.0556]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0734, 0.1949, 0.9405, ..., 0.2088, 0.6775, 0.6290]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 21.78993320465088 seconds + +[20.36, 20.36, 20.28, 20.28, 20.56, 20.72, 20.88, 20.96, 21.04, 20.96] +[20.88, 20.92, 24.04, 25.0, 26.56, 27.48, 28.4, 25.72, 25.72, 25.84, 25.12, 25.12, 25.52, 25.44, 25.72, 25.68, 25.6, 25.28, 25.68, 25.68, 25.72, 25.6, 25.8, 25.48] +25.08890724182129 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.78993320465088, 'TIME_S_1KI': 3.490857610485562, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 587.0853565216064, 'W': 23.400196384120708} +[20.36, 20.36, 20.28, 20.28, 20.56, 20.72, 20.88, 20.96, 21.04, 20.96, 20.48, 20.2, 20.2, 20.12, 20.12, 20.24, 20.24, 20.32, 20.44, 20.44] +368.08 +18.404 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.78993320465088, 'TIME_S_1KI': 3.490857610485562, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 587.0853565216064, 'W': 23.400196384120708, 'J_1KI': 94.05404622262198, 'W_1KI': 3.748829923761728, 'W_D': 4.996196384120708, 'J_D': 125.34910764312737, 'W_D_1KI': 0.8004159538802801, 'J_D_1KI': 0.12823068790135855} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output new file mode 100644 index 0000000..82012e6 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output @@ -0,0 +1 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 300000 -sd 1e-05 -c 1'] diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..849e147 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 9462, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.88818907737732, "TIME_S_1KI": 2.2075870933605284, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 574.1408400344848, "W": 22.884833840996915, "J_1KI": 60.678592267436564, "W_1KI": 2.418604295180397, "W_D": 4.553833840996916, "J_D": 114.24780293416971, "W_D_1KI": 0.48127603477033565, "J_D_1KI": 0.05086409160540432} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..9831ac1 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.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 synthetic csr 1000 -ss 30000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.21927547454834} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 6, ..., 89992, 89995, 90000]), + col_indices=tensor([ 4135, 5257, 7346, ..., 19970, 20460, 23828]), + values=tensor([0.3812, 0.3967, 0.4332, ..., 0.7451, 0.5477, 0.3750]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.1252, 0.8924, 0.9038, ..., 0.5916, 0.3272, 0.4447]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 2.21927547454834 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 synthetic csr 9462 -ss 30000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.88818907737732} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 89998, 89998, 90000]), + col_indices=tensor([ 9013, 9207, 23498, ..., 264, 8481, 27073]), + values=tensor([0.3265, 0.9217, 0.2088, ..., 0.3044, 0.7404, 0.1795]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.1642, 0.6221, 0.4016, ..., 0.5731, 0.3090, 0.6430]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.88818907737732 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 89998, 89998, 90000]), + col_indices=tensor([ 9013, 9207, 23498, ..., 264, 8481, 27073]), + values=tensor([0.3265, 0.9217, 0.2088, ..., 0.3044, 0.7404, 0.1795]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.1642, 0.6221, 0.4016, ..., 0.5731, 0.3090, 0.6430]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.88818907737732 seconds + +[20.4, 20.6, 20.6, 20.76, 20.56, 20.6, 20.32, 20.12, 20.12, 20.2] +[20.04, 20.0, 20.84, 22.2, 23.8, 25.04, 26.28, 26.28, 26.08, 25.8, 25.32, 25.2, 25.16, 25.28, 26.0, 26.2, 25.92, 26.12, 25.76, 25.28, 25.48, 25.32, 25.56, 25.84] +25.08826780319214 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 9462, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.88818907737732, 'TIME_S_1KI': 2.2075870933605284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 574.1408400344848, 'W': 22.884833840996915} +[20.4, 20.6, 20.6, 20.76, 20.56, 20.6, 20.32, 20.12, 20.12, 20.2, 20.2, 20.16, 20.0, 19.92, 19.92, 20.12, 20.36, 20.6, 20.96, 21.0] +366.62 +18.331 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 9462, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.88818907737732, 'TIME_S_1KI': 2.2075870933605284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 574.1408400344848, 'W': 22.884833840996915, 'J_1KI': 60.678592267436564, 'W_1KI': 2.418604295180397, 'W_D': 4.553833840996916, 'J_D': 114.24780293416971, 'W_D_1KI': 0.48127603477033565, 'J_D_1KI': 0.05086409160540432} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json new file mode 100644 index 0000000..cc699f5 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 21.110622882843018, "TIME_S_1KI": 10.608353207458803, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 543.18098487854, "W": 22.61402761523156, "J_1KI": 272.95526878318594, "W_1KI": 11.363832972478171, "W_D": 4.532027615231559, "J_D": 108.8577083845138, "W_D_1KI": 2.27740081167415, "J_D_1KI": 1.1444225184292212} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output new file mode 100644 index 0000000..e29b500 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.0005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 10.55085802078247} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 14, 24, ..., 449973, 449985, + 450000]), + col_indices=tensor([ 46, 2006, 2283, ..., 27547, 29014, 29850]), + values=tensor([0.4834, 0.4450, 0.5507, ..., 0.7876, 0.9956, 0.8691]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.6061, 0.2390, 0.3325, ..., 0.9801, 0.7580, 0.8339]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 10.55085802078247 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 synthetic csr 1990 -ss 30000 -sd 0.0005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 21.110622882843018} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 20, 37, ..., 449971, 449989, + 450000]), + col_indices=tensor([ 278, 1146, 6158, ..., 22458, 26366, 27217]), + values=tensor([0.7620, 0.0882, 0.1659, ..., 0.8176, 0.5012, 0.2468]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.2139, 0.3171, 0.2720, ..., 0.8919, 0.1670, 0.7588]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 21.110622882843018 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 20, 37, ..., 449971, 449989, + 450000]), + col_indices=tensor([ 278, 1146, 6158, ..., 22458, 26366, 27217]), + values=tensor([0.7620, 0.0882, 0.1659, ..., 0.8176, 0.5012, 0.2468]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.2139, 0.3171, 0.2720, ..., 0.8919, 0.1670, 0.7588]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 21.110622882843018 seconds + +[20.0, 20.04, 20.08, 20.0, 20.0, 20.0, 20.0, 20.16, 20.16, 20.32] +[20.52, 20.72, 21.08, 25.24, 27.4, 28.2, 29.0, 26.36, 25.24, 24.04, 24.0, 24.0, 24.12, 24.4, 24.56, 24.72, 24.8, 24.84, 24.44, 24.4, 24.32, 24.32, 24.48] +24.01964807510376 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 21.110622882843018, 'TIME_S_1KI': 10.608353207458803, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 543.18098487854, 'W': 22.61402761523156} +[20.0, 20.04, 20.08, 20.0, 20.0, 20.0, 20.0, 20.16, 20.16, 20.32, 20.32, 20.2, 20.16, 19.96, 19.84, 20.04, 20.24, 20.24, 20.16, 20.08] +361.64 +18.082 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 21.110622882843018, 'TIME_S_1KI': 10.608353207458803, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 543.18098487854, 'W': 22.61402761523156, 'J_1KI': 272.95526878318594, 'W_1KI': 11.363832972478171, 'W_D': 4.532027615231559, 'J_D': 108.8577083845138, 'W_D_1KI': 2.27740081167415, 'J_D_1KI': 1.1444225184292212} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..31f0e20 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1067, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.000109434127808, "TIME_S_1KI": 19.681452140700852, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 559.3280275917053, "W": 22.321542174679774, "J_1KI": 524.2062114261531, "W_1KI": 20.919908317413096, "W_D": 3.8025421746797754, "J_D": 95.2832200281621, "W_D_1KI": 3.5637696107589276, "J_D_1KI": 3.3399902631292666} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..2362809 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 19.667322635650635} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 32, 62, ..., 899942, 899975, + 900000]), + col_indices=tensor([ 740, 1042, 1045, ..., 28033, 28173, 29596]), + values=tensor([0.2730, 0.0823, 0.1244, ..., 0.0611, 0.7750, 0.7520]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.9441, 0.9003, 0.6345, ..., 0.2976, 0.9481, 0.5370]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 19.667322635650635 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 synthetic csr 1067 -ss 30000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.000109434127808} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 61, ..., 899954, 899977, + 900000]), + col_indices=tensor([ 498, 561, 1389, ..., 26094, 29069, 29804]), + values=tensor([0.7571, 0.4869, 0.1051, ..., 0.0359, 0.9032, 0.3458]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.6710, 0.3662, 0.6537, ..., 0.7839, 0.4339, 0.5677]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 21.000109434127808 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 61, ..., 899954, 899977, + 900000]), + col_indices=tensor([ 498, 561, 1389, ..., 26094, 29069, 29804]), + values=tensor([0.7571, 0.4869, 0.1051, ..., 0.0359, 0.9032, 0.3458]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.6710, 0.3662, 0.6537, ..., 0.7839, 0.4339, 0.5677]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 21.000109434127808 seconds + +[20.36, 20.36, 20.6, 20.64, 20.68, 20.56, 20.56, 20.64, 20.44, 20.44] +[20.44, 20.36, 21.72, 22.8, 24.84, 24.84, 25.76, 26.48, 25.64, 24.72, 24.68, 24.32, 24.2, 24.4, 24.4, 24.28, 24.4, 24.36, 24.52, 24.6, 24.68, 24.64, 24.68, 24.72] +25.05776810646057 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1067, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.000109434127808, 'TIME_S_1KI': 19.681452140700852, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 559.3280275917053, 'W': 22.321542174679774} +[20.36, 20.36, 20.6, 20.64, 20.68, 20.56, 20.56, 20.64, 20.44, 20.44, 20.16, 20.36, 20.36, 20.72, 20.8, 20.96, 20.68, 20.84, 20.56, 20.28] +370.37999999999994 +18.519 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1067, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.000109434127808, 'TIME_S_1KI': 19.681452140700852, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 559.3280275917053, 'W': 22.321542174679774, 'J_1KI': 524.2062114261531, 'W_1KI': 20.919908317413096, 'W_D': 3.8025421746797754, 'J_D': 95.2832200281621, 'W_D_1KI': 3.5637696107589276, 'J_D_1KI': 3.3399902631292666} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json new file mode 100644 index 0000000..883d2cb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 96.94969439506531, "TIME_S_1KI": 96.94969439506531, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2378.374522399904, "W": 23.364920168223726, "J_1KI": 2378.374522399904, "W_1KI": 23.364920168223726, "W_D": 5.238920168223725, "J_D": 533.2829799237265, "W_D_1KI": 5.238920168223725, "J_D_1KI": 5.238920168223725} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output new file mode 100644 index 0000000..9061dbb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 96.94969439506531} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 143, 286, ..., 4499692, + 4499854, 4500000]), + col_indices=tensor([ 245, 443, 986, ..., 29592, 29945, 29961]), + values=tensor([0.6844, 0.8171, 0.9701, ..., 0.0838, 0.2528, 0.1757]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.4329, 0.8636, 0.1677, ..., 0.1956, 0.5933, 0.9265]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 96.94969439506531 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 143, 286, ..., 4499692, + 4499854, 4500000]), + col_indices=tensor([ 245, 443, 986, ..., 29592, 29945, 29961]), + values=tensor([0.6844, 0.8171, 0.9701, ..., 0.0838, 0.2528, 0.1757]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.4329, 0.8636, 0.1677, ..., 0.1956, 0.5933, 0.9265]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 96.94969439506531 seconds + +[20.28, 20.48, 20.32, 20.24, 20.08, 20.28, 20.2, 20.08, 20.24, 20.04] +[20.04, 20.12, 20.0, 23.8, 25.0, 27.52, 28.72, 29.16, 26.24, 24.8, 24.4, 24.48, 24.72, 24.6, 24.44, 24.36, 24.28, 24.16, 24.24, 24.24, 24.24, 24.28, 24.4, 24.48, 24.6, 24.8, 24.6, 24.4, 24.48, 24.44, 24.44, 24.56, 24.36, 24.56, 24.84, 24.56, 24.6, 24.4, 24.36, 24.6, 24.8, 25.16, 25.16, 25.04, 24.76, 24.92, 24.68, 24.48, 24.44, 24.44, 24.44, 24.36, 24.48, 24.64, 24.56, 24.72, 24.72, 24.52, 24.64, 24.6, 24.32, 24.64, 24.56, 24.44, 24.44, 24.4, 24.36, 24.28, 24.52, 24.64, 24.68, 24.64, 24.6, 24.4, 24.2, 24.52, 24.64, 24.84, 25.12, 24.92, 24.76, 24.76, 24.68, 24.84, 24.76, 24.76, 24.76, 24.64, 24.52, 24.32, 24.44, 24.4, 24.28, 24.28, 24.48, 24.24, 24.32] +101.79253792762756 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 96.94969439506531, 'TIME_S_1KI': 96.94969439506531, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2378.374522399904, 'W': 23.364920168223726} +[20.28, 20.48, 20.32, 20.24, 20.08, 20.28, 20.2, 20.08, 20.24, 20.04, 20.36, 20.44, 20.08, 20.08, 20.0, 19.84, 19.84, 19.96, 19.96, 20.12] +362.52000000000004 +18.126 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 96.94969439506531, 'TIME_S_1KI': 96.94969439506531, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2378.374522399904, 'W': 23.364920168223726, 'J_1KI': 2378.374522399904, 'W_1KI': 23.364920168223726, 'W_D': 5.238920168223725, 'J_D': 533.2829799237265, 'W_D_1KI': 5.238920168223725, 'J_D_1KI': 5.238920168223725} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json new file mode 100644 index 0000000..de9aa95 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 192.67980027198792, "TIME_S_1KI": 192.67980027198792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4713.865335979459, "W": 23.38761944190409, "J_1KI": 4713.865335979459, "W_1KI": 23.38761944190409, "W_D": 5.0876194419040885, "J_D": 1025.4294153118105, "W_D_1KI": 5.0876194419040885, "J_D_1KI": 5.0876194419040885} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output new file mode 100644 index 0000000..111912f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.01 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 192.67980027198792} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 280, 537, ..., 8999446, + 8999733, 9000000]), + col_indices=tensor([ 104, 197, 254, ..., 29816, 29922, 29974]), + values=tensor([0.4269, 0.5481, 0.4506, ..., 0.7600, 0.9930, 0.8353]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.6961, 0.8979, 0.5119, ..., 0.0794, 0.6244, 0.2452]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 192.67980027198792 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 280, 537, ..., 8999446, + 8999733, 9000000]), + col_indices=tensor([ 104, 197, 254, ..., 29816, 29922, 29974]), + values=tensor([0.4269, 0.5481, 0.4506, ..., 0.7600, 0.9930, 0.8353]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.6961, 0.8979, 0.5119, ..., 0.0794, 0.6244, 0.2452]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 192.67980027198792 seconds + +[20.36, 20.16, 20.08, 20.2, 20.36, 20.44, 20.48, 20.28, 20.28, 20.04] +[20.04, 20.4, 20.56, 25.0, 25.72, 28.12, 29.56, 28.28, 27.64, 26.76, 25.6, 24.84, 24.28, 24.32, 24.32, 24.32, 24.48, 24.52, 24.52, 24.48, 24.56, 24.88, 24.76, 24.8, 24.84, 24.76, 24.52, 24.68, 24.6, 24.56, 24.6, 24.56, 24.52, 24.52, 24.56, 24.72, 24.76, 24.76, 24.88, 24.84, 24.84, 24.56, 24.56, 24.72, 24.6, 24.52, 24.52, 24.48, 24.36, 24.4, 24.48, 24.6, 24.64, 24.64, 24.48, 24.56, 24.48, 24.56, 24.44, 24.28, 24.04, 23.96, 23.96, 23.92, 24.04, 24.24, 24.52, 24.76, 24.84, 24.72, 24.6, 24.72, 24.8, 24.76, 24.8, 24.64, 24.52, 24.52, 24.56, 24.56, 24.52, 24.56, 24.6, 24.72, 24.72, 24.6, 24.76, 24.88, 24.52, 24.68, 24.8, 24.72, 24.64, 24.72, 24.32, 24.16, 24.24, 24.2, 24.48, 24.68, 24.6, 24.72, 24.68, 24.56, 24.52, 24.8, 24.8, 24.68, 24.88, 24.88, 24.52, 24.56, 24.56, 24.56, 24.72, 24.72, 24.6, 24.56, 24.52, 24.76, 24.92, 24.96, 24.92, 24.88, 24.88, 24.8, 24.76, 24.48, 24.48, 24.68, 24.44, 24.64, 24.68, 24.72, 24.56, 24.6, 24.36, 24.32, 24.2, 24.24, 24.16, 24.24, 24.32, 24.52, 24.52, 24.44, 24.48, 24.12, 23.96, 23.92, 23.92, 24.08, 24.12, 24.44, 24.6, 24.48, 24.44, 24.64, 24.48, 24.4, 24.44, 24.28, 24.24, 24.52, 24.56, 24.6, 24.8, 24.68, 24.68, 24.84, 24.88, 24.84, 24.84, 24.72, 24.64, 24.68, 24.52, 24.16, 24.32, 24.32, 24.32, 24.28, 24.72, 24.84, 24.72, 24.8, 24.92, 24.6, 24.72, 24.68, 24.64, 24.6] +201.55387544631958 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 192.67980027198792, 'TIME_S_1KI': 192.67980027198792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4713.865335979459, 'W': 23.38761944190409} +[20.36, 20.16, 20.08, 20.2, 20.36, 20.44, 20.48, 20.28, 20.28, 20.04, 20.44, 20.44, 20.24, 20.24, 20.2, 20.24, 20.56, 20.56, 20.56, 20.52] +366.0 +18.3 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 192.67980027198792, 'TIME_S_1KI': 192.67980027198792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4713.865335979459, 'W': 23.38761944190409, 'J_1KI': 4713.865335979459, 'W_1KI': 23.38761944190409, 'W_D': 5.0876194419040885, 'J_D': 1025.4294153118105, 'W_D_1KI': 5.0876194419040885, 'J_D_1KI': 5.0876194419040885} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json new file mode 100644 index 0000000..8e7a90d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 974.439944267273, "TIME_S_1KI": 974.439944267273, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 23454.716241874696, "W": 23.50814234111929, "J_1KI": 23454.716241874696, "W_1KI": 23.50814234111929, "W_D": 4.921142341119289, "J_D": 4909.958240083218, "W_D_1KI": 4.921142341119289, "J_D_1KI": 4.921142341119289} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output new file mode 100644 index 0000000..129f379 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 974.439944267273} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1561, 3045, ..., 44996950, + 44998508, 45000000]), + col_indices=tensor([ 5, 11, 69, ..., 29993, 29995, 29999]), + values=tensor([0.1916, 0.3634, 0.6366, ..., 0.4534, 0.7597, 0.1741]), + size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.1350, 0.9680, 0.5489, ..., 0.5585, 0.8579, 0.6858]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 45000000 +Density: 0.05 +Time: 974.439944267273 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1561, 3045, ..., 44996950, + 44998508, 45000000]), + col_indices=tensor([ 5, 11, 69, ..., 29993, 29995, 29999]), + values=tensor([0.1916, 0.3634, 0.6366, ..., 0.4534, 0.7597, 0.1741]), + size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.1350, 0.9680, 0.5489, ..., 0.5585, 0.8579, 0.6858]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 45000000 +Density: 0.05 +Time: 974.439944267273 seconds + +[20.36, 20.44, 20.52, 20.6, 20.8, 20.68, 20.76, 20.72, 21.08, 20.84] +[20.76, 20.32, 21.24, 21.24, 22.2, 25.24, 26.8, 27.64, 27.96, 29.52, 29.32, 29.6, 30.12, 29.4, 27.72, 27.76, 28.52, 28.68, 28.16, 27.28, 26.28, 25.36, 24.68, 24.64, 24.52, 24.52, 24.64, 24.64, 24.72, 24.6, 24.68, 24.8, 24.72, 24.72, 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900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 974.439944267273, 'TIME_S_1KI': 974.439944267273, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 23454.716241874696, 'W': 23.50814234111929, 'J_1KI': 23454.716241874696, 'W_1KI': 23.50814234111929, 'W_D': 4.921142341119289, 'J_D': 4909.958240083218, 'W_D_1KI': 4.921142341119289, 'J_D_1KI': 4.921142341119289} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json new file mode 100644 index 0000000..32cac99 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1889.6461987495422, "TIME_S_1KI": 1889.6461987495422, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 46014.76951049807, "W": 23.55154793120378, "J_1KI": 46014.76951049807, "W_1KI": 23.55154793120378, "W_D": 5.418547931203779, "J_D": 10586.702617774983, "W_D_1KI": 5.418547931203779, "J_D_1KI": 5.418547931203779} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output new file mode 100644 index 0000000..3244029 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.1 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1889.6461987495422} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2919, 6033, ..., 89993985, + 89996951, 90000000]), + col_indices=tensor([ 15, 22, 25, ..., 29928, 29955, 29961]), + values=tensor([0.1237, 0.9766, 0.2142, ..., 0.5188, 0.0654, 0.8458]), + size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.8545, 0.2049, 0.9446, ..., 0.6392, 0.0667, 0.5059]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000000 +Density: 0.1 +Time: 1889.6461987495422 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2919, 6033, ..., 89993985, + 89996951, 90000000]), + col_indices=tensor([ 15, 22, 25, ..., 29928, 29955, 29961]), + values=tensor([0.1237, 0.9766, 0.2142, ..., 0.5188, 0.0654, 0.8458]), + size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.8545, 0.2049, 0.9446, ..., 0.6392, 0.0667, 0.5059]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000000 +Density: 0.1 +Time: 1889.6461987495422 seconds + +[20.2, 20.08, 20.12, 19.96, 20.0, 20.0, 20.04, 20.04, 19.92, 20.12] +[20.08, 20.4, 20.64, 22.0, 23.52, 26.84, 27.8, 27.76, 27.76, 25.24, 26.52, 28.12, 30.6, 30.6, 32.76, 32.88, 32.32, 30.76, 28.68, 27.32, 27.48, 26.68, 27.08, 27.28, 27.36, 28.12, 28.64, 28.76, 28.48, 28.24, 28.24, 27.28, 26.68, 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20.2] +362.65999999999997 +18.133 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 1889.6461987495422, 'TIME_S_1KI': 1889.6461987495422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 46014.76951049807, 'W': 23.55154793120378, 'J_1KI': 46014.76951049807, 'W_1KI': 23.55154793120378, 'W_D': 5.418547931203779, 'J_D': 10586.702617774983, 'W_D_1KI': 5.418547931203779, 'J_D_1KI': 5.418547931203779} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..e115dec --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 53329, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.33877921104431, "TIME_S_1KI": 0.4001346211450489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 541.4117899322509, "W": 22.47116935228423, "J_1KI": 10.152295935274447, "W_1KI": 0.42136866155908104, "W_D": 4.265169352284232, "J_D": 102.76336478900909, "W_D_1KI": 0.07997842360224704, "J_D_1KI": 0.0014997172945723158} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..e2a45bb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.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 synthetic csr 1000 -ss 30000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.39377737045288086} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8999, 9000, 9000]), + col_indices=tensor([10394, 25541, 5557, ..., 25175, 23986, 28004]), + values=tensor([0.8334, 0.8462, 0.9277, ..., 0.8850, 0.0932, 0.4483]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3238, 0.7549, 0.3676, ..., 0.0953, 0.4629, 0.1375]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.39377737045288086 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 synthetic csr 53329 -ss 30000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.33877921104431} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8999, 9000, 9000]), + col_indices=tensor([19970, 26420, 19050, ..., 11684, 15529, 21908]), + values=tensor([0.5268, 0.0229, 0.8842, ..., 0.2264, 0.1987, 0.6579]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3153, 0.9988, 0.0667, ..., 0.8874, 0.8455, 0.6438]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.33877921104431 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 8999, 9000, 9000]), + col_indices=tensor([19970, 26420, 19050, ..., 11684, 15529, 21908]), + values=tensor([0.5268, 0.0229, 0.8842, ..., 0.2264, 0.1987, 0.6579]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3153, 0.9988, 0.0667, ..., 0.8874, 0.8455, 0.6438]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.33877921104431 seconds + +[20.04, 20.04, 20.12, 20.16, 20.4, 20.32, 20.4, 20.36, 20.2, 20.16] +[19.76, 19.88, 23.52, 24.72, 26.8, 26.8, 27.72, 28.52, 25.4, 24.2, 24.0, 24.12, 23.96, 24.08, 24.08, 23.92, 24.0, 24.0, 24.16, 24.28, 24.4, 24.44, 24.48] +24.093618869781494 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 53329, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.33877921104431, 'TIME_S_1KI': 0.4001346211450489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 541.4117899322509, 'W': 22.47116935228423} +[20.04, 20.04, 20.12, 20.16, 20.4, 20.32, 20.4, 20.36, 20.2, 20.16, 19.84, 19.92, 20.0, 20.12, 20.12, 20.44, 20.48, 20.48, 20.48, 20.12] +364.12 +18.206 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 53329, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.33877921104431, 'TIME_S_1KI': 0.4001346211450489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 541.4117899322509, 'W': 22.47116935228423, 'J_1KI': 10.152295935274447, 'W_1KI': 0.42136866155908104, 'W_D': 4.265169352284232, 'J_D': 102.76336478900909, 'W_D_1KI': 0.07997842360224704, 'J_D_1KI': 0.0014997172945723158} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..ddc6fac --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3411, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.881499767303467, "TIME_S_1KI": 6.121811717180729, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 532.9655063343048, "W": 22.190313426181827, "J_1KI": 156.24904905725734, "W_1KI": 6.505515516324194, "W_D": 3.8713134261818247, "J_D": 92.98095438027374, "W_D_1KI": 1.134949699848087, "J_D_1KI": 0.3327322485629103} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..19d6f4f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,66 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.156386375427246} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 12, ..., 249983, 249990, + 250000]), + col_indices=tensor([ 2925, 8906, 11132, ..., 41372, 46211, 46407]), + values=tensor([4.7685e-01, 8.2631e-01, 1.3241e-01, ..., + 9.9306e-01, 3.1562e-01, 2.9121e-04]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.6560, 0.4823, 0.8578, ..., 0.1247, 0.5626, 0.6108]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 6.156386375427246 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 synthetic csr 3411 -ss 50000 -sd 0.0001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.881499767303467} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 11, ..., 249987, 249992, + 250000]), + col_indices=tensor([ 5492, 16093, 20671, ..., 32727, 38238, 43452]), + values=tensor([0.3185, 0.0470, 0.4206, ..., 0.2062, 0.5185, 0.9595]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.4169, 0.1713, 0.4477, ..., 0.1220, 0.0527, 0.5400]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.881499767303467 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 11, ..., 249987, 249992, + 250000]), + col_indices=tensor([ 5492, 16093, 20671, ..., 32727, 38238, 43452]), + values=tensor([0.3185, 0.0470, 0.4206, ..., 0.2062, 0.5185, 0.9595]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.4169, 0.1713, 0.4477, ..., 0.1220, 0.0527, 0.5400]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.881499767303467 seconds + +[20.32, 20.28, 20.4, 20.4, 20.32, 20.4, 20.72, 20.56, 20.56, 20.56] +[20.52, 20.56, 20.8, 22.24, 23.32, 25.08, 26.04, 25.96, 25.92, 24.88, 25.0, 24.72, 24.6, 24.56, 24.12, 24.24, 24.24, 24.44, 24.56, 24.76, 24.96, 24.84, 24.84] +24.017935037612915 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.881499767303467, 'TIME_S_1KI': 6.121811717180729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 532.9655063343048, 'W': 22.190313426181827} +[20.32, 20.28, 20.4, 20.4, 20.32, 20.4, 20.72, 20.56, 20.56, 20.56, 20.52, 20.32, 20.2, 20.28, 20.36, 20.32, 20.28, 20.28, 20.08, 19.84] +366.38000000000005 +18.319000000000003 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.881499767303467, 'TIME_S_1KI': 6.121811717180729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 532.9655063343048, 'W': 22.190313426181827, 'J_1KI': 156.24904905725734, 'W_1KI': 6.505515516324194, 'W_D': 3.8713134261818247, 'J_D': 92.98095438027374, 'W_D_1KI': 1.134949699848087, 'J_D_1KI': 0.3327322485629103} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json new file mode 100644 index 0000000..e80940c --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 27.37552046775818, "TIME_S_1KI": 27.37552046775818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 709.9588974285126, "W": 22.617473628428268, "J_1KI": 709.9588974285126, "W_1KI": 22.617473628428268, "W_D": 4.155473628428268, "J_D": 130.43965581655505, "W_D_1KI": 4.155473628428268, "J_D_1KI": 4.155473628428268} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output new file mode 100644 index 0000000..5ee77f1 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 27.37552046775818} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 27, 46, ..., 1249943, + 1249977, 1250000]), + col_indices=tensor([ 1915, 6358, 8298, ..., 42036, 43103, 48835]), + values=tensor([0.4919, 0.4887, 0.0616, ..., 0.3370, 0.2927, 0.3892]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.0947, 0.0414, 0.5709, ..., 0.1435, 0.2486, 0.5038]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 27.37552046775818 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 27, 46, ..., 1249943, + 1249977, 1250000]), + col_indices=tensor([ 1915, 6358, 8298, ..., 42036, 43103, 48835]), + values=tensor([0.4919, 0.4887, 0.0616, ..., 0.3370, 0.2927, 0.3892]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.0947, 0.0414, 0.5709, ..., 0.1435, 0.2486, 0.5038]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 27.37552046775818 seconds + +[20.24, 20.24, 20.44, 20.44, 20.52, 20.84, 20.8, 20.8, 20.52, 20.48] +[20.44, 20.44, 21.24, 22.44, 23.88, 24.76, 25.84, 25.32, 25.32, 25.04, 24.24, 24.36, 24.44, 24.64, 24.84, 24.72, 24.52, 24.56, 24.28, 24.44, 24.4, 24.4, 24.44, 24.2, 24.08, 24.16, 24.08, 24.32, 24.52, 24.52] +31.389840841293335 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 27.37552046775818, 'TIME_S_1KI': 27.37552046775818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 709.9588974285126, 'W': 22.617473628428268} +[20.24, 20.24, 20.44, 20.44, 20.52, 20.84, 20.8, 20.8, 20.52, 20.48, 20.56, 20.56, 20.64, 20.6, 20.32, 20.36, 20.2, 20.32, 20.72, 20.56] +369.24 +18.462 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 27.37552046775818, 'TIME_S_1KI': 27.37552046775818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 709.9588974285126, 'W': 22.617473628428268, 'J_1KI': 709.9588974285126, 'W_1KI': 22.617473628428268, 'W_D': 4.155473628428268, 'J_D': 130.43965581655505, 'W_D_1KI': 4.155473628428268, 'J_D_1KI': 4.155473628428268} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..8992275 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.24125528335571, "TIME_S_1KI": 54.24125528335571, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1372.0545160293577, "W": 23.357171683962374, "J_1KI": 1372.0545160293577, "W_1KI": 23.357171683962374, "W_D": 5.006171683962375, "J_D": 294.0741524674891, "W_D_1KI": 5.006171683962375, "J_D_1KI": 5.006171683962375} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..1dd53bb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.24125528335571} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 50, 100, ..., 2499893, + 2499949, 2500000]), + col_indices=tensor([ 3726, 3738, 3891, ..., 47883, 48507, 49636]), + values=tensor([0.9449, 0.1440, 0.2391, ..., 0.6142, 0.1134, 0.3366]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1491, 0.4739, 0.9733, ..., 0.7895, 0.7265, 0.6840]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 54.24125528335571 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 50, 100, ..., 2499893, + 2499949, 2500000]), + col_indices=tensor([ 3726, 3738, 3891, ..., 47883, 48507, 49636]), + values=tensor([0.9449, 0.1440, 0.2391, ..., 0.6142, 0.1134, 0.3366]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1491, 0.4739, 0.9733, ..., 0.7895, 0.7265, 0.6840]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 54.24125528335571 seconds + +[20.52, 20.48, 20.52, 20.52, 20.48, 20.28, 20.36, 20.08, 20.2, 20.2] +[20.4, 20.56, 21.16, 25.2, 26.96, 28.36, 29.16, 26.04, 26.0, 26.0, 24.56, 24.48, 24.68, 24.64, 24.76, 24.8, 24.44, 24.48, 24.56, 24.36, 24.44, 24.6, 24.44, 24.44, 24.32, 24.2, 24.16, 24.24, 24.36, 24.44, 24.52, 24.52, 24.52, 24.52, 24.48, 24.44, 24.56, 24.56, 24.68, 24.76, 24.64, 24.64, 24.48, 24.36, 24.28, 24.16, 24.36, 24.48, 24.4, 24.6, 24.68, 24.6, 24.6, 24.6, 24.56, 24.16] +58.74232268333435 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.24125528335571, 'TIME_S_1KI': 54.24125528335571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1372.0545160293577, 'W': 23.357171683962374} +[20.52, 20.48, 20.52, 20.52, 20.48, 20.28, 20.36, 20.08, 20.2, 20.2, 20.4, 20.52, 20.32, 20.48, 20.6, 20.4, 20.44, 20.4, 20.24, 20.28] +367.02 +18.351 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.24125528335571, 'TIME_S_1KI': 54.24125528335571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1372.0545160293577, 'W': 23.357171683962374, 'J_1KI': 1372.0545160293577, 'W_1KI': 23.357171683962374, 'W_D': 5.006171683962375, 'J_D': 294.0741524674891, 'W_D_1KI': 5.006171683962375, 'J_D_1KI': 5.006171683962375} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json new file mode 100644 index 0000000..5b8cd5a --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 267.4636125564575, "TIME_S_1KI": 267.4636125564575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6714.934566097259, "W": 23.431218104999314, "J_1KI": 6714.934566097259, "W_1KI": 23.431218104999314, "W_D": 5.143218104999313, "J_D": 1473.9469744796756, "W_D_1KI": 5.143218104999313, "J_D_1KI": 5.143218104999313} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output new file mode 100644 index 0000000..d7cdb54 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.005 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 267.4636125564575} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 245, 505, ..., 12499495, + 12499738, 12500000]), + col_indices=tensor([ 233, 421, 423, ..., 49587, 49831, 49917]), + values=tensor([0.0085, 0.6781, 0.7487, ..., 0.0311, 0.6051, 0.4921]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.9687, 0.6648, 0.3251, ..., 0.0954, 0.1242, 0.9203]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 267.4636125564575 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 245, 505, ..., 12499495, + 12499738, 12500000]), + col_indices=tensor([ 233, 421, 423, ..., 49587, 49831, 49917]), + values=tensor([0.0085, 0.6781, 0.7487, ..., 0.0311, 0.6051, 0.4921]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.9687, 0.6648, 0.3251, ..., 0.0954, 0.1242, 0.9203]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 267.4636125564575 seconds + +[20.32, 20.36, 20.44, 20.44, 20.44, 20.6, 20.6, 20.64, 20.64, 20.8] +[20.68, 20.84, 21.36, 22.52, 24.16, 25.64, 27.64, 28.36, 28.44, 28.44, 27.88, 26.96, 25.68, 25.0, 24.68, 24.68, 24.88, 25.0, 25.08, 25.0, 24.72, 24.6, 24.68, 24.44, 24.32, 24.4, 24.32, 24.12, 24.36, 24.6, 24.6, 24.6, 24.48, 24.72, 24.76, 24.72, 24.84, 24.92, 24.72, 24.72, 24.6, 24.44, 24.56, 24.6, 24.36, 24.48, 24.56, 24.36, 24.36, 24.32, 24.4, 24.32, 24.44, 24.44, 24.6, 24.6, 24.56, 24.52, 24.28, 24.28, 24.32, 24.28, 24.52, 24.4, 24.44, 24.24, 24.12, 24.12, 24.32, 24.52, 24.8, 25.0, 25.04, 24.72, 24.72, 24.72, 24.56, 24.24, 24.24, 24.16, 24.12, 24.0, 24.16, 24.4, 24.4, 24.56, 24.68, 24.76, 24.68, 24.72, 24.56, 24.6, 24.68, 24.8, 24.84, 24.88, 24.84, 24.84, 24.72, 24.52, 24.48, 24.44, 24.36, 24.4, 24.56, 24.32, 24.28, 24.4, 24.52, 24.36, 24.76, 24.88, 24.8, 24.72, 24.84, 24.88, 24.84, 24.92, 24.88, 24.88, 24.8, 24.76, 24.68, 24.48, 24.52, 24.44, 24.44, 24.56, 24.56, 24.64, 24.52, 24.4, 24.6, 24.64, 24.72, 24.52, 24.36, 24.48, 24.48, 24.68, 24.56, 24.56, 24.88, 24.92, 24.96, 25.12, 25.12, 24.88, 24.6, 24.6, 24.4, 24.6, 24.88, 24.8, 24.6, 24.56, 24.48, 24.52, 24.68, 24.92, 24.96, 24.72, 24.76, 24.76, 24.6, 24.6, 24.64, 24.52, 24.48, 24.48, 24.6, 24.64, 24.6, 24.56, 24.56, 24.28, 24.44, 24.32, 24.24, 24.2, 24.12, 24.2, 24.44, 24.56, 24.48, 24.48, 24.44, 24.48, 24.64, 24.84, 24.84, 24.96, 24.68, 24.72, 24.44, 24.76, 24.72, 24.6, 24.72, 24.76, 24.6, 24.72, 25.08, 24.72, 24.88, 24.92, 25.0, 25.0, 25.04, 25.2, 25.08, 24.92, 24.56, 24.44, 24.48, 24.36, 24.64, 24.64, 24.68, 24.6, 24.44, 24.48, 24.28, 24.24, 24.4, 24.52, 24.4, 24.24, 24.4, 24.4, 24.36, 24.36, 24.56, 24.76, 24.76, 24.72, 24.8, 24.72, 24.64, 24.56, 24.44, 24.24, 24.32, 24.32, 24.6, 24.64, 24.6, 24.64, 24.68, 24.52, 24.8, 24.8, 24.8, 24.88, 24.76, 24.68, 24.64, 24.56, 24.6, 24.72, 24.64, 24.48, 24.56, 24.48, 24.48, 24.36, 24.44, 24.44, 24.56, 24.76, 24.68, 24.68, 24.72] +286.5806863307953 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 267.4636125564575, 'TIME_S_1KI': 267.4636125564575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6714.934566097259, 'W': 23.431218104999314} +[20.32, 20.36, 20.44, 20.44, 20.44, 20.6, 20.6, 20.64, 20.64, 20.8, 20.04, 20.04, 20.04, 19.92, 20.12, 20.08, 20.12, 20.16, 20.32, 20.44] +365.76 +18.288 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 267.4636125564575, 'TIME_S_1KI': 267.4636125564575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6714.934566097259, 'W': 23.431218104999314, 'J_1KI': 6714.934566097259, 'W_1KI': 23.431218104999314, 'W_D': 5.143218104999313, 'J_D': 1473.9469744796756, 'W_D_1KI': 5.143218104999313, 'J_D_1KI': 5.143218104999313} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..789132f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 535.8486552238464, "TIME_S_1KI": 535.8486552238464, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 13280.309822225587, "W": 23.46860300867941, "J_1KI": 13280.309822225587, "W_1KI": 23.46860300867941, "W_D": 4.95160300867941, "J_D": 2801.991326352374, "W_D_1KI": 4.95160300867941, "J_D_1KI": 4.95160300867941} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..209aa36 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 535.8486552238464} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 524, 1011, ..., 24999011, + 24999504, 25000000]), + col_indices=tensor([ 192, 200, 454, ..., 49935, 49965, 49995]), + values=tensor([0.7895, 0.2997, 0.5746, ..., 0.4223, 0.3918, 0.3456]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4665, 0.6238, 0.5276, ..., 0.6350, 0.6391, 0.4023]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 535.8486552238464 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 524, 1011, ..., 24999011, + 24999504, 25000000]), + col_indices=tensor([ 192, 200, 454, ..., 49935, 49965, 49995]), + values=tensor([0.7895, 0.2997, 0.5746, ..., 0.4223, 0.3918, 0.3456]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4665, 0.6238, 0.5276, ..., 0.6350, 0.6391, 0.4023]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 535.8486552238464 seconds + +[19.88, 20.2, 20.24, 20.52, 20.64, 20.6, 20.68, 20.56, 20.56, 20.64] +[20.64, 20.68, 20.44, 22.0, 22.56, 24.6, 26.28, 27.32, 28.6, 28.84, 29.2, 28.92, 29.32, 28.08, 27.28, 26.68, 26.0, 26.0, 24.96, 24.8, 24.88, 24.96, 24.84, 24.72, 24.48, 24.56, 24.6, 24.64, 24.56, 24.48, 24.48, 24.36, 24.48, 24.64, 24.52, 24.64, 24.44, 24.52, 24.36, 24.36, 24.44, 24.44, 24.56, 24.88, 24.92, 24.96, 24.96, 24.8, 24.64, 24.56, 24.48, 24.64, 24.56, 24.52, 24.64, 24.72, 24.84, 24.92, 24.88, 24.72, 24.68, 24.68, 24.8, 24.72, 24.56, 24.36, 24.16, 23.96, 24.08, 24.4, 24.52, 24.6, 24.76, 24.8, 24.92, 24.96, 24.84, 24.84, 24.6, 24.44, 24.52, 24.48, 24.64, 24.64, 25.0, 24.8, 24.88, 24.56, 24.52, 24.44, 24.56, 24.52, 24.72, 24.76, 24.44, 24.44, 24.56, 24.36, 24.4, 24.48, 24.44, 24.4, 24.64, 24.84, 24.72, 24.72, 24.6, 24.52, 24.28, 24.28, 24.52, 24.64, 24.72, 24.68, 24.56, 24.36, 24.48, 24.4, 24.28, 24.48, 24.52, 24.32, 24.56, 24.52, 24.4, 24.32, 24.36, 24.36, 24.32, 24.4, 24.44, 24.24, 24.4, 24.36, 24.28, 24.44, 24.4, 24.44, 24.4, 24.8, 24.68, 24.64, 24.72, 24.32, 24.08, 24.2, 24.48, 24.44, 24.6, 24.6, 24.44, 24.4, 24.48, 24.6, 24.52, 24.68, 24.52, 24.64, 24.76, 24.72, 24.64, 24.56, 24.48, 24.52, 24.76, 24.52, 24.76, 24.76, 24.88, 24.92, 24.96, 24.96, 24.68, 24.72, 24.64, 24.56, 24.52, 24.52, 24.52, 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24.52, 24.6, 24.56, 24.52, 24.52, 24.44, 24.6, 24.6, 24.44, 24.48, 24.68, 24.72, 24.84, 25.0, 24.96, 24.92, 24.96, 25.0, 24.8, 24.72, 24.56, 24.56, 24.2, 24.32, 24.36, 24.24, 24.24, 24.36, 24.28, 24.28, 24.52, 24.32, 24.36, 24.64, 24.72, 24.92, 24.92, 24.72, 24.72, 24.56, 24.56, 24.52, 24.6, 24.48, 24.76, 24.48, 24.6, 24.6, 24.6, 24.44, 24.44, 24.44, 24.6, 24.44, 24.6, 24.52, 24.8, 24.6, 24.52, 24.48, 24.24, 24.04, 24.28, 24.2, 24.32, 24.56, 24.68] +565.8756005764008 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 535.8486552238464, 'TIME_S_1KI': 535.8486552238464, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13280.309822225587, 'W': 23.46860300867941} +[19.88, 20.2, 20.24, 20.52, 20.64, 20.6, 20.68, 20.56, 20.56, 20.64, 20.6, 20.6, 20.48, 20.52, 20.32, 20.32, 20.6, 20.96, 21.36, 21.24] +370.34 +18.517 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 535.8486552238464, 'TIME_S_1KI': 535.8486552238464, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13280.309822225587, 'W': 23.46860300867941, 'J_1KI': 13280.309822225587, 'W_1KI': 23.46860300867941, 'W_D': 4.95160300867941, 'J_D': 2801.991326352374, 'W_D_1KI': 4.95160300867941, 'J_D_1KI': 4.95160300867941} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..918a20d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 1, "ITERATIONS": 21239, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.893531799316406, "TIME_S_1KI": 0.9837342529929096, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 567.132219734192, "W": 22.611941270368835, "J_1KI": 26.7023974638256, "W_1KI": 1.0646424629393492, "W_D": 4.100941270368832, "J_D": 102.85609262180327, "W_D_1KI": 0.19308542164738604, "J_D_1KI": 0.009091078753584728} diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..bf1327e --- /dev/null +++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.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 synthetic csr 1000 -ss 50000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.9887256622314453} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 25000, 25000]), + col_indices=tensor([37749, 5687, 46660, ..., 48444, 47762, 13606]), + values=tensor([0.6973, 0.6140, 0.4905, ..., 0.2540, 0.0834, 0.5554]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8263, 0.0637, 0.7656, ..., 0.0179, 0.5334, 0.7448]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.9887256622314453 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 synthetic csr 21239 -ss 50000 -sd 1e-05 -c 1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.893531799316406} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([17445, 34363, 49525, ..., 23738, 42338, 13045]), + values=tensor([0.8308, 0.1110, 0.1320, ..., 0.5346, 0.9645, 0.6427]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4506, 0.3540, 0.2946, ..., 0.2277, 0.1153, 0.5755]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 20.893531799316406 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([17445, 34363, 49525, ..., 23738, 42338, 13045]), + values=tensor([0.8308, 0.1110, 0.1320, ..., 0.5346, 0.9645, 0.6427]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4506, 0.3540, 0.2946, ..., 0.2277, 0.1153, 0.5755]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 20.893531799316406 seconds + +[20.28, 20.2, 20.12, 20.32, 20.4, 20.4, 20.52, 20.44, 20.28, 20.28] +[20.36, 20.36, 20.32, 21.48, 22.48, 24.48, 25.64, 26.08, 25.96, 25.2, 25.16, 25.16, 25.12, 25.12, 25.16, 25.36, 25.32, 25.24, 25.64, 25.64, 25.56, 25.72, 25.76, 25.8] +25.081093788146973 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 21239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.893531799316406, 'TIME_S_1KI': 0.9837342529929096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 567.132219734192, 'W': 22.611941270368835} +[20.28, 20.2, 20.12, 20.32, 20.4, 20.4, 20.52, 20.44, 20.28, 20.28, 20.6, 20.72, 20.84, 20.84, 20.72, 20.8, 20.8, 20.8, 20.96, 20.96] +370.22 +18.511000000000003 +{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 21239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.893531799316406, 'TIME_S_1KI': 0.9837342529929096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 567.132219734192, 'W': 22.611941270368835, 'J_1KI': 26.7023974638256, 'W_1KI': 1.0646424629393492, 'W_D': 4.100941270368832, 'J_D': 102.85609262180327, 'W_D_1KI': 0.19308542164738604, 'J_D_1KI': 0.009091078753584728} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json new file mode 100644 index 0000000..0c0f3d2 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 28.845969438552856, "TIME_S_1KI": 28.845969438552856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2585.6487583732605, "W": 78.16, "J_1KI": 2585.6487583732605, "W_1KI": 78.16, "W_D": 43.0085, "J_D": 1422.784987519145, "W_D_1KI": 43.0085, "J_D_1KI": 43.0085} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output new file mode 100644 index 0000000..89364a9 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '1000000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 28.845969438552856} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 13, ..., 9999985, + 9999990, 10000000]), + col_indices=tensor([129131, 466272, 498291, ..., 666802, 863606, + 946629]), + values=tensor([0.5704, 0.0489, 0.8998, ..., 0.0930, 0.7201, 0.2084]), + size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9798, 0.4611, 0.5869, ..., 0.0442, 0.2383, 0.1498]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([1000000, 1000000]) +Rows: 1000000 +Size: 1000000000000 +NNZ: 10000000 +Density: 1e-05 +Time: 28.845969438552856 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 13, ..., 9999985, + 9999990, 10000000]), + col_indices=tensor([129131, 466272, 498291, ..., 666802, 863606, + 946629]), + values=tensor([0.5704, 0.0489, 0.8998, ..., 0.0930, 0.7201, 0.2084]), + size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9798, 0.4611, 0.5869, ..., 0.0442, 0.2383, 0.1498]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([1000000, 1000000]) +Rows: 1000000 +Size: 1000000000000 +NNZ: 10000000 +Density: 1e-05 +Time: 28.845969438552856 seconds + +[40.14, 38.78, 39.11, 39.29, 39.32, 38.69, 39.29, 38.91, 39.05, 39.11] +[78.16] +33.081483602523804 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 28.845969438552856, 'TIME_S_1KI': 28.845969438552856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2585.6487583732605, 'W': 78.16} +[40.14, 38.78, 39.11, 39.29, 39.32, 38.69, 39.29, 38.91, 39.05, 39.11, 40.65, 38.68, 39.17, 39.0, 39.16, 39.21, 38.7, 38.72, 38.68, 38.64] +703.03 +35.1515 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 28.845969438552856, 'TIME_S_1KI': 28.845969438552856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2585.6487583732605, 'W': 78.16, 'J_1KI': 2585.6487583732605, 'W_1KI': 78.16, 'W_D': 43.0085, 'J_D': 1422.784987519145, 'W_D_1KI': 43.0085, 'J_D_1KI': 43.0085} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..11555aa --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12281, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.43535876274109, "TIME_S_1KI": 1.7454082536227578, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1544.854190015793, "W": 65.9, "J_1KI": 125.79221480464072, "W_1KI": 5.366012539695466, "W_D": 30.747500000000002, "J_D": 720.7952080047131, "W_D_1KI": 2.503664196726651, "J_D_1KI": 0.20386484787286466} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..a35639b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,66 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.7099568843841553} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 20, ..., 999975, + 999991, 1000000]), + col_indices=tensor([ 4154, 20798, 21409, ..., 65320, 83277, 90457]), + values=tensor([0.0206, 0.0188, 0.3875, ..., 0.2566, 0.8734, 0.4713]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([2.2552e-01, 8.2165e-04, 8.9899e-01, ..., 7.1003e-01, 6.8443e-02, + 6.7507e-01]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 1.7099568843841553 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12281', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.43535876274109} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 20, ..., 999985, + 999994, 1000000]), + col_indices=tensor([ 2661, 16984, 17010, ..., 72407, 79760, 99948]), + values=tensor([0.6261, 0.1903, 0.4758, ..., 0.9266, 0.4335, 0.5751]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.1330, 0.4019, 0.6390, ..., 0.8808, 0.7758, 0.9416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 21.43535876274109 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 20, ..., 999985, + 999994, 1000000]), + col_indices=tensor([ 2661, 16984, 17010, ..., 72407, 79760, 99948]), + values=tensor([0.6261, 0.1903, 0.4758, ..., 0.9266, 0.4335, 0.5751]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.1330, 0.4019, 0.6390, ..., 0.8808, 0.7758, 0.9416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 21.43535876274109 seconds + +[39.05, 39.08, 38.77, 38.37, 38.41, 38.56, 38.82, 38.92, 43.8, 38.57] +[65.9] +23.442400455474854 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12281, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.43535876274109, 'TIME_S_1KI': 1.7454082536227578, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1544.854190015793, 'W': 65.9} +[39.05, 39.08, 38.77, 38.37, 38.41, 38.56, 38.82, 38.92, 43.8, 38.57, 39.12, 38.73, 38.52, 39.3, 38.76, 38.93, 38.86, 38.9, 38.78, 38.34] +703.0500000000001 +35.1525 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12281, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.43535876274109, 'TIME_S_1KI': 1.7454082536227578, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1544.854190015793, 'W': 65.9, 'J_1KI': 125.79221480464072, 'W_1KI': 5.366012539695466, 'W_D': 30.747500000000002, 'J_D': 720.7952080047131, 'W_D_1KI': 2.503664196726651, 'J_D_1KI': 0.20386484787286466} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json new file mode 100644 index 0000000..0a9c6a6 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2942, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.68795394897461, "TIME_S_1KI": 7.03193540073916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1874.2356022763252, "W": 76.49, "J_1KI": 637.0617274902532, "W_1KI": 25.9993201903467, "W_D": 40.7645, "J_D": 998.8531469341516, "W_D_1KI": 13.856050305914344, "J_D_1KI": 4.709738377265243} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output new file mode 100644 index 0000000..5c997f3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 7.137338638305664} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 52, 98, ..., 4999908, + 4999951, 5000000]), + col_indices=tensor([ 774, 4471, 4915, ..., 92493, 94807, 99005]), + values=tensor([0.1957, 0.1752, 0.4711, ..., 0.3350, 0.8302, 0.4161]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5056, 0.9806, 0.9907, ..., 0.9600, 0.9702, 0.1169]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 7.137338638305664 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2942', '-ss', '100000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.68795394897461} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 45, 79, ..., 4999902, + 4999950, 5000000]), + col_indices=tensor([11504, 12222, 12883, ..., 96456, 97352, 97598]), + values=tensor([0.3754, 0.5479, 0.7533, ..., 0.2937, 0.0115, 0.1659]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4390, 0.1553, 0.7240, ..., 0.6581, 0.8843, 0.0193]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 20.68795394897461 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 45, 79, ..., 4999902, + 4999950, 5000000]), + col_indices=tensor([11504, 12222, 12883, ..., 96456, 97352, 97598]), + values=tensor([0.3754, 0.5479, 0.7533, ..., 0.2937, 0.0115, 0.1659]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4390, 0.1553, 0.7240, ..., 0.6581, 0.8843, 0.0193]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 20.68795394897461 seconds + +[40.56, 38.51, 39.43, 38.93, 54.59, 38.38, 38.52, 38.39, 39.6, 38.42] +[76.49] +24.50301480293274 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2942, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.68795394897461, 'TIME_S_1KI': 7.03193540073916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1874.2356022763252, 'W': 76.49} +[40.56, 38.51, 39.43, 38.93, 54.59, 38.38, 38.52, 38.39, 39.6, 38.42, 39.81, 38.45, 38.58, 38.39, 38.5, 38.48, 38.64, 39.27, 39.03, 38.85] +714.51 +35.7255 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2942, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.68795394897461, 'TIME_S_1KI': 7.03193540073916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1874.2356022763252, 'W': 76.49, 'J_1KI': 637.0617274902532, 'W_1KI': 25.9993201903467, 'W_D': 40.7645, 'J_D': 998.8531469341516, 'W_D_1KI': 13.856050305914344, 'J_D_1KI': 4.709738377265243} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json new file mode 100644 index 0000000..7cc2d82 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1260, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.127803564071655, "TIME_S_1KI": 16.768098066723535, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2030.2760949325561, "W": 77.88, "J_1KI": 1611.3302340734572, "W_1KI": 61.80952380952381, "W_D": 42.534, "J_D": 1108.8310660228728, "W_D_1KI": 33.75714285714285, "J_D_1KI": 26.791383219954643} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output new file mode 100644 index 0000000..56baef1 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.660033226013184} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 108, 211, ..., 9999825, + 9999911, 10000000]), + col_indices=tensor([ 2064, 2545, 2770, ..., 96472, 96974, 97481]), + values=tensor([0.9939, 0.7295, 0.6290, ..., 0.4583, 0.7573, 0.7957]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.0307, 0.5740, 0.3084, ..., 0.9686, 0.7857, 0.7900]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 16.660033226013184 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1260', '-ss', '100000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.127803564071655} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 87, 189, ..., 9999804, + 9999908, 10000000]), + col_indices=tensor([ 1134, 1351, 3464, ..., 96987, 97572, 98330]), + values=tensor([0.8017, 0.9469, 0.5440, ..., 0.1663, 0.6077, 0.2624]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9534, 0.2929, 0.7145, ..., 0.1886, 0.7155, 0.7573]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 21.127803564071655 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 87, 189, ..., 9999804, + 9999908, 10000000]), + col_indices=tensor([ 1134, 1351, 3464, ..., 96987, 97572, 98330]), + values=tensor([0.8017, 0.9469, 0.5440, ..., 0.1663, 0.6077, 0.2624]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.9534, 0.2929, 0.7145, ..., 0.1886, 0.7155, 0.7573]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 21.127803564071655 seconds + +[41.52, 38.75, 38.56, 39.98, 39.02, 38.4, 39.41, 44.36, 39.07, 39.11] +[77.88] +26.069287300109863 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.127803564071655, 'TIME_S_1KI': 16.768098066723535, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.2760949325561, 'W': 77.88} +[41.52, 38.75, 38.56, 39.98, 39.02, 38.4, 39.41, 44.36, 39.07, 39.11, 39.9, 38.94, 38.5, 38.44, 38.69, 38.62, 38.87, 38.86, 38.81, 38.75] +706.92 +35.346 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.127803564071655, 'TIME_S_1KI': 16.768098066723535, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.2760949325561, 'W': 77.88, 'J_1KI': 1611.3302340734572, 'W_1KI': 61.80952380952381, 'W_D': 42.534, 'J_D': 1108.8310660228728, 'W_D_1KI': 33.75714285714285, 'J_D_1KI': 26.791383219954643} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json new file mode 100644 index 0000000..378f4a9 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 80.02073836326599, "TIME_S_1KI": 80.02073836326599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7205.058165025711, "W": 78.13, "J_1KI": 7205.058165025711, "W_1KI": 78.13, "W_D": 42.671499999999995, "J_D": 3935.116338012218, "W_D_1KI": 42.671499999999995, "J_D_1KI": 42.671499999999995} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output new file mode 100644 index 0000000..bd8189d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 80.02073836326599} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 526, 1009, ..., 49999039, + 49999525, 50000000]), + col_indices=tensor([ 783, 789, 851, ..., 99387, 99562, 99965]), + values=tensor([0.0435, 0.6996, 0.0280, ..., 0.1403, 0.1144, 0.7500]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.9356, 0.8803, 0.8700, ..., 0.3387, 0.6442, 0.0455]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 80.02073836326599 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 526, 1009, ..., 49999039, + 49999525, 50000000]), + col_indices=tensor([ 783, 789, 851, ..., 99387, 99562, 99965]), + values=tensor([0.0435, 0.6996, 0.0280, ..., 0.1403, 0.1144, 0.7500]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.9356, 0.8803, 0.8700, ..., 0.3387, 0.6442, 0.0455]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 80.02073836326599 seconds + +[39.78, 38.73, 38.83, 38.73, 40.16, 38.51, 39.25, 38.91, 39.2, 38.87] +[78.13] +92.21884250640869 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 80.02073836326599, 'TIME_S_1KI': 80.02073836326599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7205.058165025711, 'W': 78.13} +[39.78, 38.73, 38.83, 38.73, 40.16, 38.51, 39.25, 38.91, 39.2, 38.87, 39.46, 38.64, 39.23, 44.76, 39.46, 39.36, 38.92, 38.68, 39.19, 39.11] +709.17 +35.4585 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 80.02073836326599, 'TIME_S_1KI': 80.02073836326599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7205.058165025711, 'W': 78.13, 'J_1KI': 7205.058165025711, 'W_1KI': 78.13, 'W_D': 42.671499999999995, 'J_D': 3935.116338012218, 'W_D_1KI': 42.671499999999995, 'J_D_1KI': 42.671499999999995} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..8e3d7eb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 24272, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.77256941795349, "TIME_S_1KI": 0.8558243827436343, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1485.908479528427, "W": 63.94, "J_1KI": 61.21903755473084, "W_1KI": 2.6343111404087014, "W_D": 28.96575, "J_D": 673.1381535955668, "W_D_1KI": 1.193381262359921, "J_D_1KI": 0.049166993340471365} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..1f19b8b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8651721477508545} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 99999, 100000, + 100000]), + col_indices=tensor([90599, 28958, 57214, ..., 84272, 90301, 79327]), + values=tensor([0.9831, 0.6502, 0.8427, ..., 0.3005, 0.4197, 0.6469]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.7674, 0.7013, 0.3294, ..., 0.7372, 0.8879, 0.9691]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.8651721477508545 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24272', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.77256941795349} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 99999, 100000, + 100000]), + col_indices=tensor([13062, 27623, 58180, ..., 66636, 6102, 47055]), + values=tensor([0.6006, 0.9692, 0.3277, ..., 0.8424, 0.3843, 0.6842]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8442, 0.5820, 0.8888, ..., 0.9824, 0.3648, 0.8783]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 20.77256941795349 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 99999, 100000, + 100000]), + col_indices=tensor([13062, 27623, 58180, ..., 66636, 6102, 47055]), + values=tensor([0.6006, 0.9692, 0.3277, ..., 0.8424, 0.3843, 0.6842]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8442, 0.5820, 0.8888, ..., 0.9824, 0.3648, 0.8783]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 20.77256941795349 seconds + +[39.23, 38.93, 39.06, 38.59, 38.47, 39.34, 39.72, 38.43, 38.47, 38.55] +[63.94] +23.23910665512085 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 24272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.77256941795349, 'TIME_S_1KI': 0.8558243827436343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1485.908479528427, 'W': 63.94} +[39.23, 38.93, 39.06, 38.59, 38.47, 39.34, 39.72, 38.43, 38.47, 38.55, 40.18, 38.38, 38.82, 38.77, 39.08, 39.46, 38.9, 38.51, 38.41, 38.33] +699.4849999999999 +34.97425 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 24272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.77256941795349, 'TIME_S_1KI': 0.8558243827436343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1485.908479528427, 'W': 63.94, 'J_1KI': 61.21903755473084, 'W_1KI': 2.6343111404087014, 'W_D': 28.96575, 'J_D': 673.1381535955668, 'W_D_1KI': 1.193381262359921, 'J_D_1KI': 0.049166993340471365} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json new file mode 100644 index 0000000..39218a0 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [3000000, 3000000], "MATRIX_ROWS": 3000000, "MATRIX_SIZE": 9000000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 191.72871232032776, "TIME_S_1KI": 191.72871232032776, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 16829.73738718033, "W": 76.6, "J_1KI": 16829.73738718033, "W_1KI": 76.6, "W_D": 41.25525, "J_D": 9064.164795593619, "W_D_1KI": 41.25525, "J_D_1KI": 41.25525} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output new file mode 100644 index 0000000..100fb4b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output @@ -0,0 +1,51 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [3000000, 3000000], "MATRIX_ROWS": 3000000, "MATRIX_SIZE": 9000000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 191.72871232032776} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 23, 45, ..., 89999934, + 89999968, 90000000]), + col_indices=tensor([ 247582, 664315, 879297, ..., 2581992, 2759433, + 2830895]), + values=tensor([2.7185e-04, 1.6444e-01, 5.9424e-01, ..., + 8.6814e-02, 3.3148e-01, 4.3454e-01]), + size=(3000000, 3000000), nnz=90000000, layout=torch.sparse_csr) +tensor([2.7160e-02, 3.7741e-01, 2.6824e-01, ..., 9.4002e-01, 3.9211e-01, + 5.3889e-04]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([3000000, 3000000]) +Rows: 3000000 +Size: 9000000000000 +NNZ: 90000000 +Density: 1e-05 +Time: 191.72871232032776 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 23, 45, ..., 89999934, + 89999968, 90000000]), + col_indices=tensor([ 247582, 664315, 879297, ..., 2581992, 2759433, + 2830895]), + values=tensor([2.7185e-04, 1.6444e-01, 5.9424e-01, ..., + 8.6814e-02, 3.3148e-01, 4.3454e-01]), + size=(3000000, 3000000), nnz=90000000, layout=torch.sparse_csr) +tensor([2.7160e-02, 3.7741e-01, 2.6824e-01, ..., 9.4002e-01, 3.9211e-01, + 5.3889e-04]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([3000000, 3000000]) +Rows: 3000000 +Size: 9000000000000 +NNZ: 90000000 +Density: 1e-05 +Time: 191.72871232032776 seconds + +[40.3, 39.61, 39.02, 39.13, 38.86, 39.07, 38.9, 39.42, 39.4, 39.23] +[76.6] +219.7093653678894 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [3000000, 3000000], 'MATRIX_ROWS': 3000000, 'MATRIX_SIZE': 9000000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 191.72871232032776, 'TIME_S_1KI': 191.72871232032776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 16829.73738718033, 'W': 76.6} +[40.3, 39.61, 39.02, 39.13, 38.86, 39.07, 38.9, 39.42, 39.4, 39.23, 40.08, 39.53, 39.61, 39.13, 38.92, 38.85, 39.16, 39.29, 39.5, 39.38] +706.895 +35.34475 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [3000000, 3000000], 'MATRIX_ROWS': 3000000, 'MATRIX_SIZE': 9000000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 191.72871232032776, 'TIME_S_1KI': 191.72871232032776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 16829.73738718033, 'W': 76.6, 'J_1KI': 16829.73738718033, 'W_1KI': 76.6, 'W_D': 41.25525, 'J_D': 9064.164795593619, 'W_D_1KI': 41.25525, 'J_D_1KI': 41.25525} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json new file mode 100644 index 0000000..4b4dea9 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1063, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.793079614639282, "TIME_S_1KI": 19.560752224496035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.0967997741698, "W": 77.38, "J_1KI": 1864.625399599407, "W_1KI": 72.793979303857, "W_D": 42.05524999999999, "J_D": 1077.2496308956142, "W_D_1KI": 39.56279397930385, "J_D_1KI": 37.21805642455677} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output new file mode 100644 index 0000000..b2c76a2 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 19.742191314697266} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 21, 53, ..., 8999946, + 8999970, 9000000]), + col_indices=tensor([ 7507, 16267, 30874, ..., 240828, 243309, + 292990]), + values=tensor([0.1523, 0.7416, 0.9394, ..., 0.2210, 0.2823, 0.7452]), + size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.6931, 0.4096, 0.6953, ..., 0.1410, 0.7837, 0.2675]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 9000000 +Density: 0.0001 +Time: 19.742191314697266 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1063', '-ss', '300000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.793079614639282} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 54, ..., 8999928, + 8999960, 9000000]), + col_indices=tensor([ 1108, 2325, 17171, ..., 276191, 279985, + 288044]), + values=tensor([0.7163, 0.0556, 0.2109, ..., 0.2836, 0.9162, 0.9781]), + size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.4023, 0.6004, 0.3990, ..., 0.2499, 0.0207, 0.8980]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 9000000 +Density: 0.0001 +Time: 20.793079614639282 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 54, ..., 8999928, + 8999960, 9000000]), + col_indices=tensor([ 1108, 2325, 17171, ..., 276191, 279985, + 288044]), + values=tensor([0.7163, 0.0556, 0.2109, ..., 0.2836, 0.9162, 0.9781]), + size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.4023, 0.6004, 0.3990, ..., 0.2499, 0.0207, 0.8980]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 9000000 +Density: 0.0001 +Time: 20.793079614639282 seconds + +[39.41, 38.59, 38.99, 39.22, 38.71, 38.78, 38.62, 38.49, 38.71, 38.61] +[77.38] +25.61510467529297 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1063, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.793079614639282, 'TIME_S_1KI': 19.560752224496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.0967997741698, 'W': 77.38} +[39.41, 38.59, 38.99, 39.22, 38.71, 38.78, 38.62, 38.49, 38.71, 38.61, 40.8, 38.54, 38.59, 45.03, 39.84, 38.78, 38.85, 38.74, 39.11, 38.99] +706.4950000000001 +35.32475000000001 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1063, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.793079614639282, 'TIME_S_1KI': 19.560752224496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.0967997741698, 'W': 77.38, 'J_1KI': 1864.625399599407, 'W_1KI': 72.793979303857, 'W_D': 42.05524999999999, 'J_D': 1077.2496308956142, 'W_D_1KI': 39.56279397930385, 'J_D_1KI': 37.21805642455677} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json new file mode 100644 index 0000000..9826631 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 82.70979857444763, "TIME_S_1KI": 82.70979857444763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7617.53978612423, "W": 78.7, "J_1KI": 7617.53978612423, "W_1KI": 78.7, "W_D": 43.10925, "J_D": 4172.635667407572, "W_D_1KI": 43.10925, "J_D_1KI": 43.10925} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output new file mode 100644 index 0000000..1d00751 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 82.70979857444763} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 152, 293, ..., 44999682, + 44999844, 45000000]), + col_indices=tensor([ 3223, 3275, 5832, ..., 294123, 295027, + 295416]), + values=tensor([0.3881, 0.9495, 0.6878, ..., 0.4195, 0.0754, 0.7743]), + size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.1554, 0.9886, 0.7175, ..., 0.9350, 0.8453, 0.9634]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 45000000 +Density: 0.0005 +Time: 82.70979857444763 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 152, 293, ..., 44999682, + 44999844, 45000000]), + col_indices=tensor([ 3223, 3275, 5832, ..., 294123, 295027, + 295416]), + values=tensor([0.3881, 0.9495, 0.6878, ..., 0.4195, 0.0754, 0.7743]), + size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.1554, 0.9886, 0.7175, ..., 0.9350, 0.8453, 0.9634]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 45000000 +Density: 0.0005 +Time: 82.70979857444763 seconds + +[40.11, 39.46, 38.73, 39.77, 38.75, 39.19, 44.18, 39.79, 38.69, 39.03] +[78.7] +96.79211926460266 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 82.70979857444763, 'TIME_S_1KI': 82.70979857444763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7617.53978612423, 'W': 78.7} +[40.11, 39.46, 38.73, 39.77, 38.75, 39.19, 44.18, 39.79, 38.69, 39.03, 40.06, 38.93, 39.06, 39.1, 39.39, 38.81, 40.25, 39.15, 38.9, 40.13] +711.8149999999999 +35.59075 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 82.70979857444763, 'TIME_S_1KI': 82.70979857444763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7617.53978612423, 'W': 78.7, 'J_1KI': 7617.53978612423, 'W_1KI': 78.7, 'W_D': 43.10925, 'J_D': 4172.635667407572, 'W_D_1KI': 43.10925, 'J_D_1KI': 43.10925} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json new file mode 100644 index 0000000..b74a472 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.001, "TIME_S": 156.8496162891388, "TIME_S_1KI": 156.8496162891388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 14006.660120918752, "W": 78.01, "J_1KI": 14006.660120918752, "W_1KI": 78.01, "W_D": 42.747000000000014, "J_D": 7675.204463388207, "W_D_1KI": 42.747000000000014, "J_D_1KI": 42.747000000000014} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output new file mode 100644 index 0000000..798bebb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.001, "TIME_S": 156.8496162891388} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 283, 569, ..., 89999415, + 89999689, 90000000]), + col_indices=tensor([ 2602, 2894, 4432, ..., 298607, 298963, + 299275]), + values=tensor([0.8641, 0.5339, 0.9185, ..., 0.5269, 0.1925, 0.1221]), + size=(300000, 300000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.4486, 0.1676, 0.4646, ..., 0.7992, 0.4354, 0.7205]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 90000000 +Density: 0.001 +Time: 156.8496162891388 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 283, 569, ..., 89999415, + 89999689, 90000000]), + col_indices=tensor([ 2602, 2894, 4432, ..., 298607, 298963, + 299275]), + values=tensor([0.8641, 0.5339, 0.9185, ..., 0.5269, 0.1925, 0.1221]), + size=(300000, 300000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.4486, 0.1676, 0.4646, ..., 0.7992, 0.4354, 0.7205]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 90000000 +Density: 0.001 +Time: 156.8496162891388 seconds + +[41.21, 38.89, 39.08, 39.22, 38.89, 38.77, 38.88, 39.4, 38.92, 38.72] +[78.01] +179.54954648017883 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 156.8496162891388, 'TIME_S_1KI': 156.8496162891388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 14006.660120918752, 'W': 78.01} +[41.21, 38.89, 39.08, 39.22, 38.89, 38.77, 38.88, 39.4, 38.92, 38.72, 39.91, 38.95, 39.72, 39.54, 39.0, 39.29, 39.46, 38.83, 39.09, 38.82] +705.2599999999999 +35.26299999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 156.8496162891388, 'TIME_S_1KI': 156.8496162891388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 14006.660120918752, 'W': 78.01, 'J_1KI': 14006.660120918752, 'W_1KI': 78.01, 'W_D': 42.747000000000014, 'J_D': 7675.204463388207, 'W_D_1KI': 42.747000000000014, 'J_D_1KI': 42.747000000000014} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json new file mode 100644 index 0000000..e1aa60a --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 5359, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.92583179473877, "TIME_S_1KI": 3.9048016037952546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1549.9649752044677, "W": 65.36, "J_1KI": 289.2265301743735, "W_1KI": 12.196305280835977, "W_D": 30.458, "J_D": 722.2893698711395, "W_D_1KI": 5.68352304534428, "J_D_1KI": 1.0605566421616497} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output new file mode 100644 index 0000000..219c53d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.918142080307007} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 8, ..., 899997, 899998, + 900000]), + col_indices=tensor([ 53227, 167745, 185678, ..., 77368, 81779, + 166650]), + values=tensor([0.4014, 0.7044, 0.9681, ..., 0.8398, 0.4850, 0.3713]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.1108, 0.9813, 0.9578, ..., 0.9978, 0.7777, 0.0486]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 3.918142080307007 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5359', '-ss', '300000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.92583179473877} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 5, ..., 899994, 899995, + 900000]), + col_indices=tensor([ 27767, 11526, 53261, ..., 95027, 105010, + 203459]), + values=tensor([0.0853, 0.1452, 0.4972, ..., 0.6342, 0.0274, 0.2283]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.6261, 0.3964, 0.4952, ..., 0.8021, 0.8822, 0.0136]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 20.92583179473877 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 5, ..., 899994, 899995, + 900000]), + col_indices=tensor([ 27767, 11526, 53261, ..., 95027, 105010, + 203459]), + values=tensor([0.0853, 0.1452, 0.4972, ..., 0.6342, 0.0274, 0.2283]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.6261, 0.3964, 0.4952, ..., 0.8021, 0.8822, 0.0136]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 20.92583179473877 seconds + +[39.87, 38.56, 38.69, 39.11, 38.77, 38.59, 38.92, 38.89, 38.6, 38.62] +[65.36] +23.714274406433105 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5359, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.92583179473877, 'TIME_S_1KI': 3.9048016037952546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1549.9649752044677, 'W': 65.36} +[39.87, 38.56, 38.69, 39.11, 38.77, 38.59, 38.92, 38.89, 38.6, 38.62, 39.84, 38.63, 38.77, 38.47, 38.56, 38.46, 38.55, 38.86, 39.14, 38.61] +698.04 +34.902 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5359, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.92583179473877, 'TIME_S_1KI': 3.9048016037952546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1549.9649752044677, 'W': 65.36, 'J_1KI': 289.2265301743735, 'W_1KI': 12.196305280835977, 'W_D': 30.458, 'J_D': 722.2893698711395, 'W_D_1KI': 5.68352304534428, 'J_D_1KI': 1.0605566421616497} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..9bbde60 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 55249, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.486661672592163, "TIME_S_1KI": 0.37080601771239596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1479.9529262781143, "W": 64.5, "J_1KI": 26.78696313558823, "W_1KI": 1.167441944650582, "W_D": 29.78750000000001, "J_D": 683.4743843644859, "W_D_1KI": 0.5391500298647941, "J_D_1KI": 0.009758548206570147} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..616b7ef --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output @@ -0,0 +1,62 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3800966739654541} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 4, ..., 89996, 89999, 90000]), + col_indices=tensor([ 1221, 15892, 17835, ..., 22172, 27458, 10275]), + values=tensor([0.6309, 0.5140, 0.9291, ..., 0.9679, 0.2956, 0.6723]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.8758, 0.8303, 0.8564, ..., 0.1623, 0.2512, 0.5347]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 0.3800966739654541 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55249', '-ss', '30000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.486661672592163} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 89993, 89996, 90000]), + col_indices=tensor([ 3466, 7549, 9181, ..., 12705, 16674, 29218]), + values=tensor([0.0628, 0.6253, 0.0638, ..., 0.6445, 0.5421, 0.6895]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.0380, 0.3127, 0.8894, ..., 0.0355, 0.5164, 0.5166]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.486661672592163 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 7, ..., 89993, 89996, 90000]), + col_indices=tensor([ 3466, 7549, 9181, ..., 12705, 16674, 29218]), + values=tensor([0.0628, 0.6253, 0.0638, ..., 0.6445, 0.5421, 0.6895]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.0380, 0.3127, 0.8894, ..., 0.0355, 0.5164, 0.5166]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.486661672592163 seconds + +[39.55, 38.61, 38.56, 38.2, 38.29, 38.62, 38.69, 39.07, 38.21, 38.58] +[64.5] +22.945006608963013 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 55249, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.486661672592163, 'TIME_S_1KI': 0.37080601771239596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.9529262781143, 'W': 64.5} +[39.55, 38.61, 38.56, 38.2, 38.29, 38.62, 38.69, 39.07, 38.21, 38.58, 39.63, 38.76, 38.33, 38.68, 38.62, 38.18, 38.6, 38.43, 38.25, 38.54] +694.2499999999999 +34.71249999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 55249, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.486661672592163, 'TIME_S_1KI': 0.37080601771239596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.9529262781143, 'W': 64.5, 'J_1KI': 26.78696313558823, 'W_1KI': 1.167441944650582, 'W_D': 29.78750000000001, 'J_D': 683.4743843644859, 'W_D_1KI': 0.5391500298647941, 'J_D_1KI': 0.009758548206570147} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json new file mode 100644 index 0000000..80e5199 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 34887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.488590478897095, "TIME_S_1KI": 0.5872843889958177, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1541.8012697553634, "W": 66.46, "J_1KI": 44.194148816331676, "W_1KI": 1.905007595952647, "W_D": 31.609249999999996, "J_D": 733.3009597654938, "W_D_1KI": 0.9060466649468283, "J_D_1KI": 0.025970896464208106} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output new file mode 100644 index 0000000..ccdbe37 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 0.6019301414489746} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 16, 37, ..., 449975, 449988, + 450000]), + col_indices=tensor([ 264, 1229, 1878, ..., 24507, 28043, 28225]), + values=tensor([0.5098, 0.2540, 0.2183, ..., 0.6993, 0.8002, 0.9164]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.9266, 0.1174, 0.4919, ..., 0.2483, 0.0597, 0.7571]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 0.6019301414489746 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '34887', '-ss', '30000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.488590478897095} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 31, ..., 449970, 449990, + 450000]), + col_indices=tensor([ 170, 825, 5087, ..., 18453, 22268, 25473]), + values=tensor([0.8450, 0.9269, 0.6663, ..., 0.1685, 0.3198, 0.2341]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.4348, 0.7206, 0.1155, ..., 0.4036, 0.9348, 0.9025]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 20.488590478897095 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 15, 31, ..., 449970, 449990, + 450000]), + col_indices=tensor([ 170, 825, 5087, ..., 18453, 22268, 25473]), + values=tensor([0.8450, 0.9269, 0.6663, ..., 0.1685, 0.3198, 0.2341]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.4348, 0.7206, 0.1155, ..., 0.4036, 0.9348, 0.9025]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 20.488590478897095 seconds + +[39.44, 38.55, 38.49, 38.28, 38.9, 38.59, 38.27, 39.46, 38.91, 38.62] +[66.46] +23.198935747146606 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.488590478897095, 'TIME_S_1KI': 0.5872843889958177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.8012697553634, 'W': 66.46} +[39.44, 38.55, 38.49, 38.28, 38.9, 38.59, 38.27, 39.46, 38.91, 38.62, 38.9, 40.17, 38.27, 38.69, 38.64, 38.67, 38.48, 38.59, 38.33, 38.49] +697.015 +34.85075 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.488590478897095, 'TIME_S_1KI': 0.5872843889958177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.8012697553634, 'W': 66.46, 'J_1KI': 44.194148816331676, 'W_1KI': 1.905007595952647, 'W_D': 31.609249999999996, 'J_D': 733.3009597654938, 'W_D_1KI': 0.9060466649468283, 'J_D_1KI': 0.025970896464208106} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..6b2d57a --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 20725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.679001569747925, "TIME_S_1KI": 0.9977805341253522, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1558.1207911372185, "W": 66.77, "J_1KI": 75.18073781120475, "W_1KI": 3.2217129071170083, "W_D": 31.95525, "J_D": 745.6962619587779, "W_D_1KI": 1.541869722557298, "J_D_1KI": 0.07439660904980931} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..5dedece --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.0132288932800293} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 29, 66, ..., 899946, 899968, + 900000]), + col_indices=tensor([ 3782, 4225, 4981, ..., 28194, 28873, 29915]), + values=tensor([0.9172, 0.4074, 0.0680, ..., 0.0394, 0.1843, 0.7343]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.0798, 0.0011, 0.8799, ..., 0.7150, 0.2962, 0.9319]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 1.0132288932800293 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20725', '-ss', '30000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.679001569747925} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 26, 62, ..., 899937, 899971, + 900000]), + col_indices=tensor([ 143, 864, 1272, ..., 28362, 29224, 29939]), + values=tensor([0.4085, 0.1763, 0.0566, ..., 0.6744, 0.4746, 0.4502]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.4289, 0.5358, 0.7834, ..., 0.0567, 0.8331, 0.5874]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.679001569747925 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 26, 62, ..., 899937, 899971, + 900000]), + col_indices=tensor([ 143, 864, 1272, ..., 28362, 29224, 29939]), + values=tensor([0.4085, 0.1763, 0.0566, ..., 0.6744, 0.4746, 0.4502]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.4289, 0.5358, 0.7834, ..., 0.0567, 0.8331, 0.5874]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.679001569747925 seconds + +[39.66, 38.47, 38.7, 38.29, 38.39, 38.27, 38.73, 40.46, 38.83, 38.77] +[66.77] +23.335641622543335 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 20725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.679001569747925, 'TIME_S_1KI': 0.9977805341253522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1558.1207911372185, 'W': 66.77} +[39.66, 38.47, 38.7, 38.29, 38.39, 38.27, 38.73, 40.46, 38.83, 38.77, 39.05, 38.32, 38.41, 38.47, 38.31, 38.24, 38.86, 38.82, 38.88, 38.21] +696.295 +34.81475 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 20725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.679001569747925, 'TIME_S_1KI': 0.9977805341253522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1558.1207911372185, 'W': 66.77, 'J_1KI': 75.18073781120475, 'W_1KI': 3.2217129071170083, 'W_D': 31.95525, 'J_D': 745.6962619587779, 'W_D_1KI': 1.541869722557298, 'J_D_1KI': 0.07439660904980931} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json new file mode 100644 index 0000000..465580a --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3915, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.893795013427734, "TIME_S_1KI": 5.336856963838502, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1813.4314341068268, "W": 74.06, "J_1KI": 463.2008771664947, "W_1KI": 18.91698595146871, "W_D": 38.97975, "J_D": 954.4572501164675, "W_D_1KI": 9.956513409961687, "J_D_1KI": 2.543170730513841} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output new file mode 100644 index 0000000..8f37edd --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 5.36383318901062} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 155, 299, ..., 4499724, + 4499869, 4500000]), + col_indices=tensor([ 264, 391, 402, ..., 28982, 29660, 29822]), + values=tensor([0.6346, 0.1316, 0.6696, ..., 0.4424, 0.6280, 0.5777]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.7068, 0.8368, 0.5788, ..., 0.2748, 0.1756, 0.1861]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 5.36383318901062 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3915', '-ss', '30000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.893795013427734} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 154, 308, ..., 4499718, + 4499857, 4500000]), + col_indices=tensor([ 610, 1343, 1528, ..., 29102, 29121, 29420]), + values=tensor([0.4309, 0.4085, 0.5621, ..., 0.6007, 0.1982, 0.6029]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.1648, 0.4135, 0.3012, ..., 0.9678, 0.7893, 0.8451]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 20.893795013427734 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 154, 308, ..., 4499718, + 4499857, 4500000]), + col_indices=tensor([ 610, 1343, 1528, ..., 29102, 29121, 29420]), + values=tensor([0.4309, 0.4085, 0.5621, ..., 0.6007, 0.1982, 0.6029]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.1648, 0.4135, 0.3012, ..., 0.9678, 0.7893, 0.8451]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 20.893795013427734 seconds + +[39.73, 38.52, 38.68, 38.37, 38.44, 38.79, 38.97, 38.82, 38.35, 38.46] +[74.06] +24.485976696014404 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3915, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.893795013427734, 'TIME_S_1KI': 5.336856963838502, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1813.4314341068268, 'W': 74.06} +[39.73, 38.52, 38.68, 38.37, 38.44, 38.79, 38.97, 38.82, 38.35, 38.46, 39.31, 38.45, 39.17, 38.79, 38.86, 38.46, 38.42, 40.19, 42.43, 38.29] +701.605 +35.08025 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3915, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.893795013427734, 'TIME_S_1KI': 5.336856963838502, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1813.4314341068268, 'W': 74.06, 'J_1KI': 463.2008771664947, 'W_1KI': 18.91698595146871, 'W_D': 38.97975, 'J_D': 954.4572501164675, 'W_D_1KI': 9.956513409961687, 'J_D_1KI': 2.543170730513841} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json new file mode 100644 index 0000000..a4676a2 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1556, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.02338671684265, "TIME_S_1KI": 13.51117398254669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1995.9884867286682, "W": 77.72, "J_1KI": 1282.768950339761, "W_1KI": 49.948586118251924, "W_D": 42.48775, "J_D": 1091.1613462044, "W_D_1KI": 27.305751928020563, "J_D_1KI": 17.54868375836797} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output new file mode 100644 index 0000000..d1812b6 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 13.49134111404419} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 301, 621, ..., 8999428, + 8999715, 9000000]), + col_indices=tensor([ 350, 633, 742, ..., 29783, 29873, 29944]), + values=tensor([0.6028, 0.5433, 0.5346, ..., 0.4728, 0.8732, 0.8469]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.9726, 0.3801, 0.1059, ..., 0.5337, 0.8863, 0.3497]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 13.49134111404419 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1556', '-ss', '30000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.02338671684265} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 286, 540, ..., 8999445, + 8999721, 9000000]), + col_indices=tensor([ 47, 65, 169, ..., 29629, 29825, 29922]), + values=tensor([0.0693, 0.5848, 0.3473, ..., 0.1079, 0.3518, 0.8477]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.4830, 0.8793, 0.7685, ..., 0.7345, 0.8852, 0.3790]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 21.02338671684265 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 286, 540, ..., 8999445, + 8999721, 9000000]), + col_indices=tensor([ 47, 65, 169, ..., 29629, 29825, 29922]), + values=tensor([0.0693, 0.5848, 0.3473, ..., 0.1079, 0.3518, 0.8477]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.4830, 0.8793, 0.7685, ..., 0.7345, 0.8852, 0.3790]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 21.02338671684265 seconds + +[39.69, 38.9, 38.91, 42.35, 40.35, 38.51, 38.87, 38.42, 38.6, 38.45] +[77.72] +25.68178701400757 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1556, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.02338671684265, 'TIME_S_1KI': 13.51117398254669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1995.9884867286682, 'W': 77.72} +[39.69, 38.9, 38.91, 42.35, 40.35, 38.51, 38.87, 38.42, 38.6, 38.45, 39.21, 38.97, 38.49, 40.7, 38.5, 38.41, 38.96, 38.84, 38.98, 38.42] +704.645 +35.23225 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1556, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.02338671684265, 'TIME_S_1KI': 13.51117398254669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1995.9884867286682, 'W': 77.72, 'J_1KI': 1282.768950339761, 'W_1KI': 49.948586118251924, 'W_D': 42.48775, 'J_D': 1091.1613462044, 'W_D_1KI': 27.305751928020563, 'J_D_1KI': 17.54868375836797} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json new file mode 100644 index 0000000..7f038bb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 67.5497567653656, "TIME_S_1KI": 67.5497567653656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6177.228344964981, "W": 78.3, "J_1KI": 6177.228344964981, "W_1KI": 78.3, "W_D": 42.846000000000004, "J_D": 3380.198284398079, "W_D_1KI": 42.846000000000004, "J_D_1KI": 42.846000000000004} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output new file mode 100644 index 0000000..509a893 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 67.5497567653656} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1454, 2998, ..., 44997015, + 44998496, 45000000]), + col_indices=tensor([ 16, 34, 52, ..., 29923, 29949, 29997]), + values=tensor([0.3150, 0.1901, 0.4388, ..., 0.7749, 0.7841, 0.0957]), + size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.5511, 0.2744, 0.0926, ..., 0.8323, 0.9167, 0.9679]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 45000000 +Density: 0.05 +Time: 67.5497567653656 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1454, 2998, ..., 44997015, + 44998496, 45000000]), + col_indices=tensor([ 16, 34, 52, ..., 29923, 29949, 29997]), + values=tensor([0.3150, 0.1901, 0.4388, ..., 0.7749, 0.7841, 0.0957]), + size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.5511, 0.2744, 0.0926, ..., 0.8323, 0.9167, 0.9679]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 45000000 +Density: 0.05 +Time: 67.5497567653656 seconds + +[39.5, 38.95, 38.67, 41.05, 38.61, 38.51, 38.69, 44.88, 39.32, 38.97] +[78.3] +78.89180517196655 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 67.5497567653656, 'TIME_S_1KI': 67.5497567653656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6177.228344964981, 'W': 78.3} +[39.5, 38.95, 38.67, 41.05, 38.61, 38.51, 38.69, 44.88, 39.32, 38.97, 39.34, 39.37, 38.66, 38.66, 38.66, 38.61, 39.18, 39.8, 39.03, 39.05] +709.0799999999999 +35.45399999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 67.5497567653656, 'TIME_S_1KI': 67.5497567653656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6177.228344964981, 'W': 78.3, 'J_1KI': 6177.228344964981, 'W_1KI': 78.3, 'W_D': 42.846000000000004, 'J_D': 3380.198284398079, 'W_D_1KI': 42.846000000000004, 'J_D_1KI': 42.846000000000004} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json new file mode 100644 index 0000000..9a9fc75 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 133.26440334320068, "TIME_S_1KI": 133.26440334320068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11974.481567811967, "W": 77.72, "J_1KI": 11974.481567811967, "W_1KI": 77.72, "W_D": 41.982749999999996, "J_D": 6468.369352046549, "W_D_1KI": 41.982749999999996, "J_D_1KI": 41.982749999999996} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output new file mode 100644 index 0000000..09ee6f8 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 133.26440334320068} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3021, 6099, ..., 89993867, + 89996977, 90000000]), + col_indices=tensor([ 3, 4, 14, ..., 29943, 29960, 29964]), + values=tensor([0.6910, 0.0984, 0.4875, ..., 0.7473, 0.5068, 0.5297]), + size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.9263, 0.2732, 0.6847, ..., 0.8727, 0.6781, 0.2367]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000000 +Density: 0.1 +Time: 133.26440334320068 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3021, 6099, ..., 89993867, + 89996977, 90000000]), + col_indices=tensor([ 3, 4, 14, ..., 29943, 29960, 29964]), + values=tensor([0.6910, 0.0984, 0.4875, ..., 0.7473, 0.5068, 0.5297]), + size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr) +tensor([0.9263, 0.2732, 0.6847, ..., 0.8727, 0.6781, 0.2367]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000000 +Density: 0.1 +Time: 133.26440334320068 seconds + +[39.6, 39.07, 38.84, 44.59, 38.99, 38.94, 39.08, 38.61, 39.42, 39.07] +[77.72] +154.07207369804382 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 133.26440334320068, 'TIME_S_1KI': 133.26440334320068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11974.481567811967, 'W': 77.72} +[39.6, 39.07, 38.84, 44.59, 38.99, 38.94, 39.08, 38.61, 39.42, 39.07, 40.74, 38.83, 39.58, 40.58, 41.0, 38.76, 38.99, 38.91, 38.77, 44.16] +714.745 +35.73725 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 133.26440334320068, 'TIME_S_1KI': 133.26440334320068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11974.481567811967, 'W': 77.72, 'J_1KI': 11974.481567811967, 'W_1KI': 77.72, 'W_D': 41.982749999999996, 'J_D': 6468.369352046549, 'W_D_1KI': 41.982749999999996, 'J_D_1KI': 41.982749999999996} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..1c62691 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 175149, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.99797296524048, "TIME_S_1KI": 0.11988634228708402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1522.755680565834, "W": 64.38, "J_1KI": 8.694058661858383, "W_1KI": 0.36757275234229136, "W_D": 29.668999999999997, "J_D": 701.7495850684642, "W_D_1KI": 0.16939291688790686, "J_D_1KI": 0.0009671360777846684} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..3d76ec3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14505624771118164} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 9000, 9000, 9000]), + col_indices=tensor([ 9793, 24410, 9766, ..., 25093, 22416, 28253]), + values=tensor([0.6564, 0.9558, 0.9015, ..., 0.1425, 0.1152, 0.5551]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.6628, 0.4963, 0.8694, ..., 0.1058, 0.0731, 0.1152]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.14505624771118164 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '144771', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 17.35769486427307} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 9000, 9000, 9000]), + col_indices=tensor([23214, 17022, 19042, ..., 25316, 9102, 1076]), + values=tensor([0.7231, 0.3079, 0.5530, ..., 0.2799, 0.6458, 0.4353]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.5188, 0.8305, 0.1941, ..., 0.8435, 0.1201, 0.3717]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 17.35769486427307 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '175149', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.99797296524048} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 8999, 8999, 9000]), + col_indices=tensor([ 6289, 7348, 12538, ..., 3410, 26024, 8619]), + values=tensor([0.6330, 0.6673, 0.9719, ..., 0.9831, 0.5544, 0.1794]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.5417, 0.7572, 0.7994, ..., 0.0522, 0.2469, 0.9706]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 20.99797296524048 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 8999, 8999, 9000]), + col_indices=tensor([ 6289, 7348, 12538, ..., 3410, 26024, 8619]), + values=tensor([0.6330, 0.6673, 0.9719, ..., 0.9831, 0.5544, 0.1794]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.5417, 0.7572, 0.7994, ..., 0.0522, 0.2469, 0.9706]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 20.99797296524048 seconds + +[38.91, 38.61, 38.98, 38.21, 38.26, 38.52, 38.56, 39.49, 38.59, 38.29] +[64.38] +23.65262007713318 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 175149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.99797296524048, 'TIME_S_1KI': 0.11988634228708402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1522.755680565834, 'W': 64.38} +[38.91, 38.61, 38.98, 38.21, 38.26, 38.52, 38.56, 39.49, 38.59, 38.29, 39.46, 38.49, 38.32, 38.33, 38.28, 38.28, 38.38, 38.51, 38.81, 38.54] +694.22 +34.711 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 175149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.99797296524048, 'TIME_S_1KI': 0.11988634228708402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1522.755680565834, 'W': 64.38, 'J_1KI': 8.694058661858383, 'W_1KI': 0.36757275234229136, 'W_D': 29.668999999999997, 'J_D': 701.7495850684642, 'W_D_1KI': 0.16939291688790686, 'J_D_1KI': 0.0009671360777846684} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json new file mode 100644 index 0000000..d3de446 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 53.28069806098938, "TIME_S_1KI": 53.28069806098938, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4799.690837917327, "W": 80.74, "J_1KI": 4799.690837917327, "W_1KI": 80.74, "W_D": 45.05724999999999, "J_D": 2678.484889853238, "W_D_1KI": 45.05724999999999, "J_D_1KI": 45.05724999999999} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output new file mode 100644 index 0000000..c127bed --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 53.28069806098938} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 53, 102, ..., 24999886, + 24999937, 25000000]), + col_indices=tensor([ 16979, 17933, 30686, ..., 481834, 490973, + 494514]), + values=tensor([0.3572, 0.8267, 0.7501, ..., 0.1157, 0.6125, 0.1407]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.1123, 0.0430, 0.5296, ..., 0.1520, 0.8075, 0.3691]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 53.28069806098938 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 53, 102, ..., 24999886, + 24999937, 25000000]), + col_indices=tensor([ 16979, 17933, 30686, ..., 481834, 490973, + 494514]), + values=tensor([0.3572, 0.8267, 0.7501, ..., 0.1157, 0.6125, 0.1407]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.1123, 0.0430, 0.5296, ..., 0.1520, 0.8075, 0.3691]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 53.28069806098938 seconds + +[39.46, 39.23, 39.42, 39.24, 38.85, 39.3, 38.79, 39.04, 38.81, 39.05] +[80.74] +59.44625759124756 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 53.28069806098938, 'TIME_S_1KI': 53.28069806098938, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4799.690837917327, 'W': 80.74} +[39.46, 39.23, 39.42, 39.24, 38.85, 39.3, 38.79, 39.04, 38.81, 39.05, 39.8, 38.77, 39.08, 40.74, 39.37, 39.16, 39.3, 47.07, 38.83, 39.0] +713.6550000000001 +35.682750000000006 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 53.28069806098938, 'TIME_S_1KI': 53.28069806098938, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4799.690837917327, 'W': 80.74, 'J_1KI': 4799.690837917327, 'W_1KI': 80.74, 'W_D': 45.05724999999999, 'J_D': 2678.484889853238, 'W_D_1KI': 45.05724999999999, 'J_D_1KI': 45.05724999999999} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..19009e3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2738, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.017223119735718, "TIME_S_1KI": 7.676122395812899, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1812.0194424438475, "W": 76.16, "J_1KI": 661.804033032815, "W_1KI": 27.81592403214025, "W_D": 40.82625, "J_D": 971.3492484515906, "W_D_1KI": 14.910975164353543, "J_D_1KI": 5.445936875220432} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..b6ef67e --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.669674634933472} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 17, ..., 2499990, + 2499993, 2500000]), + col_indices=tensor([ 52473, 65771, 123815, ..., 335848, 435662, + 475263]), + values=tensor([0.2082, 0.2192, 0.6702, ..., 0.7546, 0.3364, 0.5504]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6123, 0.6429, 0.4015, ..., 0.8475, 0.6522, 0.4492]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 7.669674634933472 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2738', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.017223119735718} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 8, ..., 2499990, + 2499995, 2500000]), + col_indices=tensor([334289, 479579, 4894, ..., 301830, 313714, + 458526]), + values=tensor([0.7811, 0.4358, 0.2037, ..., 0.6487, 0.4394, 0.7955]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3985, 0.6545, 0.8794, ..., 0.0163, 0.4728, 0.5226]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 21.017223119735718 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 8, ..., 2499990, + 2499995, 2500000]), + col_indices=tensor([334289, 479579, 4894, ..., 301830, 313714, + 458526]), + values=tensor([0.7811, 0.4358, 0.2037, ..., 0.6487, 0.4394, 0.7955]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3985, 0.6545, 0.8794, ..., 0.0163, 0.4728, 0.5226]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 21.017223119735718 seconds + +[39.49, 38.83, 40.75, 42.9, 39.3, 39.29, 38.89, 38.88, 38.79, 38.76] +[76.16] +23.792272090911865 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.017223119735718, 'TIME_S_1KI': 7.676122395812899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1812.0194424438475, 'W': 76.16} +[39.49, 38.83, 40.75, 42.9, 39.3, 39.29, 38.89, 38.88, 38.79, 38.76, 40.02, 38.88, 38.89, 38.66, 38.84, 38.71, 38.92, 38.66, 38.76, 39.18] +706.675 +35.333749999999995 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.017223119735718, 'TIME_S_1KI': 7.676122395812899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1812.0194424438475, 'W': 76.16, 'J_1KI': 661.804033032815, 'W_1KI': 27.81592403214025, 'W_D': 40.82625, 'J_D': 971.3492484515906, 'W_D_1KI': 14.910975164353543, 'J_D_1KI': 5.445936875220432} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..b0cdcc0 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.60400652885437, "TIME_S_1KI": 0.6715340111092618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1545.1941801166536, "W": 65.18, "J_1KI": 50.36158594995938, "W_1KI": 2.1243725963105407, "W_D": 30.163750000000007, "J_D": 715.0790265494587, "W_D_1KI": 0.9831089889837691, "J_D_1KI": 0.032041880874251} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..114c64b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6844191551208496} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 9, ..., 249993, 249996, + 250000]), + col_indices=tensor([ 8529, 23824, 37106, ..., 11640, 15800, 34725]), + values=tensor([0.8073, 0.5844, 0.8147, ..., 0.3062, 0.9804, 0.2233]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0481, 0.3329, 0.6398, ..., 0.2932, 0.9523, 0.8115]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.6844191551208496 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '30682', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.60400652885437} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 10, ..., 249991, 249996, + 250000]), + col_indices=tensor([ 1625, 14875, 16966, ..., 28233, 46165, 49230]), + values=tensor([0.7686, 0.4498, 0.3631, ..., 0.7737, 0.9073, 0.9265]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0406, 0.0267, 0.6076, ..., 0.5503, 0.5752, 0.3050]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.60400652885437 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 10, ..., 249991, 249996, + 250000]), + col_indices=tensor([ 1625, 14875, 16966, ..., 28233, 46165, 49230]), + values=tensor([0.7686, 0.4498, 0.3631, ..., 0.7737, 0.9073, 0.9265]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0406, 0.0267, 0.6076, ..., 0.5503, 0.5752, 0.3050]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.60400652885437 seconds + +[43.14, 38.95, 39.67, 38.57, 38.57, 38.52, 38.64, 38.47, 38.93, 38.56] +[65.18] +23.7065691947937 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.60400652885437, 'TIME_S_1KI': 0.6715340111092618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.1941801166536, 'W': 65.18} +[43.14, 38.95, 39.67, 38.57, 38.57, 38.52, 38.64, 38.47, 38.93, 38.56, 40.45, 38.49, 38.69, 38.62, 38.51, 38.64, 38.59, 38.5, 39.32, 39.14] +700.325 +35.01625 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.60400652885437, 'TIME_S_1KI': 0.6715340111092618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.1941801166536, 'W': 65.18, 'J_1KI': 50.36158594995938, 'W_1KI': 2.1243725963105407, 'W_D': 30.163750000000007, 'J_D': 715.0790265494587, 'W_D_1KI': 0.9831089889837691, 'J_D_1KI': 0.032041880874251} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json new file mode 100644 index 0000000..e7d785e --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 14725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.75746774673462, "TIME_S_1KI": 1.4096752289802799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1579.229201145172, "W": 66.94, "J_1KI": 107.24816306588606, "W_1KI": 4.546010186757215, "W_D": 32.068, "J_D": 756.5390203514098, "W_D_1KI": 2.177792869269949, "J_D_1KI": 0.14789764816773848} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output new file mode 100644 index 0000000..ac0ade3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 1.4260749816894531} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 22, 46, ..., 1249956, + 1249973, 1250000]), + col_indices=tensor([ 3380, 4310, 7517, ..., 40689, 41242, 47374]), + values=tensor([0.8200, 0.1077, 0.6690, ..., 0.5575, 0.1139, 0.4853]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.0332, 0.0813, 0.1673, ..., 0.7573, 0.1508, 0.1365]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 1.4260749816894531 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14725', '-ss', '50000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.75746774673462} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 52, ..., 1249951, + 1249977, 1250000]), + col_indices=tensor([ 344, 2189, 2223, ..., 35575, 37368, 38958]), + values=tensor([0.6875, 0.6607, 0.3048, ..., 0.8392, 0.7333, 0.1352]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.9792, 0.5124, 0.4372, ..., 0.1755, 0.2227, 0.5082]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 20.75746774673462 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 52, ..., 1249951, + 1249977, 1250000]), + col_indices=tensor([ 344, 2189, 2223, ..., 35575, 37368, 38958]), + values=tensor([0.6875, 0.6607, 0.3048, ..., 0.8392, 0.7333, 0.1352]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.9792, 0.5124, 0.4372, ..., 0.1755, 0.2227, 0.5082]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 20.75746774673462 seconds + +[39.99, 38.86, 39.01, 38.83, 38.77, 38.47, 38.81, 38.72, 39.0, 38.37] +[66.94] +23.59171199798584 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 14725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.75746774673462, 'TIME_S_1KI': 1.4096752289802799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1579.229201145172, 'W': 66.94} +[39.99, 38.86, 39.01, 38.83, 38.77, 38.47, 38.81, 38.72, 39.0, 38.37, 39.4, 38.41, 39.01, 38.37, 38.38, 39.17, 38.41, 38.71, 38.46, 38.34] +697.4399999999999 +34.872 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 14725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.75746774673462, 'TIME_S_1KI': 1.4096752289802799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1579.229201145172, 'W': 66.94, 'J_1KI': 107.24816306588606, 'W_1KI': 4.546010186757215, 'W_D': 32.068, 'J_D': 756.5390203514098, 'W_D_1KI': 2.177792869269949, 'J_D_1KI': 0.14789764816773848} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..3e8eb72 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6999, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.897379159927368, "TIME_S_1KI": 2.9857664180493457, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1683.2534707903862, "W": 70.47, "J_1KI": 240.49913856127822, "W_1KI": 10.068581225889414, "W_D": 35.496750000000006, "J_D": 847.8789220842721, "W_D_1KI": 5.071688812687528, "J_D_1KI": 0.724630491882773} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..452da36 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0004022121429443} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 47, 90, ..., 2499903, + 2499954, 2500000]), + col_indices=tensor([ 2310, 2538, 3521, ..., 46920, 47069, 48673]), + values=tensor([0.5437, 0.3122, 0.8737, ..., 0.7809, 0.3023, 0.2727]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.6467, 0.8674, 0.4268, ..., 0.4389, 0.3661, 0.7175]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 3.0004022121429443 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6999', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.897379159927368} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 48, 100, ..., 2499890, + 2499936, 2500000]), + col_indices=tensor([ 2219, 5577, 6326, ..., 48217, 48582, 49573]), + values=tensor([0.8831, 0.9308, 0.4380, ..., 0.8264, 0.2520, 0.0049]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1681, 0.1881, 0.9636, ..., 0.5462, 0.5477, 0.9460]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 20.897379159927368 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 48, 100, ..., 2499890, + 2499936, 2500000]), + col_indices=tensor([ 2219, 5577, 6326, ..., 48217, 48582, 49573]), + values=tensor([0.8831, 0.9308, 0.4380, ..., 0.8264, 0.2520, 0.0049]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1681, 0.1881, 0.9636, ..., 0.5462, 0.5477, 0.9460]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 20.897379159927368 seconds + +[39.8, 38.87, 39.22, 38.93, 38.9, 38.99, 38.89, 38.48, 38.43, 39.67] +[70.47] +23.88610005378723 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6999, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.897379159927368, 'TIME_S_1KI': 2.9857664180493457, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1683.2534707903862, 'W': 70.47} +[39.8, 38.87, 39.22, 38.93, 38.9, 38.99, 38.89, 38.48, 38.43, 39.67, 39.45, 38.51, 39.01, 38.47, 38.42, 38.44, 39.43, 38.73, 38.56, 39.45] +699.4649999999999 +34.97324999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6999, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.897379159927368, 'TIME_S_1KI': 2.9857664180493457, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1683.2534707903862, 'W': 70.47, 'J_1KI': 240.49913856127822, 'W_1KI': 10.068581225889414, 'W_D': 35.496750000000006, 'J_D': 847.8789220842721, 'W_D_1KI': 5.071688812687528, 'J_D_1KI': 0.724630491882773} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json new file mode 100644 index 0000000..7504f86 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1085, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.97959613800049, "TIME_S_1KI": 19.336033306912892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2081.730987071991, "W": 77.68, "J_1KI": 1918.6460710340932, "W_1KI": 71.59447004608295, "W_D": 42.64725000000001, "J_D": 1142.8952347889544, "W_D_1KI": 39.306221198156685, "J_D_1KI": 36.22693197986791} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output new file mode 100644 index 0000000..4a5e75c --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 19.343926906585693} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 284, 572, ..., 12499476, + 12499735, 12500000]), + col_indices=tensor([ 166, 205, 430, ..., 49351, 49645, 49668]), + values=tensor([0.0452, 0.2727, 0.3621, ..., 0.2139, 0.8914, 0.4747]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.9835, 0.4490, 0.0492, ..., 0.9538, 0.9723, 0.7788]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 19.343926906585693 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1085', '-ss', '50000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.97959613800049} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 246, 489, ..., 12499492, + 12499760, 12500000]), + col_indices=tensor([ 25, 267, 758, ..., 49421, 49749, 49833]), + values=tensor([0.1040, 0.5728, 0.3234, ..., 0.7341, 0.5414, 0.1257]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.8524, 0.8502, 0.2995, ..., 0.6737, 0.1053, 0.0588]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 20.97959613800049 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 246, 489, ..., 12499492, + 12499760, 12500000]), + col_indices=tensor([ 25, 267, 758, ..., 49421, 49749, 49833]), + values=tensor([0.1040, 0.5728, 0.3234, ..., 0.7341, 0.5414, 0.1257]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.8524, 0.8502, 0.2995, ..., 0.6737, 0.1053, 0.0588]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 20.97959613800049 seconds + +[39.14, 39.08, 38.98, 38.87, 38.6, 38.74, 38.49, 39.78, 38.46, 38.7] +[77.68] +26.798802614212036 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1085, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.97959613800049, 'TIME_S_1KI': 19.336033306912892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2081.730987071991, 'W': 77.68} +[39.14, 39.08, 38.98, 38.87, 38.6, 38.74, 38.49, 39.78, 38.46, 38.7, 40.03, 38.43, 38.73, 38.45, 38.99, 38.54, 39.08, 39.02, 40.09, 38.78] +700.655 +35.03275 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1085, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.97959613800049, 'TIME_S_1KI': 19.336033306912892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2081.730987071991, 'W': 77.68, 'J_1KI': 1918.6460710340932, 'W_1KI': 71.59447004608295, 'W_D': 42.64725000000001, 'J_D': 1142.8952347889544, 'W_D_1KI': 39.306221198156685, 'J_D_1KI': 36.22693197986791} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..446b456 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 37.82172966003418, "TIME_S_1KI": 37.82172966003418, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3731.490132689476, "W": 78.5, "J_1KI": 3731.490132689476, "W_1KI": 78.5, "W_D": 43.14625000000001, "J_D": 2050.9529444274312, "W_D_1KI": 43.14625000000001, "J_D_1KI": 43.14625000000001} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..607e614 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 37.82172966003418} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 478, 967, ..., 24999023, + 24999517, 25000000]), + col_indices=tensor([ 55, 61, 67, ..., 49814, 49816, 49912]), + values=tensor([0.1124, 0.8573, 0.8758, ..., 0.9585, 0.5286, 0.9143]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.5266, 0.3019, 0.6446, ..., 0.2615, 0.0113, 0.3544]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 37.82172966003418 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 478, 967, ..., 24999023, + 24999517, 25000000]), + col_indices=tensor([ 55, 61, 67, ..., 49814, 49816, 49912]), + values=tensor([0.1124, 0.8573, 0.8758, ..., 0.9585, 0.5286, 0.9143]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.5266, 0.3019, 0.6446, ..., 0.2615, 0.0113, 0.3544]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 37.82172966003418 seconds + +[39.15, 39.92, 38.57, 38.55, 39.15, 44.43, 38.71, 39.58, 38.59, 38.46] +[78.5] +47.53490614891052 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 37.82172966003418, 'TIME_S_1KI': 37.82172966003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.490132689476, 'W': 78.5} +[39.15, 39.92, 38.57, 38.55, 39.15, 44.43, 38.71, 39.58, 38.59, 38.46, 40.51, 38.75, 38.64, 38.52, 38.8, 39.78, 38.55, 38.77, 39.3, 38.81] +707.0749999999998 +35.35374999999999 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 37.82172966003418, 'TIME_S_1KI': 37.82172966003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.490132689476, 'W': 78.5, 'J_1KI': 3731.490132689476, 'W_1KI': 78.5, 'W_D': 43.14625000000001, 'J_D': 2050.9529444274312, 'W_D_1KI': 43.14625000000001, 'J_D_1KI': 43.14625000000001} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..116c34c --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 71652, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.077060222625732, "TIME_S_1KI": 0.29415871465731214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1735.6051402235032, "W": 73.31, "J_1KI": 24.222703347059444, "W_1KI": 1.0231396192709206, "W_D": 28.61775, "J_D": 677.5216751006842, "W_D_1KI": 0.39939917936694025, "J_D_1KI": 0.005574152561923467} diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..b01c98d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.31334519386291504} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([33825, 38381, 10898, ..., 16973, 5749, 12690]), + values=tensor([0.0927, 0.2822, 0.8971, ..., 0.4021, 0.1329, 0.8374]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.5931, 0.9062, 0.9804, ..., 0.6131, 0.7776, 0.6003]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.31334519386291504 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67018', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 19.641794443130493} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 25000, 25000]), + col_indices=tensor([16677, 19807, 33770, ..., 39614, 2095, 28370]), + values=tensor([0.9725, 0.0867, 0.0870, ..., 0.5654, 0.5916, 0.4400]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.2944, 0.4597, 0.6320, ..., 0.6057, 0.1898, 0.1566]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 19.641794443130493 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71652', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.077060222625732} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 24998, 24998, 25000]), + col_indices=tensor([32921, 41293, 48516, ..., 42072, 6133, 17318]), + values=tensor([0.8803, 0.8660, 0.8154, ..., 0.4754, 0.7296, 0.3650]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.6139, 0.5193, 0.8626, ..., 0.9070, 0.3972, 0.6619]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 21.077060222625732 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 24998, 24998, 25000]), + col_indices=tensor([32921, 41293, 48516, ..., 42072, 6133, 17318]), + values=tensor([0.8803, 0.8660, 0.8154, ..., 0.4754, 0.7296, 0.3650]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.6139, 0.5193, 0.8626, ..., 0.9070, 0.3972, 0.6619]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 21.077060222625732 seconds + +[65.71, 63.81, 50.02, 60.96, 68.38, 61.87, 58.96, 64.5, 60.78, 39.15] +[73.31] +23.674875736236572 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 71652, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.077060222625732, 'TIME_S_1KI': 0.29415871465731214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1735.6051402235032, 'W': 73.31} +[65.71, 63.81, 50.02, 60.96, 68.38, 61.87, 58.96, 64.5, 60.78, 39.15, 39.3, 40.05, 38.69, 39.1, 39.07, 38.76, 39.16, 39.36, 38.75, 39.09] +893.845 +44.69225 +{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 71652, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.077060222625732, 'TIME_S_1KI': 0.29415871465731214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1735.6051402235032, 'W': 73.31, 'J_1KI': 24.222703347059444, 'W_1KI': 1.0231396192709206, 'W_D': 28.61775, 'J_D': 677.5216751006842, 'W_D_1KI': 0.39939917936694025, 'J_D_1KI': 0.005574152561923467} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json new file mode 100644 index 0000000..72b0c08 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 66.54721975326538, "TIME_S_1KI": 66.54721975326538, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3619.1285343217846, "W": 48.66, "J_1KI": 3619.1285343217846, "W_1KI": 48.66, "W_D": 32.3165, "J_D": 2403.5669395686386, "W_D_1KI": 32.3165, "J_D_1KI": 32.3165} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output new file mode 100644 index 0000000..88175d1 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '1000000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 66.54721975326538} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 21, ..., 9999976, + 9999986, 10000000]), + col_indices=tensor([ 54005, 89807, 113734, ..., 908702, 925766, + 933923]), + values=tensor([0.9939, 0.7767, 0.0078, ..., 0.2146, 0.1281, 0.5768]), + size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.8058, 0.8985, 0.9859, ..., 0.2784, 0.2654, 0.0031]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([1000000, 1000000]) +Rows: 1000000 +Size: 1000000000000 +NNZ: 10000000 +Density: 1e-05 +Time: 66.54721975326538 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 21, ..., 9999976, + 9999986, 10000000]), + col_indices=tensor([ 54005, 89807, 113734, ..., 908702, 925766, + 933923]), + values=tensor([0.9939, 0.7767, 0.0078, ..., 0.2146, 0.1281, 0.5768]), + size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.8058, 0.8985, 0.9859, ..., 0.2784, 0.2654, 0.0031]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([1000000, 1000000]) +Rows: 1000000 +Size: 1000000000000 +NNZ: 10000000 +Density: 1e-05 +Time: 66.54721975326538 seconds + +[18.44, 17.85, 17.91, 17.76, 18.24, 18.72, 18.35, 17.95, 18.1, 18.22] +[48.66] +74.37584328651428 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 66.54721975326538, 'TIME_S_1KI': 66.54721975326538, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3619.1285343217846, 'W': 48.66} +[18.44, 17.85, 17.91, 17.76, 18.24, 18.72, 18.35, 17.95, 18.1, 18.22, 18.26, 17.74, 18.13, 17.82, 17.99, 18.0, 18.47, 17.87, 17.8, 21.42] +326.87 +16.3435 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 66.54721975326538, 'TIME_S_1KI': 66.54721975326538, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3619.1285343217846, 'W': 48.66, 'J_1KI': 3619.1285343217846, 'W_1KI': 48.66, 'W_D': 32.3165, 'J_D': 2403.5669395686386, 'W_D_1KI': 32.3165, 'J_D_1KI': 32.3165} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..7aceaa7 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 7304, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.99964690208435, "TIME_S_1KI": 2.8750885681933664, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1205.7149387598038, "W": 48.21, "J_1KI": 165.07597737675297, "W_1KI": 6.600492880613363, "W_D": 31.907750000000004, "J_D": 798.001469346881, "W_D_1KI": 4.368530941949617, "J_D_1KI": 0.5981011694892684} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..00d043f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8750712871551514} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 18, ..., 999987, + 999994, 1000000]), + col_indices=tensor([ 72, 12664, 19832, ..., 78809, 83339, 93425]), + values=tensor([0.5785, 0.6431, 0.7942, ..., 0.9990, 0.0590, 0.8738]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6025, 0.9571, 0.3756, ..., 0.4137, 0.6855, 0.5355]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 2.8750712871551514 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', 'synthetic', 'csr', '7304', '-ss', '100000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.99964690208435} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 17, ..., 999983, + 999991, 1000000]), + col_indices=tensor([41096, 50256, 52141, ..., 67700, 76057, 98450]), + values=tensor([0.9809, 0.6280, 0.2788, ..., 0.6940, 0.2813, 0.2359]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.7723, 0.9147, 0.2969, ..., 0.3023, 0.2205, 0.3351]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 20.99964690208435 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 8, 17, ..., 999983, + 999991, 1000000]), + col_indices=tensor([41096, 50256, 52141, ..., 67700, 76057, 98450]), + values=tensor([0.9809, 0.6280, 0.2788, ..., 0.6940, 0.2813, 0.2359]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.7723, 0.9147, 0.2969, ..., 0.3023, 0.2205, 0.3351]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 20.99964690208435 seconds + +[19.43, 17.9, 18.08, 18.16, 18.32, 17.98, 18.9, 18.21, 18.25, 17.86] +[48.21] +25.009644031524658 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7304, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.99964690208435, 'TIME_S_1KI': 2.8750885681933664, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1205.7149387598038, 'W': 48.21} +[19.43, 17.9, 18.08, 18.16, 18.32, 17.98, 18.9, 18.21, 18.25, 17.86, 18.22, 17.66, 18.06, 18.08, 17.89, 18.02, 17.74, 17.97, 18.02, 18.1] +326.04499999999996 +16.302249999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7304, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.99964690208435, 'TIME_S_1KI': 2.8750885681933664, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1205.7149387598038, 'W': 48.21, 'J_1KI': 165.07597737675297, 'W_1KI': 6.600492880613363, 'W_D': 31.907750000000004, 'J_D': 798.001469346881, 'W_D_1KI': 4.368530941949617, 'J_D_1KI': 0.5981011694892684} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json new file mode 100644 index 0000000..62dfba2 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1831, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.986559629440308, "TIME_S_1KI": 11.461802091447463, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.7127650523184, "W": 48.96999999999999, "J_1KI": 710.9299645288469, "W_1KI": 26.74494811578372, "W_D": 32.56024999999999, "J_D": 865.5113959218857, "W_D_1KI": 17.782768978700158, "J_D_1KI": 9.712052964882664} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output new file mode 100644 index 0000000..1517cae --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 11.463933229446411} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 45, 93, ..., 4999886, + 4999950, 5000000]), + col_indices=tensor([ 115, 4142, 9033, ..., 95272, 97957, 99327]), + values=tensor([0.3001, 0.5395, 0.4547, ..., 0.4945, 0.6230, 0.1383]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.4447, 0.7495, 0.9824, ..., 0.5428, 0.2940, 0.4090]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 11.463933229446411 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', 'synthetic', 'csr', '1831', '-ss', '100000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.986559629440308} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 47, 90, ..., 4999886, + 4999944, 5000000]), + col_indices=tensor([ 1153, 2047, 4582, ..., 97809, 98430, 99156]), + values=tensor([0.1070, 0.5860, 0.6550, ..., 0.5725, 0.6043, 0.9156]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2942, 0.6403, 0.2970, ..., 0.2270, 0.6172, 0.6041]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 20.986559629440308 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 47, 90, ..., 4999886, + 4999944, 5000000]), + col_indices=tensor([ 1153, 2047, 4582, ..., 97809, 98430, 99156]), + values=tensor([0.1070, 0.5860, 0.6550, ..., 0.5725, 0.6043, 0.9156]), + size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.2942, 0.6403, 0.2970, ..., 0.2270, 0.6172, 0.6041]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 5000000 +Density: 0.0005 +Time: 20.986559629440308 seconds + +[18.11, 17.74, 18.14, 18.38, 18.12, 18.97, 17.92, 18.11, 17.89, 21.28] +[48.97] +26.581841230392456 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1831, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.986559629440308, 'TIME_S_1KI': 11.461802091447463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.7127650523184, 'W': 48.96999999999999} +[18.11, 17.74, 18.14, 18.38, 18.12, 18.97, 17.92, 18.11, 17.89, 21.28, 18.44, 17.94, 18.29, 17.78, 17.91, 18.22, 17.95, 18.14, 18.84, 17.88] +328.19500000000005 +16.409750000000003 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1831, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.986559629440308, 'TIME_S_1KI': 11.461802091447463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.7127650523184, 'W': 48.96999999999999, 'J_1KI': 710.9299645288469, 'W_1KI': 26.74494811578372, 'W_D': 32.56024999999999, 'J_D': 865.5113959218857, 'W_D_1KI': 17.782768978700158, 'J_D_1KI': 9.712052964882664} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json new file mode 100644 index 0000000..03d76fb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.55906629562378, "TIME_S_1KI": 27.55906629562378, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1724.2497406768798, "W": 48.51, "J_1KI": 1724.2497406768798, "W_1KI": 48.51, "W_D": 22.645749999999992, "J_D": 804.9253466281888, "W_D_1KI": 22.645749999999992, "J_D_1KI": 22.645749999999992} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output new file mode 100644 index 0000000..1b57712 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.55906629562378} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 125, 225, ..., 9999802, + 9999889, 10000000]), + col_indices=tensor([ 1628, 2146, 2363, ..., 95541, 97818, 98495]), + values=tensor([0.1115, 0.6662, 0.7909, ..., 0.2161, 0.9828, 0.9922]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.3917, 0.2257, 0.3594, ..., 0.6468, 0.3908, 0.8732]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 27.55906629562378 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 125, 225, ..., 9999802, + 9999889, 10000000]), + col_indices=tensor([ 1628, 2146, 2363, ..., 95541, 97818, 98495]), + values=tensor([0.1115, 0.6662, 0.7909, ..., 0.2161, 0.9828, 0.9922]), + size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.3917, 0.2257, 0.3594, ..., 0.6468, 0.3908, 0.8732]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 10000000 +Density: 0.001 +Time: 27.55906629562378 seconds + +[18.54, 17.96, 18.27, 17.87, 17.85, 17.91, 18.31, 18.22, 18.21, 17.84] +[48.51] +35.544212341308594 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.55906629562378, 'TIME_S_1KI': 27.55906629562378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1724.2497406768798, 'W': 48.51} +[18.54, 17.96, 18.27, 17.87, 17.85, 17.91, 18.31, 18.22, 18.21, 17.84, 39.12, 40.67, 39.37, 39.18, 39.34, 39.09, 38.69, 39.74, 39.23, 39.25] +517.2850000000001 +25.864250000000006 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.55906629562378, 'TIME_S_1KI': 27.55906629562378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1724.2497406768798, 'W': 48.51, 'J_1KI': 1724.2497406768798, 'W_1KI': 48.51, 'W_D': 22.645749999999992, 'J_D': 804.9253466281888, 'W_D_1KI': 22.645749999999992, 'J_D_1KI': 22.645749999999992} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json new file mode 100644 index 0000000..b8a679c --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 152.2796802520752, "TIME_S_1KI": 152.2796802520752, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24430.853182520867, "W": 40.51, "J_1KI": 24430.853182520867, "W_1KI": 40.51, "W_D": 24.2635, "J_D": 14632.880923083067, "W_D_1KI": 24.2635, "J_D_1KI": 24.2635} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output new file mode 100644 index 0000000..3163e2f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 152.2796802520752} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 501, 992, ..., 49999019, + 49999505, 50000000]), + col_indices=tensor([ 35, 43, 383, ..., 99897, 99938, 99967]), + values=tensor([0.4513, 0.8581, 0.1042, ..., 0.5255, 0.8133, 0.9103]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.9931, 0.5926, 0.5381, ..., 0.5378, 0.4212, 0.4881]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 152.2796802520752 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 501, 992, ..., 49999019, + 49999505, 50000000]), + col_indices=tensor([ 35, 43, 383, ..., 99897, 99938, 99967]), + values=tensor([0.4513, 0.8581, 0.1042, ..., 0.5255, 0.8133, 0.9103]), + size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr) +tensor([0.9931, 0.5926, 0.5381, ..., 0.5378, 0.4212, 0.4881]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 50000000 +Density: 0.005 +Time: 152.2796802520752 seconds + +[18.54, 17.77, 17.99, 18.91, 17.96, 17.85, 17.94, 17.96, 17.92, 17.99] +[40.51] +603.0820336341858 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 152.2796802520752, 'TIME_S_1KI': 152.2796802520752, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24430.853182520867, 'W': 40.51} +[18.54, 17.77, 17.99, 18.91, 17.96, 17.85, 17.94, 17.96, 17.92, 17.99, 18.55, 18.48, 18.08, 18.21, 17.89, 17.61, 18.0, 17.78, 18.03, 18.02] +324.92999999999995 +16.246499999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 152.2796802520752, 'TIME_S_1KI': 152.2796802520752, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24430.853182520867, 'W': 40.51, 'J_1KI': 24430.853182520867, 'W_1KI': 40.51, 'W_D': 24.2635, 'J_D': 14632.880923083067, 'W_D_1KI': 24.2635, 'J_D_1KI': 24.2635} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..0a18e77 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 15982, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.790293216705322, "TIME_S_1KI": 1.300856789932757, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1154.2612649059295, "W": 46.92999999999999, "J_1KI": 72.22257945851142, "W_1KI": 2.936428482042297, "W_D": 30.483749999999993, "J_D": 749.7594680178164, "W_D_1KI": 1.907380177699912, "J_D_1KI": 0.11934552482166888} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..2e16cf3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3139328956604004} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 100000, 100000, + 100000]), + col_indices=tensor([39907, 27987, 76798, ..., 32180, 99907, 17440]), + values=tensor([0.1487, 0.6263, 0.1935, ..., 0.3652, 0.0716, 0.9913]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.5233, 0.9086, 0.0476, ..., 0.5287, 0.8958, 0.9684]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 1.3139328956604004 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', 'synthetic', 'csr', '15982', '-ss', '100000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.790293216705322} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 99999, 99999, + 100000]), + col_indices=tensor([ 6594, 93201, 43608, ..., 41278, 68005, 16586]), + values=tensor([0.8802, 0.5778, 0.4721, ..., 0.6728, 0.8802, 0.6767]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6356, 0.8811, 0.4031, ..., 0.8985, 0.9999, 0.3835]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 20.790293216705322 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 99999, 99999, + 100000]), + col_indices=tensor([ 6594, 93201, 43608, ..., 41278, 68005, 16586]), + values=tensor([0.8802, 0.5778, 0.4721, ..., 0.6728, 0.8802, 0.6767]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6356, 0.8811, 0.4031, ..., 0.8985, 0.9999, 0.3835]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 20.790293216705322 seconds + +[18.44, 18.07, 17.97, 17.94, 21.94, 18.0, 18.46, 18.26, 18.2, 18.27] +[46.93] +24.59538173675537 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 15982, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.790293216705322, 'TIME_S_1KI': 1.300856789932757, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1154.2612649059295, 'W': 46.92999999999999} +[18.44, 18.07, 17.97, 17.94, 21.94, 18.0, 18.46, 18.26, 18.2, 18.27, 18.3, 17.85, 18.0, 18.05, 18.14, 17.86, 17.89, 17.78, 18.15, 17.72] +328.925 +16.44625 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 15982, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.790293216705322, 'TIME_S_1KI': 1.300856789932757, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1154.2612649059295, 'W': 46.92999999999999, 'J_1KI': 72.22257945851142, 'W_1KI': 2.936428482042297, 'W_D': 30.483749999999993, 'J_D': 749.7594680178164, 'W_D_1KI': 1.907380177699912, 'J_D_1KI': 0.11934552482166888} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output new file mode 100644 index 0000000..95dcd7c --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output @@ -0,0 +1,10 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1'] +Traceback (most recent call last): + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in + program_result = run_program(program( + ^^^^^^^^^^^^^^^^^^^^ + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program + process.check_returncode() + File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode + raise CalledProcessError(self.returncode, self.args, self.stdout, +subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1']' died with . diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json new file mode 100644 index 0000000..b220cd4 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 43.59297776222229, "TIME_S_1KI": 43.59297776222229, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2443.445860421658, "W": 47.95000000000001, "J_1KI": 2443.445860421658, "W_1KI": 47.95000000000001, "W_D": 31.519750000000013, "J_D": 1606.1898364760286, "W_D_1KI": 31.519750000000013, "J_D_1KI": 31.519750000000013} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output new file mode 100644 index 0000000..48e92e3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 43.59297776222229} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 25, 55, ..., 8999936, + 8999970, 9000000]), + col_indices=tensor([ 9127, 10614, 42656, ..., 264952, 278523, + 294763]), + values=tensor([0.1591, 0.4772, 0.9607, ..., 0.8861, 0.4140, 0.1211]), + size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.3992, 0.8236, 0.1831, ..., 0.0857, 0.3847, 0.6830]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 9000000 +Density: 0.0001 +Time: 43.59297776222229 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 25, 55, ..., 8999936, + 8999970, 9000000]), + col_indices=tensor([ 9127, 10614, 42656, ..., 264952, 278523, + 294763]), + values=tensor([0.1591, 0.4772, 0.9607, ..., 0.8861, 0.4140, 0.1211]), + size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.3992, 0.8236, 0.1831, ..., 0.0857, 0.3847, 0.6830]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 9000000 +Density: 0.0001 +Time: 43.59297776222229 seconds + +[18.34, 18.1, 18.78, 20.56, 17.99, 18.51, 17.93, 18.03, 17.94, 18.04] +[47.95] +50.95820355415344 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 43.59297776222229, 'TIME_S_1KI': 43.59297776222229, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2443.445860421658, 'W': 47.95000000000001} +[18.34, 18.1, 18.78, 20.56, 17.99, 18.51, 17.93, 18.03, 17.94, 18.04, 18.97, 18.18, 18.43, 17.75, 17.93, 17.87, 17.94, 18.08, 17.97, 17.88] +328.60499999999996 +16.430249999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 43.59297776222229, 'TIME_S_1KI': 43.59297776222229, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2443.445860421658, 'W': 47.95000000000001, 'J_1KI': 2443.445860421658, 'W_1KI': 47.95000000000001, 'W_D': 31.519750000000013, 'J_D': 1606.1898364760286, 'W_D_1KI': 31.519750000000013, 'J_D_1KI': 31.519750000000013} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json new file mode 100644 index 0000000..efafaea --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 233.5992555618286, "TIME_S_1KI": 233.5992555618286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24127.435356621743, "W": 44.42, "J_1KI": 24127.435356621743, "W_1KI": 44.42, "W_D": 28.19375, "J_D": 15313.887451277675, "W_D_1KI": 28.19375, "J_D_1KI": 28.19375} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output new file mode 100644 index 0000000..f20383f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 233.5992555618286} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 130, 284, ..., 44999682, + 44999844, 45000000]), + col_indices=tensor([ 1852, 3586, 4765, ..., 295056, 296384, + 297411]), + values=tensor([0.2696, 0.5396, 0.2299, ..., 0.9264, 0.4734, 0.5186]), + size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.0972, 0.1995, 0.9087, ..., 0.4631, 0.8051, 0.0013]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 45000000 +Density: 0.0005 +Time: 233.5992555618286 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 130, 284, ..., 44999682, + 44999844, 45000000]), + col_indices=tensor([ 1852, 3586, 4765, ..., 295056, 296384, + 297411]), + values=tensor([0.2696, 0.5396, 0.2299, ..., 0.9264, 0.4734, 0.5186]), + size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr) +tensor([0.0972, 0.1995, 0.9087, ..., 0.4631, 0.8051, 0.0013]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 45000000 +Density: 0.0005 +Time: 233.5992555618286 seconds + +[18.63, 18.36, 17.97, 17.94, 18.0, 17.84, 17.92, 17.85, 18.12, 17.92] +[44.42] +543.1660368442535 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 233.5992555618286, 'TIME_S_1KI': 233.5992555618286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24127.435356621743, 'W': 44.42} +[18.63, 18.36, 17.97, 17.94, 18.0, 17.84, 17.92, 17.85, 18.12, 17.92, 18.74, 17.84, 18.23, 17.69, 18.02, 17.95, 18.42, 17.86, 17.99, 17.76] +324.525 +16.22625 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 233.5992555618286, 'TIME_S_1KI': 233.5992555618286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24127.435356621743, 'W': 44.42, 'J_1KI': 24127.435356621743, 'W_1KI': 44.42, 'W_D': 28.19375, 'J_D': 15313.887451277675, 'W_D_1KI': 28.19375, 'J_D_1KI': 28.19375} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output new file mode 100644 index 0000000..30ff4c4 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output @@ -0,0 +1,10 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1'] +Traceback (most recent call last): + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in + program_result = run_program(program( + ^^^^^^^^^^^^^^^^^^^^ + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program + process.check_returncode() + File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode + raise CalledProcessError(self.returncode, self.args, self.stdout, +subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1']' died with . diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json new file mode 100644 index 0000000..49d4d09 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3598, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.015570878982544, "TIME_S_1KI": 5.840903523897316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1199.0254331445694, "W": 48.06, "J_1KI": 333.24775796124777, "W_1KI": 13.357420789327406, "W_D": 31.746750000000002, "J_D": 792.0341379459501, "W_D_1KI": 8.823443579766536, "J_D_1KI": 2.4523189493514552} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output new file mode 100644 index 0000000..46d6c02 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.835606575012207} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 7, ..., 899996, 899998, + 900000]), + col_indices=tensor([100602, 129512, 176801, ..., 48622, 26613, + 190176]), + values=tensor([0.1487, 0.8854, 0.0841, ..., 0.8808, 0.2948, 0.6815]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.5133, 0.8774, 0.0043, ..., 0.0470, 0.1306, 0.4977]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 5.835606575012207 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', 'synthetic', 'csr', '3598', '-ss', '300000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.015570878982544} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 5, ..., 899988, 899992, + 900000]), + col_indices=tensor([ 18941, 81855, 33867, ..., 201457, 255893, + 299263]), + values=tensor([0.5587, 0.5974, 0.8127, ..., 0.5995, 0.0776, 0.5594]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.7686, 0.4534, 0.3324, ..., 0.2462, 0.7149, 0.9702]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 21.015570878982544 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 5, ..., 899988, 899992, + 900000]), + col_indices=tensor([ 18941, 81855, 33867, ..., 201457, 255893, + 299263]), + values=tensor([0.5587, 0.5974, 0.8127, ..., 0.5995, 0.0776, 0.5594]), + size=(300000, 300000), nnz=900000, layout=torch.sparse_csr) +tensor([0.7686, 0.4534, 0.3324, ..., 0.2462, 0.7149, 0.9702]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([300000, 300000]) +Rows: 300000 +Size: 90000000000 +NNZ: 900000 +Density: 1e-05 +Time: 21.015570878982544 seconds + +[18.25, 18.65, 17.9, 17.84, 17.95, 18.05, 18.67, 17.88, 18.3, 18.01] +[48.06] +24.948510885238647 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.015570878982544, 'TIME_S_1KI': 5.840903523897316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.0254331445694, 'W': 48.06} +[18.25, 18.65, 17.9, 17.84, 17.95, 18.05, 18.67, 17.88, 18.3, 18.01, 18.4, 18.0, 17.84, 18.93, 18.16, 17.77, 18.14, 17.83, 18.06, 17.93] +326.265 +16.31325 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.015570878982544, 'TIME_S_1KI': 5.840903523897316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.0254331445694, 'W': 48.06, 'J_1KI': 333.24775796124777, 'W_1KI': 13.357420789327406, 'W_D': 31.746750000000002, 'J_D': 792.0341379459501, 'W_D_1KI': 8.823443579766536, 'J_D_1KI': 2.4523189493514552} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json new file mode 100644 index 0000000..aa132a9 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 33277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.4328293800354, "TIME_S_1KI": 0.6140225795605192, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1130.1519185829163, "W": 46.86, "J_1KI": 33.96195325849435, "W_1KI": 1.408179823902395, "W_D": 30.543249999999997, "J_D": 736.6306570050716, "W_D_1KI": 0.9178486642425698, "J_D_1KI": 0.027582073631714693} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output new file mode 100644 index 0000000..5969df1 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.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', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6310491561889648} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 7, ..., 89994, 89997, 90000]), + col_indices=tensor([ 2667, 5647, 6980, ..., 2168, 3268, 28772]), + values=tensor([0.1347, 0.9532, 0.3607, ..., 0.9962, 0.2520, 0.4682]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.4612, 0.6559, 0.6162, ..., 0.2529, 0.9562, 0.7602]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 0.6310491561889648 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', 'synthetic', 'csr', '33277', '-ss', '30000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.4328293800354} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 6, ..., 89998, 89999, 90000]), + col_indices=tensor([13602, 24899, 9076, ..., 25653, 15048, 9911]), + values=tensor([0.7061, 0.1886, 0.4037, ..., 0.8692, 0.8414, 0.2535]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.8274, 0.3618, 0.1359, ..., 0.5733, 0.1118, 0.0977]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.4328293800354 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 6, ..., 89998, 89999, 90000]), + col_indices=tensor([13602, 24899, 9076, ..., 25653, 15048, 9911]), + values=tensor([0.7061, 0.1886, 0.4037, ..., 0.8692, 0.8414, 0.2535]), + size=(30000, 30000), nnz=90000, layout=torch.sparse_csr) +tensor([0.8274, 0.3618, 0.1359, ..., 0.5733, 0.1118, 0.0977]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 90000 +Density: 0.0001 +Time: 20.4328293800354 seconds + +[18.45, 18.04, 18.2, 18.12, 18.16, 18.2, 18.24, 17.92, 17.99, 18.02] +[46.86] +24.11762523651123 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.4328293800354, 'TIME_S_1KI': 0.6140225795605192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1130.1519185829163, 'W': 46.86} +[18.45, 18.04, 18.2, 18.12, 18.16, 18.2, 18.24, 17.92, 17.99, 18.02, 18.46, 18.14, 18.16, 18.26, 17.95, 18.13, 18.04, 18.32, 17.97, 18.06] +326.33500000000004 +16.316750000000003 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.4328293800354, 'TIME_S_1KI': 0.6140225795605192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1130.1519185829163, 'W': 46.86, 'J_1KI': 33.96195325849435, 'W_1KI': 1.408179823902395, 'W_D': 30.543249999999997, 'J_D': 736.6306570050716, 'W_D_1KI': 0.9178486642425698, 'J_D_1KI': 0.027582073631714693} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json new file mode 100644 index 0000000..e7b3ed3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18733, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.639018058776855, "TIME_S_1KI": 1.1017465466704133, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.502190952301, "W": 47.87, "J_1KI": 62.75034382919452, "W_1KI": 2.55538354774996, "W_D": 31.414249999999996, "J_D": 771.4125695033073, "W_D_1KI": 1.6769470987028237, "J_D_1KI": 0.0895183418941346} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output new file mode 100644 index 0000000..2f47860 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 1.1210110187530518} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 29, ..., 449963, 449981, + 450000]), + col_indices=tensor([ 792, 5705, 11402, ..., 28541, 29300, 29723]), + values=tensor([0.4108, 0.9785, 0.1600, ..., 0.6171, 0.0607, 0.2902]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.8871, 0.5462, 0.8388, ..., 0.2778, 0.5429, 0.3514]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 1.1210110187530518 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', 'synthetic', 'csr', '18733', '-ss', '30000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.639018058776855} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 20, ..., 449977, 449985, + 450000]), + col_indices=tensor([ 220, 1019, 9874, ..., 23783, 27634, 29111]), + values=tensor([0.9174, 0.1323, 0.6653, ..., 0.3636, 0.2491, 0.8467]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.9099, 0.7629, 0.0246, ..., 0.7433, 0.9009, 0.8261]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 20.639018058776855 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 20, ..., 449977, 449985, + 450000]), + col_indices=tensor([ 220, 1019, 9874, ..., 23783, 27634, 29111]), + values=tensor([0.9174, 0.1323, 0.6653, ..., 0.3636, 0.2491, 0.8467]), + size=(30000, 30000), nnz=450000, layout=torch.sparse_csr) +tensor([0.9099, 0.7629, 0.0246, ..., 0.7433, 0.9009, 0.8261]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 450000 +Density: 0.0005 +Time: 20.639018058776855 seconds + +[18.47, 17.86, 18.03, 18.06, 18.18, 18.04, 18.02, 21.6, 18.44, 17.85] +[47.87] +24.556135177612305 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.639018058776855, 'TIME_S_1KI': 1.1017465466704133, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.502190952301, 'W': 47.87} +[18.47, 17.86, 18.03, 18.06, 18.18, 18.04, 18.02, 21.6, 18.44, 17.85, 18.05, 18.82, 17.94, 18.23, 17.84, 17.88, 17.89, 18.05, 18.07, 17.96] +329.11500000000007 +16.455750000000002 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.639018058776855, 'TIME_S_1KI': 1.1017465466704133, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.502190952301, 'W': 47.87, 'J_1KI': 62.75034382919452, 'W_1KI': 2.55538354774996, 'W_D': 31.414249999999996, 'J_D': 771.4125695033073, 'W_D_1KI': 1.6769470987028237, 'J_D_1KI': 0.0895183418941346} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json new file mode 100644 index 0000000..a79d073 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11640, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.839291095733643, "TIME_S_1KI": 1.790317104444471, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1197.1045216155053, "W": 48.31, "J_1KI": 102.84403106662417, "W_1KI": 4.150343642611684, "W_D": 31.900750000000002, "J_D": 790.4891754900814, "W_D_1KI": 2.7406142611683855, "J_D_1KI": 0.2354479605814764} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output new file mode 100644 index 0000000..4ec6b31 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.804121494293213} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 61, ..., 899944, 899973, + 900000]), + col_indices=tensor([ 214, 468, 621, ..., 27947, 28785, 29886]), + values=tensor([0.5497, 0.2471, 0.3999, ..., 0.8981, 0.5437, 0.4393]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.3080, 0.4231, 0.6575, ..., 0.3533, 0.8148, 0.0442]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 1.804121494293213 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', 'synthetic', 'csr', '11640', '-ss', '30000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.839291095733643} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 32, 54, ..., 899931, 899971, + 900000]), + col_indices=tensor([ 1264, 2511, 3373, ..., 24630, 25069, 29984]), + values=tensor([0.3611, 0.1242, 0.5465, ..., 0.3866, 0.2436, 0.7931]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.0647, 0.2891, 0.3015, ..., 0.6009, 0.0656, 0.2202]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.839291095733643 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 32, 54, ..., 899931, 899971, + 900000]), + col_indices=tensor([ 1264, 2511, 3373, ..., 24630, 25069, 29984]), + values=tensor([0.3611, 0.1242, 0.5465, ..., 0.3866, 0.2436, 0.7931]), + size=(30000, 30000), nnz=900000, layout=torch.sparse_csr) +tensor([0.0647, 0.2891, 0.3015, ..., 0.6009, 0.0656, 0.2202]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 900000 +Density: 0.001 +Time: 20.839291095733643 seconds + +[18.3, 17.96, 21.33, 17.75, 18.29, 18.22, 17.9, 17.9, 18.0, 18.1] +[48.31] +24.779642343521118 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.839291095733643, 'TIME_S_1KI': 1.790317104444471, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.1045216155053, 'W': 48.31} +[18.3, 17.96, 21.33, 17.75, 18.29, 18.22, 17.9, 17.9, 18.0, 18.1, 18.54, 17.81, 18.9, 17.85, 18.01, 17.86, 18.01, 17.93, 18.05, 17.89] +328.185 +16.40925 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.839291095733643, 'TIME_S_1KI': 1.790317104444471, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.1045216155053, 'W': 48.31, 'J_1KI': 102.84403106662417, 'W_1KI': 4.150343642611684, 'W_D': 31.900750000000002, 'J_D': 790.4891754900814, 'W_D_1KI': 2.7406142611683855, 'J_D_1KI': 0.2354479605814764} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json new file mode 100644 index 0000000..4201fbb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2157, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.902870178222656, "TIME_S_1KI": 9.690714037191773, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.0206210327149, "W": 48.72, "J_1KI": 600.3804455413606, "W_1KI": 22.58692628650904, "W_D": 32.4265, "J_D": 861.9250034465789, "W_D_1KI": 15.03314789058878, "J_D_1KI": 6.96947051024051} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output new file mode 100644 index 0000000..98b9204 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 9.735076665878296} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 157, 297, ..., 4499702, + 4499846, 4500000]), + col_indices=tensor([ 52, 107, 115, ..., 29647, 29660, 29851]), + values=tensor([0.0696, 0.1442, 0.4515, ..., 0.9885, 0.1135, 0.9052]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.6712, 0.0504, 0.9004, ..., 0.5534, 0.8230, 0.9335]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 9.735076665878296 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', 'synthetic', 'csr', '2157', '-ss', '30000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.902870178222656} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 143, 285, ..., 4499715, + 4499860, 4500000]), + col_indices=tensor([ 156, 239, 621, ..., 29559, 29678, 29713]), + values=tensor([0.8567, 0.6051, 0.6450, ..., 0.7880, 0.1108, 0.6079]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.9332, 0.3072, 0.5823, ..., 0.4039, 0.3932, 0.8837]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 20.902870178222656 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 143, 285, ..., 4499715, + 4499860, 4500000]), + col_indices=tensor([ 156, 239, 621, ..., 29559, 29678, 29713]), + values=tensor([0.8567, 0.6051, 0.6450, ..., 0.7880, 0.1108, 0.6079]), + size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr) +tensor([0.9332, 0.3072, 0.5823, ..., 0.4039, 0.3932, 0.8837]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 4500000 +Density: 0.005 +Time: 20.902870178222656 seconds + +[18.31, 18.87, 18.08, 17.97, 18.03, 17.81, 18.09, 17.94, 17.97, 17.95] +[48.72] +26.580883026123047 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2157, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.902870178222656, 'TIME_S_1KI': 9.690714037191773, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.0206210327149, 'W': 48.72} +[18.31, 18.87, 18.08, 17.97, 18.03, 17.81, 18.09, 17.94, 17.97, 17.95, 18.88, 17.75, 18.5, 17.89, 18.12, 18.19, 18.06, 17.82, 18.14, 18.14] +325.87 +16.2935 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2157, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.902870178222656, 'TIME_S_1KI': 9.690714037191773, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.0206210327149, 'W': 48.72, 'J_1KI': 600.3804455413606, 'W_1KI': 22.58692628650904, 'W_D': 32.4265, 'J_D': 861.9250034465789, 'W_D_1KI': 15.03314789058878, 'J_D_1KI': 6.96947051024051} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json new file mode 100644 index 0000000..46b364e --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 24.740506410598755, "TIME_S_1KI": 24.740506410598755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1555.6125507831573, "W": 48.26, "J_1KI": 1555.6125507831573, "W_1KI": 48.26, "W_D": 31.86275, "J_D": 1027.063692550063, "W_D_1KI": 31.862749999999995, "J_D_1KI": 31.862749999999995} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output new file mode 100644 index 0000000..5a27d6d --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 24.740506410598755} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 261, 540, ..., 8999386, + 8999702, 9000000]), + col_indices=tensor([ 87, 89, 474, ..., 29936, 29945, 29986]), + values=tensor([0.1960, 0.4552, 0.3026, ..., 0.0541, 0.8647, 0.1885]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.5184, 0.4657, 0.5829, ..., 0.6124, 0.1016, 0.6788]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 24.740506410598755 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 261, 540, ..., 8999386, + 8999702, 9000000]), + col_indices=tensor([ 87, 89, 474, ..., 29936, 29945, 29986]), + values=tensor([0.1960, 0.4552, 0.3026, ..., 0.0541, 0.8647, 0.1885]), + size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr) +tensor([0.5184, 0.4657, 0.5829, ..., 0.6124, 0.1016, 0.6788]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000000 +Density: 0.01 +Time: 24.740506410598755 seconds + +[18.57, 18.03, 17.81, 17.79, 18.07, 17.83, 17.76, 17.79, 22.13, 17.94] +[48.26] +32.233994007110596 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 24.740506410598755, 'TIME_S_1KI': 24.740506410598755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1555.6125507831573, 'W': 48.26} +[18.57, 18.03, 17.81, 17.79, 18.07, 17.83, 17.76, 17.79, 22.13, 17.94, 18.41, 18.28, 18.04, 17.85, 18.14, 17.76, 17.85, 17.98, 18.37, 18.01] +327.945 +16.39725 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 24.740506410598755, 'TIME_S_1KI': 24.740506410598755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1555.6125507831573, 'W': 48.26, 'J_1KI': 1555.6125507831573, 'W_1KI': 48.26, 'W_D': 31.86275, 'J_D': 1027.063692550063, 'W_D_1KI': 31.862749999999995, 'J_D_1KI': 31.862749999999995} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output new file mode 100644 index 0000000..1e221b3 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output @@ -0,0 +1,10 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1'] +Traceback (most recent call last): + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in + program_result = run_program(program( + ^^^^^^^^^^^^^^^^^^^^ + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program + process.check_returncode() + File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode + raise CalledProcessError(self.returncode, self.args, self.stdout, +subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1']' died with . diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.json new file mode 100644 index 0000000..e69de29 diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output new file mode 100644 index 0000000..3d666e4 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output @@ -0,0 +1,10 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1'] +Traceback (most recent call last): + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in + program_result = run_program(program( + ^^^^^^^^^^^^^^^^^^^^ + File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program + process.check_returncode() + File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode + raise CalledProcessError(self.returncode, self.args, self.stdout, +subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1']' died with . diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json new file mode 100644 index 0000000..0a79d7f --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 107895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.02440118789673, "TIME_S_1KI": 0.1948598284248272, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1153.7907242584229, "W": 46.480000000000004, "J_1KI": 10.69364404521454, "W_1KI": 0.4307891931970898, "W_D": 30.00775, "J_D": 744.893795306921, "W_D_1KI": 0.2781199314148014, "J_D_1KI": 0.002577690638257578} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output new file mode 100644 index 0000000..b924b07 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.21645355224609375} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 8999, 9000, 9000]), + col_indices=tensor([14460, 16831, 822, ..., 6744, 9809, 7337]), + values=tensor([0.8017, 0.3190, 0.3138, ..., 0.7835, 0.9662, 0.5600]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.9173, 0.3762, 0.0968, ..., 0.4714, 0.2077, 0.2375]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 0.21645355224609375 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', 'synthetic', 'csr', '97018', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 18.88284707069397} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 9000, 9000, 9000]), + col_indices=tensor([ 9904, 14426, 27453, ..., 21883, 11984, 20369]), + values=tensor([0.0383, 0.4855, 0.7841, ..., 0.2563, 0.0898, 0.2306]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.3139, 0.0953, 0.3077, ..., 0.3327, 0.7858, 0.4046]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 18.88284707069397 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', 'synthetic', 'csr', '107895', '-ss', '30000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.02440118789673} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 9000, 9000, 9000]), + col_indices=tensor([ 4725, 5479, 22893, ..., 1358, 17086, 18996]), + values=tensor([0.5818, 0.4877, 0.3711, ..., 0.0217, 0.6305, 0.8996]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.9504, 0.0026, 0.0759, ..., 0.7751, 0.7432, 0.9903]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.02440118789673 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 9000, 9000, 9000]), + col_indices=tensor([ 4725, 5479, 22893, ..., 1358, 17086, 18996]), + values=tensor([0.5818, 0.4877, 0.3711, ..., 0.0217, 0.6305, 0.8996]), + size=(30000, 30000), nnz=9000, layout=torch.sparse_csr) +tensor([0.9504, 0.0026, 0.0759, ..., 0.7751, 0.7432, 0.9903]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([30000, 30000]) +Rows: 30000 +Size: 900000000 +NNZ: 9000 +Density: 1e-05 +Time: 21.02440118789673 seconds + +[18.53, 17.93, 18.18, 18.25, 18.09, 18.04, 18.17, 18.3, 18.1, 18.0] +[46.48] +24.82338047027588 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 107895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.02440118789673, 'TIME_S_1KI': 0.1948598284248272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1153.7907242584229, 'W': 46.480000000000004} +[18.53, 17.93, 18.18, 18.25, 18.09, 18.04, 18.17, 18.3, 18.1, 18.0, 21.62, 17.95, 18.69, 18.08, 18.06, 18.24, 18.11, 18.56, 18.5, 18.24] +329.44500000000005 +16.472250000000003 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 107895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.02440118789673, 'TIME_S_1KI': 0.1948598284248272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1153.7907242584229, 'W': 46.480000000000004, 'J_1KI': 10.69364404521454, 'W_1KI': 0.4307891931970898, 'W_D': 30.00775, 'J_D': 744.893795306921, 'W_D_1KI': 0.2781199314148014, 'J_D_1KI': 0.002577690638257578} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json new file mode 100644 index 0000000..bdc1501 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 145.63775444030762, "TIME_S_1KI": 145.63775444030762, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8970.34398475647, "W": 47.33, "J_1KI": 8970.34398475647, "W_1KI": 47.33, "W_D": 20.887749999999997, "J_D": 3958.806308210372, "W_D_1KI": 20.887749999999997, "J_D_1KI": 20.887749999999997} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output new file mode 100644 index 0000000..53f20ab --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 145.63775444030762} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 54, 110, ..., 24999911, + 24999950, 25000000]), + col_indices=tensor([ 8959, 17884, 23107, ..., 479254, 480973, + 488093]), + values=tensor([0.9355, 0.2752, 0.4481, ..., 0.8378, 0.5445, 0.7672]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.8528, 0.7383, 0.2866, ..., 0.1948, 0.0294, 0.9953]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 145.63775444030762 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 54, 110, ..., 24999911, + 24999950, 25000000]), + col_indices=tensor([ 8959, 17884, 23107, ..., 479254, 480973, + 488093]), + values=tensor([0.9355, 0.2752, 0.4481, ..., 0.8378, 0.5445, 0.7672]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.8528, 0.7383, 0.2866, ..., 0.1948, 0.0294, 0.9953]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 145.63775444030762 seconds + +[18.47, 18.19, 18.09, 17.89, 17.96, 17.92, 17.83, 17.75, 17.84, 19.05] +[47.33] +189.52765655517578 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 145.63775444030762, 'TIME_S_1KI': 145.63775444030762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8970.34398475647, 'W': 47.33} +[18.47, 18.19, 18.09, 17.89, 17.96, 17.92, 17.83, 17.75, 17.84, 19.05, 46.69, 47.22, 47.59, 46.72, 46.73, 40.63, 39.32, 36.13, 23.63, 30.6] +528.845 +26.44225 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 145.63775444030762, 'TIME_S_1KI': 145.63775444030762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8970.34398475647, 'W': 47.33, 'J_1KI': 8970.34398475647, 'W_1KI': 47.33, 'W_D': 20.887749999999997, 'J_D': 3958.806308210372, 'W_D_1KI': 20.887749999999997, 'J_D_1KI': 20.887749999999997} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..4c5f481 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1584, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.39402437210083, "TIME_S_1KI": 13.506328517740423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.1197284793855, "W": 49.09, "J_1KI": 801.2119497975918, "W_1KI": 30.99116161616162, "W_D": 32.84400000000001, "J_D": 849.1132279930117, "W_D_1KI": 20.73484848484849, "J_D_1KI": 13.090182124273037} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..fddfb78 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.255852460861206} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 12, ..., 2499986, + 2499994, 2500000]), + col_indices=tensor([ 32665, 199892, 257011, ..., 396065, 419080, + 487395]), + values=tensor([0.3748, 0.5935, 0.2005, ..., 0.1065, 0.8464, 0.2707]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.0132, 0.8881, 0.5277, ..., 0.7521, 0.8271, 0.6760]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 13.255852460861206 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', 'synthetic', 'csr', '1584', '-ss', '500000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.39402437210083} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 15, ..., 2499992, + 2499996, 2500000]), + col_indices=tensor([ 21234, 111933, 179128, ..., 123034, 350119, + 388488]), + values=tensor([0.5221, 0.0977, 0.5310, ..., 0.7164, 0.7480, 0.4663]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3196, 0.7899, 0.9317, ..., 0.3730, 0.0273, 0.1855]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 21.39402437210083 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 15, ..., 2499992, + 2499996, 2500000]), + col_indices=tensor([ 21234, 111933, 179128, ..., 123034, 350119, + 388488]), + values=tensor([0.5221, 0.0977, 0.5310, ..., 0.7164, 0.7480, 0.4663]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3196, 0.7899, 0.9317, ..., 0.3730, 0.0273, 0.1855]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 21.39402437210083 seconds + +[18.22, 18.42, 18.08, 18.01, 17.97, 17.95, 18.16, 18.01, 18.24, 17.86] +[49.09] +25.852917671203613 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1584, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.39402437210083, 'TIME_S_1KI': 13.506328517740423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.1197284793855, 'W': 49.09} +[18.22, 18.42, 18.08, 18.01, 17.97, 17.95, 18.16, 18.01, 18.24, 17.86, 18.17, 18.05, 17.89, 17.96, 18.01, 17.98, 17.96, 17.9, 18.04, 18.33] +324.91999999999996 +16.246 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1584, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.39402437210083, 'TIME_S_1KI': 13.506328517740423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.1197284793855, 'W': 49.09, 'J_1KI': 801.2119497975918, 'W_1KI': 30.99116161616162, 'W_D': 32.84400000000001, 'J_D': 849.1132279930117, 'W_D_1KI': 20.73484848484849, 'J_D_1KI': 13.090182124273037} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..e4cd738 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18145, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.748866319656372, "TIME_S_1KI": 1.1435032416454325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1163.7240363693238, "W": 47.52, "J_1KI": 64.13469475719613, "W_1KI": 2.6189032791402593, "W_D": 31.13875, "J_D": 762.5612760415673, "W_D_1KI": 1.7161063653899147, "J_D_1KI": 0.09457736926921546} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..a874d29 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1573309898376465} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 12, ..., 249987, 249995, + 250000]), + col_indices=tensor([ 2662, 3637, 9309, ..., 20434, 25231, 37285]), + values=tensor([0.2333, 0.4961, 0.7423, ..., 0.5095, 0.9257, 0.2343]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.2799, 0.1370, 0.8773, ..., 0.1897, 0.8081, 0.5839]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 1.1573309898376465 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', 'synthetic', 'csr', '18145', '-ss', '50000', '-sd', '0.0001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.748866319656372} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 8, ..., 249992, 249993, + 250000]), + col_indices=tensor([ 4160, 33356, 44413, ..., 34267, 38517, 46233]), + values=tensor([0.6958, 0.8946, 0.2330, ..., 0.2200, 0.1570, 0.6240]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.7770, 0.7832, 0.7648, ..., 0.3539, 0.1104, 0.8005]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.748866319656372 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 8, ..., 249992, 249993, + 250000]), + col_indices=tensor([ 4160, 33356, 44413, ..., 34267, 38517, 46233]), + values=tensor([0.6958, 0.8946, 0.2330, ..., 0.2200, 0.1570, 0.6240]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.7770, 0.7832, 0.7648, ..., 0.3539, 0.1104, 0.8005]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 20.748866319656372 seconds + +[18.17, 18.03, 18.03, 18.1, 17.95, 18.76, 18.0, 18.04, 18.64, 21.07] +[47.52] +24.489142179489136 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.748866319656372, 'TIME_S_1KI': 1.1435032416454325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.7240363693238, 'W': 47.52} +[18.17, 18.03, 18.03, 18.1, 17.95, 18.76, 18.0, 18.04, 18.64, 21.07, 18.03, 17.85, 18.06, 18.05, 18.34, 17.99, 17.97, 18.26, 17.91, 18.02] +327.625 +16.38125 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.748866319656372, 'TIME_S_1KI': 1.1435032416454325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.7240363693238, 'W': 47.52, 'J_1KI': 64.13469475719613, 'W_1KI': 2.6189032791402593, 'W_D': 31.13875, 'J_D': 762.5612760415673, 'W_D_1KI': 1.7161063653899147, 'J_D_1KI': 0.09457736926921546} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json new file mode 100644 index 0000000..f35b37b --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8093, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.65882921218872, "TIME_S_1KI": 2.5526787609278045, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.7929471635819, "W": 48.53, "J_1KI": 148.86852182918346, "W_1KI": 5.996540219943161, "W_D": 32.0905, "J_D": 796.6702672770023, "W_D_1KI": 3.965216854071419, "J_D_1KI": 0.48995636402711223} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output new file mode 100644 index 0000000..e222138 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 2.594694137573242} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 27, 51, ..., 1249948, + 1249974, 1250000]), + col_indices=tensor([ 1900, 3832, 3916, ..., 43370, 44397, 46024]), + values=tensor([0.8523, 0.5318, 0.4293, ..., 0.0706, 0.5129, 0.4581]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.7048, 0.2686, 0.1617, ..., 0.3130, 0.5850, 0.3952]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 2.594694137573242 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', 'synthetic', 'csr', '8093', '-ss', '50000', '-sd', '0.0005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.65882921218872} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 51, ..., 1249949, + 1249978, 1250000]), + col_indices=tensor([ 2007, 2541, 6490, ..., 44052, 45524, 48586]), + values=tensor([0.7205, 0.3330, 0.8983, ..., 0.6824, 0.5041, 0.2342]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.2974, 0.0968, 0.2938, ..., 0.7419, 0.3048, 0.3649]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 20.65882921218872 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 31, 51, ..., 1249949, + 1249978, 1250000]), + col_indices=tensor([ 2007, 2541, 6490, ..., 44052, 45524, 48586]), + values=tensor([0.7205, 0.3330, 0.8983, ..., 0.6824, 0.5041, 0.2342]), + size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr) +tensor([0.2974, 0.0968, 0.2938, ..., 0.7419, 0.3048, 0.3649]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 1250000 +Density: 0.0005 +Time: 20.65882921218872 seconds + +[18.26, 17.93, 19.82, 19.92, 18.14, 18.3, 17.92, 18.17, 18.01, 17.89] +[48.53] +24.825735569000244 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.65882921218872, 'TIME_S_1KI': 2.5526787609278045, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7929471635819, 'W': 48.53} +[18.26, 17.93, 19.82, 19.92, 18.14, 18.3, 17.92, 18.17, 18.01, 17.89, 18.17, 18.47, 18.36, 17.94, 18.08, 17.71, 18.06, 18.03, 17.85, 17.84] +328.79 +16.439500000000002 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.65882921218872, 'TIME_S_1KI': 2.5526787609278045, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7929471635819, 'W': 48.53, 'J_1KI': 148.86852182918346, 'W_1KI': 5.996540219943161, 'W_D': 32.0905, 'J_D': 796.6702672770023, 'W_D_1KI': 3.965216854071419, 'J_D_1KI': 0.48995636402711223} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..b01cbb8 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.652085304260254, "TIME_S_1KI": 5.276465330674567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1237.7624758052825, "W": 48.73, "J_1KI": 316.2397740943491, "W_1KI": 12.450178845171179, "W_D": 32.29675, "J_D": 820.3510207359792, "W_D_1KI": 8.25159683188554, "J_D_1KI": 2.1082260684429075} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..b99ed97 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.365004062652588} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 43, 96, ..., 2499913, + 2499951, 2500000]), + col_indices=tensor([ 152, 193, 2640, ..., 47928, 48233, 48479]), + values=tensor([0.0424, 0.9841, 0.8826, ..., 0.5350, 0.0103, 0.5454]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.5585, 0.2658, 0.0418, ..., 0.1878, 0.1276, 0.9658]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 5.365004062652588 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', 'synthetic', 'csr', '3914', '-ss', '50000', '-sd', '0.001', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.652085304260254} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 43, 92, ..., 2499909, + 2499953, 2500000]), + col_indices=tensor([ 375, 697, 898, ..., 48167, 48194, 49268]), + values=tensor([0.2181, 0.7785, 0.7713, ..., 0.8896, 0.2915, 0.3579]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4723, 0.0239, 0.8312, ..., 0.6749, 0.1846, 0.8368]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 20.652085304260254 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 43, 92, ..., 2499909, + 2499953, 2500000]), + col_indices=tensor([ 375, 697, 898, ..., 48167, 48194, 49268]), + values=tensor([0.2181, 0.7785, 0.7713, ..., 0.8896, 0.2915, 0.3579]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4723, 0.0239, 0.8312, ..., 0.6749, 0.1846, 0.8368]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 20.652085304260254 seconds + +[18.41, 18.63, 18.36, 18.08, 17.92, 18.1, 17.9, 18.13, 17.86, 17.84] +[48.73] +25.40042018890381 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.652085304260254, 'TIME_S_1KI': 5.276465330674567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.7624758052825, 'W': 48.73} +[18.41, 18.63, 18.36, 18.08, 17.92, 18.1, 17.9, 18.13, 17.86, 17.84, 18.24, 18.29, 17.98, 17.83, 18.07, 17.97, 17.88, 21.28, 17.93, 18.42] +328.66499999999996 +16.433249999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.652085304260254, 'TIME_S_1KI': 5.276465330674567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.7624758052825, 'W': 48.73, 'J_1KI': 316.2397740943491, 'W_1KI': 12.450178845171179, 'W_D': 32.29675, 'J_D': 820.3510207359792, 'W_D_1KI': 8.25159683188554, 'J_D_1KI': 2.1082260684429075} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json new file mode 100644 index 0000000..b7790eb --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 35.204474687576294, "TIME_S_1KI": 35.204474687576294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2211.0711840987206, "W": 48.25, "J_1KI": 2211.0711840987206, "W_1KI": 48.25, "W_D": 31.797500000000003, "J_D": 1457.1302793031932, "W_D_1KI": 31.797500000000007, "J_D_1KI": 31.797500000000007} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output new file mode 100644 index 0000000..e6456ba --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.005', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 35.204474687576294} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 258, 483, ..., 12499493, + 12499749, 12500000]), + col_indices=tensor([ 83, 353, 999, ..., 49462, 49644, 49677]), + values=tensor([0.4021, 0.2117, 0.1170, ..., 0.4112, 0.6043, 0.8924]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.5812, 0.1638, 0.8038, ..., 0.6848, 0.2982, 0.3371]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 35.204474687576294 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 258, 483, ..., 12499493, + 12499749, 12500000]), + col_indices=tensor([ 83, 353, 999, ..., 49462, 49644, 49677]), + values=tensor([0.4021, 0.2117, 0.1170, ..., 0.4112, 0.6043, 0.8924]), + size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.5812, 0.1638, 0.8038, ..., 0.6848, 0.2982, 0.3371]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 12500000 +Density: 0.005 +Time: 35.204474687576294 seconds + +[18.49, 20.96, 18.34, 17.83, 18.46, 17.92, 17.89, 18.07, 18.14, 17.83] +[48.25] +45.82530951499939 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 35.204474687576294, 'TIME_S_1KI': 35.204474687576294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2211.0711840987206, 'W': 48.25} +[18.49, 20.96, 18.34, 17.83, 18.46, 17.92, 17.89, 18.07, 18.14, 17.83, 18.4, 17.93, 18.23, 18.54, 18.36, 18.08, 18.03, 17.87, 18.07, 17.94] +329.04999999999995 +16.452499999999997 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 35.204474687576294, 'TIME_S_1KI': 35.204474687576294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2211.0711840987206, 'W': 48.25, 'J_1KI': 2211.0711840987206, 'W_1KI': 48.25, 'W_D': 31.797500000000003, 'J_D': 1457.1302793031932, 'W_D_1KI': 31.797500000000007, 'J_D_1KI': 31.797500000000007} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..17bdd0a --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 71.75394105911255, "TIME_S_1KI": 71.75394105911255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5446.096093916894, "W": 46.45, "J_1KI": 5446.096093916894, "W_1KI": 46.45, "W_D": 30.238500000000002, "J_D": 3545.3557962520126, "W_D_1KI": 30.238500000000002, "J_D_1KI": 30.238500000000002} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..0c0f915 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 71.75394105911255} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 525, 1034, ..., 24998963, + 24999515, 25000000]), + col_indices=tensor([ 177, 318, 326, ..., 49654, 49818, 49958]), + values=tensor([0.8680, 0.9679, 0.4484, ..., 0.6827, 0.9201, 0.6726]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.0409, 0.1065, 0.8971, ..., 0.2398, 0.1614, 0.8383]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 71.75394105911255 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 525, 1034, ..., 24998963, + 24999515, 25000000]), + col_indices=tensor([ 177, 318, 326, ..., 49654, 49818, 49958]), + values=tensor([0.8680, 0.9679, 0.4484, ..., 0.6827, 0.9201, 0.6726]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.0409, 0.1065, 0.8971, ..., 0.2398, 0.1614, 0.8383]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 71.75394105911255 seconds + +[18.42, 17.83, 17.87, 17.83, 18.36, 17.93, 17.78, 17.97, 18.31, 17.74] +[46.45] +117.24641752243042 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 71.75394105911255, 'TIME_S_1KI': 71.75394105911255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5446.096093916894, 'W': 46.45} +[18.42, 17.83, 17.87, 17.83, 18.36, 17.93, 17.78, 17.97, 18.31, 17.74, 18.62, 17.9, 18.04, 17.88, 18.17, 17.99, 17.87, 18.14, 17.94, 18.06] +324.23 +16.2115 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 71.75394105911255, 'TIME_S_1KI': 71.75394105911255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5446.096093916894, 'W': 46.45, 'J_1KI': 5446.096093916894, 'W_1KI': 46.45, 'W_D': 30.238500000000002, 'J_D': 3545.3557962520126, 'W_D_1KI': 30.238500000000002, 'J_D_1KI': 30.238500000000002} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..cd77447 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 42431, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.11350655555725, "TIME_S_1KI": 0.47402857711478047, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1109.2391641235351, "W": 46.64, "J_1KI": 26.142187648736424, "W_1KI": 1.0991963422969055, "W_D": 30.3375, "J_D": 721.5167912006378, "W_D_1KI": 0.7149843274964058, "J_D_1KI": 0.01685051795848332} diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..0874c10 --- /dev/null +++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.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', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.49491095542907715} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24999, 24999, 25000]), + col_indices=tensor([48293, 21867, 31172, ..., 8085, 31082, 49903]), + values=tensor([0.7536, 0.0496, 0.9146, ..., 0.3335, 0.2529, 0.5168]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.5953, 0.1156, 0.8276, ..., 0.3405, 0.8051, 0.4714]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.49491095542907715 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', 'synthetic', 'csr', '42431', '-ss', '50000', '-sd', '1e-05', '-c', '1'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.11350655555725} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([ 5529, 8530, 10143, ..., 49628, 3004, 27732]), + values=tensor([0.0410, 0.4304, 0.2964, ..., 0.9705, 0.5689, 0.1235]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.9110, 0.1233, 0.5259, ..., 0.7671, 0.0751, 0.3217]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 20.11350655555725 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([ 5529, 8530, 10143, ..., 49628, 3004, 27732]), + values=tensor([0.0410, 0.4304, 0.2964, ..., 0.9705, 0.5689, 0.1235]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.9110, 0.1233, 0.5259, ..., 0.7671, 0.0751, 0.3217]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 20.11350655555725 seconds + +[18.38, 18.06, 17.97, 17.82, 18.09, 18.18, 18.18, 18.07, 18.17, 18.01] +[46.64] +23.783000946044922 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 42431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.11350655555725, 'TIME_S_1KI': 0.47402857711478047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.2391641235351, 'W': 46.64} +[18.38, 18.06, 17.97, 17.82, 18.09, 18.18, 18.18, 18.07, 18.17, 18.01, 18.57, 18.17, 17.88, 17.88, 17.9, 17.94, 18.68, 18.4, 18.2, 17.96] +326.05 +16.302500000000002 +{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 42431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.11350655555725, 'TIME_S_1KI': 0.47402857711478047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.2391641235351, 'W': 46.64, 'J_1KI': 26.142187648736424, 'W_1KI': 1.0991963422969055, 'W_D': 30.3375, 'J_D': 721.5167912006378, 'W_D_1KI': 0.7149843274964058, 'J_D_1KI': 0.01685051795848332} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..5cee3aa --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.577640295028687, "TIME_S_1KI": 22.577640295028687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.5292521381377, "W": 64.81606725703797, "J_1KI": 1196.5292521381377, "W_1KI": 64.81606725703797, "W_D": 45.67406725703797, "J_D": 843.1606521334647, "W_D_1KI": 45.67406725703797, "J_D_1KI": 45.67406725703797} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..047a391 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.577640295028687} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 26, ..., 999978, + 999989, 1000000]), + col_indices=tensor([16134, 16354, 24327, ..., 64689, 79970, 99510]), + values=tensor([0.0032, 0.4253, 0.4412, ..., 0.5357, 0.1333, 0.2349]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.8413, 0.1731, 0.9001, ..., 0.4021, 0.4850, 0.1983]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 22.577640295028687 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 26, ..., 999978, + 999989, 1000000]), + col_indices=tensor([16134, 16354, 24327, ..., 64689, 79970, 99510]), + values=tensor([0.0032, 0.4253, 0.4412, ..., 0.5357, 0.1333, 0.2349]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.8413, 0.1731, 0.9001, ..., 0.4021, 0.4850, 0.1983]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 22.577640295028687 seconds + +[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36] +[21.12, 21.24, 21.32, 22.2, 24.84, 39.96, 57.72, 73.16, 89.8, 95.92, 95.4, 95.4, 93.32, 91.16, 90.92, 93.4, 92.2, 91.64] +18.46038031578064 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797} +[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36, 21.08, 20.96, 20.96, 21.0, 21.28, 21.08, 21.28, 21.12, 21.12, 20.88] +382.84000000000003 +19.142000000000003 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797, 'J_1KI': 1196.5292521381377, 'W_1KI': 64.81606725703797, 'W_D': 45.67406725703797, 'J_D': 843.1606521334647, 'W_D_1KI': 45.67406725703797, 'J_D_1KI': 45.67406725703797} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..15d76ee --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5444, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 15.66837453842163, "TIME_S_1KI": 2.878099658049528, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.8989781379698, "W": 61.815457006610345, "J_1KI": 222.24448533026631, "W_1KI": 11.354786371530189, "W_D": 42.416457006610344, "J_D": 830.2070464842318, "W_D_1KI": 7.791413851324457, "J_D_1KI": 1.4311928455776004} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..529e04c --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.735213041305542} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 99998, 100000, + 100000]), + col_indices=tensor([47108, 85356, 39968, ..., 81528, 26483, 51109]), + values=tensor([0.3148, 0.6992, 0.6314, ..., 0.5894, 0.0851, 0.0670]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4890, 0.3896, 0.3852, ..., 0.6786, 0.1828, 0.3984]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 2.735213041305542 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3838 -ss 100000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.402097463607788} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 99999, 99999, + 100000]), + col_indices=tensor([ 1694, 16648, 92396, ..., 98787, 30932, 62089]), + values=tensor([0.4689, 0.5529, 0.8985, ..., 0.1212, 0.7499, 0.9985]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8040, 0.7540, 0.7072, ..., 0.4394, 0.3265, 0.7941]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 7.402097463607788 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5444 -ss 100000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 15.66837453842163} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 99997, 100000, + 100000]), + col_indices=tensor([ 8956, 7966, 63353, ..., 28673, 30724, 93829]), + values=tensor([0.9652, 0.8395, 0.8363, ..., 0.6704, 0.2134, 0.9962]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1215, 0.6198, 0.6986, ..., 0.9502, 0.5989, 0.9473]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 15.66837453842163 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 99997, 100000, + 100000]), + col_indices=tensor([ 8956, 7966, 63353, ..., 28673, 30724, 93829]), + values=tensor([0.9652, 0.8395, 0.8363, ..., 0.6704, 0.2134, 0.9962]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1215, 0.6198, 0.6986, ..., 0.9502, 0.5989, 0.9473]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 15.66837453842163 seconds + +[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76] +[21.64, 21.56, 21.96, 22.76, 26.44, 43.68, 43.68, 58.28, 74.48, 87.48, 92.36, 91.24, 90.2, 88.56, 87.2, 86.36, 85.72, 85.72, 85.2] +19.572757959365845 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345} +[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76, 21.4, 21.32, 21.48, 21.48, 21.32, 21.52, 21.24, 21.16, 21.2, 21.28] +387.98 +19.399 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345, 'J_1KI': 222.24448533026631, 'W_1KI': 11.354786371530189, 'W_D': 42.416457006610344, 'J_D': 830.2070464842318, 'W_D_1KI': 7.791413851324457, 'J_D_1KI': 1.4311928455776004} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..b31b4ad --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 31990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.162655591964722, "TIME_S_1KI": 0.3176822629560713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 281.0764434432984, "W": 21.267196136821532, "J_1KI": 8.786384602791447, "W_1KI": 0.6648076316605669, "W_D": 2.733196136821533, "J_D": 36.12309984016423, "W_D_1KI": 0.08543907898785662, "J_D_1KI": 0.0026708058451971432} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..3e5c97c --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,62 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.32822322845458984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 4, ..., 9997, 9998, 10000]), + col_indices=tensor([2721, 4826, 6729, ..., 6567, 802, 8084]), + values=tensor([0.9788, 0.8960, 0.9515, ..., 0.3823, 0.9672, 0.4403]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.1442, 0.5021, 0.5745, ..., 0.9716, 0.6255, 0.3521]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.32822322845458984 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 31990 -ss 10000 -sd 0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.162655591964722} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 9997, 10000, 10000]), + col_indices=tensor([1219, 6055, 1582, ..., 3506, 4664, 5684]), + values=tensor([0.3475, 0.3226, 0.1217, ..., 0.8742, 0.3097, 0.9052]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3839, 0.3550, 0.5972, ..., 0.2550, 0.5835, 0.6125]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.162655591964722 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 2, ..., 9997, 10000, 10000]), + col_indices=tensor([1219, 6055, 1582, ..., 3506, 4664, 5684]), + values=tensor([0.3475, 0.3226, 0.1217, ..., 0.8742, 0.3097, 0.9052]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3839, 0.3550, 0.5972, ..., 0.2550, 0.5835, 0.6125]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.162655591964722 seconds + +[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48] +[20.68, 20.88, 21.64, 22.32, 23.4, 23.4, 23.8, 24.24, 23.6, 23.6, 23.44, 23.48, 23.64] +13.216431617736816 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532} +[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48, 20.56, 20.64, 20.48, 20.52, 20.48, 20.72, 20.72, 20.8, 20.92, 21.32] +370.67999999999995 +18.534 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532, 'J_1KI': 8.786384602791447, 'W_1KI': 0.6648076316605669, 'W_D': 2.733196136821533, 'J_D': 36.12309984016423, 'W_D_1KI': 0.08543907898785662, 'J_D_1KI': 0.0026708058451971432} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..44e0916 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4642, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.39481520652771, "TIME_S_1KI": 2.2392966838706827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.1584589004516, "W": 23.611594488388015, "J_1KI": 72.20130523490987, "W_1KI": 5.086513246098236, "W_D": 5.040594488388017, "J_D": 71.54950427007671, "W_D_1KI": 1.0858669729401156, "J_D_1KI": 0.23392222596728038} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..12aea87 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2614803314208984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 19, ..., 99975, 99988, + 100000]), + col_indices=tensor([ 662, 710, 3445, ..., 9576, 9602, 9965]), + values=tensor([0.0517, 0.2381, 0.9401, ..., 0.3987, 0.7682, 0.4070]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1766, 0.1636, 0.7477, ..., 0.1192, 0.5625, 0.2605]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 2.2614803314208984 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4642 -ss 10000 -sd 0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.39481520652771} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 19, ..., 99983, 99997, + 100000]), + col_indices=tensor([ 82, 3146, 3840, ..., 8041, 8695, 8893]), + values=tensor([0.8450, 0.6541, 0.7727, ..., 0.8034, 0.8111, 0.1952]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0486, 0.3621, 0.6684, ..., 0.7127, 0.4964, 0.1751]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.39481520652771 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 19, ..., 99983, 99997, + 100000]), + col_indices=tensor([ 82, 3146, 3840, ..., 8041, 8695, 8893]), + values=tensor([0.8450, 0.6541, 0.7727, ..., 0.8034, 0.8111, 0.1952]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0486, 0.3621, 0.6684, ..., 0.7127, 0.4964, 0.1751]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.39481520652771 seconds + +[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92] +[20.16, 20.2, 20.48, 21.88, 25.12, 28.64, 29.48, 29.64, 29.64, 29.56, 24.6, 24.56, 24.44, 24.24] +14.194655895233154 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015} +[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92, 20.36, 20.52, 20.6, 20.84, 20.8, 20.56, 20.64, 20.64, 20.64, 20.52] +371.41999999999996 +18.570999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015, 'J_1KI': 72.20130523490987, 'W_1KI': 5.086513246098236, 'W_D': 5.040594488388017, 'J_D': 71.54950427007671, 'W_D_1KI': 1.0858669729401156, 'J_D_1KI': 0.23392222596728038} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..6da6654 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.402220964431763, "TIME_S_1KI": 21.402220964431763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 598.721107492447, "W": 23.65441632405048, "J_1KI": 598.721107492447, "W_1KI": 23.65441632405048, "W_D": 5.107416324050483, "J_D": 129.27471623349203, "W_D_1KI": 5.107416324050483, "J_D_1KI": 5.107416324050483} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..5b10379 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.402220964431763} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 109, 232, ..., 999786, + 999885, 1000000]), + col_indices=tensor([ 48, 108, 238, ..., 9836, 9911, 9942]), + values=tensor([0.7065, 0.8335, 0.4165, ..., 0.0617, 0.0653, 0.1993]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6170, 0.2022, 0.1812, ..., 0.2173, 0.9754, 0.3705]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.402220964431763 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 109, 232, ..., 999786, + 999885, 1000000]), + col_indices=tensor([ 48, 108, 238, ..., 9836, 9911, 9942]), + values=tensor([0.7065, 0.8335, 0.4165, ..., 0.0617, 0.0653, 0.1993]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.6170, 0.2022, 0.1812, ..., 0.2173, 0.9754, 0.3705]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 21.402220964431763 seconds + +[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8] +[20.64, 20.52, 20.44, 21.52, 22.84, 29.04, 29.96, 30.44, 29.96, 27.0, 24.4, 24.4, 24.24, 24.08, 24.0, 24.16, 24.04, 24.2, 24.28, 24.16, 24.12, 23.92, 23.72, 23.72, 23.72] +25.311176538467407 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048} +[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8, 20.76, 20.56, 20.4, 20.28, 20.24, 20.28, 20.36, 20.6, 20.6, 20.64] +370.93999999999994 +18.546999999999997 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048, 'J_1KI': 598.721107492447, 'W_1KI': 23.65441632405048, 'W_D': 5.107416324050483, 'J_D': 129.27471623349203, 'W_D_1KI': 5.107416324050483, 'J_D_1KI': 5.107416324050483} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..23a9329 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 108.30107378959656, "TIME_S_1KI": 108.30107378959656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2693.844181451797, "W": 24.185192869557547, "J_1KI": 2693.844181451797, "W_1KI": 24.185192869557547, "W_D": 5.823192869557545, "J_D": 648.6106732220641, "W_D_1KI": 5.823192869557545, "J_D_1KI": 5.823192869557545} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..f20f6e0 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,45 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 108.30107378959656} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 484, 1003, ..., 4999033, + 4999518, 5000000]), + col_indices=tensor([ 10, 43, 51, ..., 9955, 9982, 9992]), + values=tensor([0.0167, 0.2062, 0.3972, ..., 0.2194, 0.0680, 0.6916]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.1565, 0.8667, 0.6742, ..., 0.1248, 0.3395, 0.1639]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 108.30107378959656 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 484, 1003, ..., 4999033, + 4999518, 5000000]), + col_indices=tensor([ 10, 43, 51, ..., 9955, 9982, 9992]), + values=tensor([0.0167, 0.2062, 0.3972, ..., 0.2194, 0.0680, 0.6916]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.1565, 0.8667, 0.6742, ..., 0.1248, 0.3395, 0.1639]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 108.30107378959656 seconds + +[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2] +[20.12, 20.52, 21.16, 22.2, 23.84, 29.72, 33.04, 33.04, 32.72, 32.68, 29.16, 24.72, 24.68, 24.72, 24.52, 24.44, 24.24, 24.24, 24.12, 24.08, 24.08, 24.28, 24.12, 24.28, 24.44, 24.44, 24.64, 24.6, 24.64, 24.52, 24.56, 24.4, 24.4, 24.28, 24.48, 24.36, 24.44, 24.48, 24.36, 24.24, 24.28, 24.12, 24.28, 24.32, 24.32, 24.28, 24.28, 24.4, 24.44, 24.36, 24.16, 24.0, 23.88, 23.92, 24.0, 24.08, 24.2, 24.2, 24.24, 24.16, 24.28, 24.36, 24.24, 24.36, 24.44, 24.48, 24.76, 24.56, 24.4, 24.4, 24.32, 24.28, 24.12, 24.4, 24.8, 24.76, 24.84, 24.84, 24.4, 24.4, 24.4, 24.2, 24.2, 24.16, 24.16, 23.84, 23.92, 24.12, 24.52, 24.52, 24.68, 24.52, 24.48, 24.24, 24.24, 24.28, 24.28, 24.52, 24.64, 24.48, 24.32, 24.16, 24.16, 24.0, 23.96, 23.96, 24.08, 24.08, 23.96, 24.28] +111.38402724266052 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547} +[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2, 20.64, 20.72, 20.68, 20.72, 20.36, 20.32, 20.24, 20.16, 20.24, 20.56] +367.24 +18.362000000000002 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547, 'J_1KI': 2693.844181451797, 'W_1KI': 24.185192869557547, 'W_D': 5.823192869557545, 'J_D': 648.6106732220641, 'W_D_1KI': 5.823192869557545, 'J_D_1KI': 5.823192869557545} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..c8342c5 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 141816, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.164389848709106, "TIME_S_1KI": 0.07167308236524163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 285.17019953727726, "W": 21.625492932171987, "J_1KI": 2.010846445656888, "W_1KI": 0.1524897961596152, "W_D": 3.365492932171989, "J_D": 44.37994981288918, "W_D_1KI": 0.023731405004879483, "J_D_1KI": 0.00016733940461499043} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..c5a4de0 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1521 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07915711402893066} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([8887, 6657, 7565, 3220, 7011, 960, 7687, 1638, 5195, + 5216, 8504, 887, 1870, 7762, 4871, 1881, 1312, 2356, + 8882, 564, 3323, 3943, 7528, 9194, 2802, 9093, 6109, + 8556, 927, 2210, 2106, 820, 4388, 6120, 3013, 4186, + 9725, 4312, 7062, 7727, 7858, 3639, 0, 955, 9212, + 3900, 2519, 3782, 2814, 6711, 4282, 9829, 2935, 5472, + 5069, 5474, 6384, 2189, 3553, 9092, 4939, 7190, 5600, + 4241, 9909, 3829, 8005, 1584, 4693, 2762, 7432, 5677, + 4550, 3593, 1945, 2933, 5983, 5180, 6269, 6691, 1646, + 3773, 6546, 3306, 693, 4467, 6900, 7830, 1109, 4818, + 9859, 245, 7505, 9264, 3708, 4499, 1575, 3766, 2431, + 3105, 5276, 7713, 8061, 1468, 9875, 2972, 4010, 5060, + 5944, 2540, 6479, 3011, 9049, 9192, 3917, 4370, 9436, + 2170, 5413, 6341, 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0.7346, 0.8053, 0.7353]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.07915711402893066 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 132647 -ss 10000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.821086883544922} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([9896, 2294, 608, 7455, 4786, 9947, 6306, 3161, 2752, + 3769, 8365, 7822, 2650, 3972, 4525, 3555, 390, 931, + 2637, 9922, 8440, 7065, 7479, 7024, 5903, 1510, 7327, + 4589, 3801, 959, 4616, 1851, 8424, 5751, 466, 7240, + 2164, 4942, 3099, 6866, 3692, 3245, 1569, 1786, 834, + 2878, 8450, 562, 8579, 8350, 4382, 4571, 2230, 1625, + 3124, 6145, 6696, 7788, 711, 1615, 7369, 3625, 7867, + 7330, 6146, 1902, 296, 9427, 4612, 950, 3058, 3653, + 2098, 9957, 1836, 5903, 9459, 1827, 2742, 6093, 8427, + 2467, 2948, 3117, 9056, 5451, 1784, 336, 1205, 5825, + 9644, 9509, 9139, 6860, 3591, 7747, 1333, 6979, 3236, + 3937, 6062, 4432, 6485, 5241, 6733, 3552, 6786, 9248, + 6777, 6449, 1983, 8864, 9572, 9119, 62, 8989, 7326, + 738, 3062, 1891, 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0.3444, 0.7600, ..., 0.8482, 0.3104, 0.4836]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 9.821086883544922 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141816 -ss 10000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.164389848709106} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([1810, 1856, 931, 2279, 4513, 5670, 4299, 1451, 4735, + 6521, 8634, 5493, 2604, 416, 4540, 2978, 6685, 9818, + 316, 767, 3433, 9310, 5118, 5536, 8136, 256, 5007, + 9151, 5614, 7335, 5950, 7216, 5695, 8824, 5574, 8028, + 9895, 2763, 3721, 6353, 4375, 393, 4695, 4114, 2940, + 9233, 9506, 5002, 9687, 9138, 8360, 7455, 1902, 6476, + 6018, 9078, 1607, 8332, 6637, 1057, 721, 3190, 9337, + 8872, 2095, 3714, 9220, 3100, 1647, 1733, 5119, 557, + 9473, 477, 8030, 5805, 9318, 3400, 7191, 1837, 2389, + 3821, 5362, 913, 1742, 7815, 3737, 6731, 1087, 8363, + 833, 8039, 7694, 5811, 4452, 6537, 8096, 9721, 7906, + 9466, 2451, 2361, 1224, 6931, 8635, 7881, 1988, 582, + 2422, 9410, 3064, 764, 9933, 6316, 5596, 8997, 8781, + 7963, 2462, 5618, 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0.4421, ..., 0.7861, 0.0630, 0.0040]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.164389848709106 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([1810, 1856, 931, 2279, 4513, 5670, 4299, 1451, 4735, + 6521, 8634, 5493, 2604, 416, 4540, 2978, 6685, 9818, + 316, 767, 3433, 9310, 5118, 5536, 8136, 256, 5007, + 9151, 5614, 7335, 5950, 7216, 5695, 8824, 5574, 8028, + 9895, 2763, 3721, 6353, 4375, 393, 4695, 4114, 2940, + 9233, 9506, 5002, 9687, 9138, 8360, 7455, 1902, 6476, + 6018, 9078, 1607, 8332, 6637, 1057, 721, 3190, 9337, + 8872, 2095, 3714, 9220, 3100, 1647, 1733, 5119, 557, + 9473, 477, 8030, 5805, 9318, 3400, 7191, 1837, 2389, + 3821, 5362, 913, 1742, 7815, 3737, 6731, 1087, 8363, + 833, 8039, 7694, 5811, 4452, 6537, 8096, 9721, 7906, + 9466, 2451, 2361, 1224, 6931, 8635, 7881, 1988, 582, + 2422, 9410, 3064, 764, 9933, 6316, 5596, 8997, 8781, + 7963, 2462, 5618, 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0.4421, ..., 0.7861, 0.0630, 0.0040]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.164389848709106 seconds + +[20.48, 20.52, 20.52, 20.52, 20.2, 20.28, 20.28, 20.24, 20.08, 20.24] +[20.36, 20.52, 21.96, 22.92, 24.08, 24.08, 24.4, 25.08, 24.4, 23.76, 24.0, 23.72, 23.4] +13.186760663986206 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141816, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.164389848709106, 'TIME_S_1KI': 0.07167308236524163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 285.17019953727726, 'W': 21.625492932171987} +[20.48, 20.52, 20.52, 20.52, 20.2, 20.28, 20.28, 20.24, 20.08, 20.24, 20.16, 20.28, 20.28, 20.28, 20.12, 20.12, 20.0, 20.12, 20.52, 20.8] +365.2 +18.259999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141816, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.164389848709106, 'TIME_S_1KI': 0.07167308236524163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 285.17019953727726, 'W': 21.625492932171987, 'J_1KI': 2.010846445656888, 'W_1KI': 0.1524897961596152, 'W_D': 3.365492932171989, 'J_D': 44.37994981288918, 'W_D_1KI': 0.023731405004879483, 'J_D_1KI': 0.00016733940461499043} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..cb5e414 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 39.37885141372681, "TIME_S_1KI": 39.37885141372681, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3668.398367080688, "W": 77.07751960335096, "J_1KI": 3668.398367080688, "W_1KI": 77.07751960335096, "W_D": 57.18851960335097, "J_D": 2721.8088102509973, "W_D_1KI": 57.18851960335097, "J_D_1KI": 57.18851960335097} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..56c2844 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,47 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 39.37885141372681} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 10, ..., 2499992, + 2499996, 2500000]), + col_indices=tensor([ 4222, 120413, 177881, ..., 234997, 318812, + 370543]), + values=tensor([0.6429, 0.8175, 0.9231, ..., 0.8720, 0.9829, 0.6195]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7836, 0.9661, 0.9943, ..., 0.1995, 0.6325, 0.8613]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 39.37885141372681 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 7, 10, ..., 2499992, + 2499996, 2500000]), + col_indices=tensor([ 4222, 120413, 177881, ..., 234997, 318812, + 370543]), + values=tensor([0.6429, 0.8175, 0.9231, ..., 0.8720, 0.9829, 0.6195]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7836, 0.9661, 0.9943, ..., 0.1995, 0.6325, 0.8613]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 39.37885141372681 seconds + +[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0] +[22.12, 21.92, 21.96, 23.24, 24.64, 30.88, 42.28, 55.2, 68.48, 80.52, 87.0, 88.04, 90.88, 91.16, 92.4, 93.96, 93.96, 94.68, 93.52, 94.04, 94.08, 94.16, 93.04, 90.92, 89.84, 90.08, 89.76, 89.56, 91.4, 89.32, 89.44, 89.84, 89.84, 90.68, 89.76, 89.96, 89.52, 87.76, 87.84, 90.36, 91.92, 93.44, 92.0, 91.96, 92.36, 91.2] +47.59362244606018 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096} +[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0, 23.68, 23.6, 22.52, 21.8, 21.8, 21.84, 22.16, 22.32, 22.24, 22.24] +397.78 +19.889 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096, 'J_1KI': 3668.398367080688, 'W_1KI': 77.07751960335096, 'W_D': 57.18851960335097, 'J_D': 2721.8088102509973, 'W_D_1KI': 57.18851960335097, 'J_D_1KI': 57.18851960335097} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..47ee4ee --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1525, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375513792037964, "TIME_S_1KI": 6.80361560133637, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1039.7556144714354, "W": 59.41096095809724, "J_1KI": 681.8069603091379, "W_1KI": 38.958007185637534, "W_D": 40.714960958097244, "J_D": 712.5555380096434, "W_D_1KI": 26.698335054489995, "J_D_1KI": 17.507104953763932} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..b2aa093 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.884527921676636} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 12, ..., 249988, 249997, + 250000]), + col_indices=tensor([ 1848, 28763, 31705, ..., 4981, 22506, 45960]), + values=tensor([0.8493, 0.0534, 0.5342, ..., 0.4299, 0.9704, 0.1142]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.1630, 0.3141, 0.8980, ..., 0.6818, 0.2617, 0.8646]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 6.884527921676636 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1525 -ss 50000 -sd 0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375513792037964} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 12, ..., 249992, 249994, + 250000]), + col_indices=tensor([ 3205, 25770, 28303, ..., 16579, 33459, 36956]), + values=tensor([0.6871, 0.0301, 0.1880, ..., 0.0850, 0.6966, 0.8839]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.3970, 0.9447, 0.7491, ..., 0.5145, 0.9554, 0.9707]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.375513792037964 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 12, ..., 249992, 249994, + 250000]), + col_indices=tensor([ 3205, 25770, 28303, ..., 16579, 33459, 36956]), + values=tensor([0.6871, 0.0301, 0.1880, ..., 0.0850, 0.6966, 0.8839]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.3970, 0.9447, 0.7491, ..., 0.5145, 0.9554, 0.9707]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.375513792037964 seconds + +[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84] +[21.0, 20.96, 21.88, 23.12, 23.12, 32.08, 50.16, 64.0, 76.16, 91.92, 90.6, 89.16, 89.36, 89.52, 88.8, 89.28, 89.04] +17.501073837280273 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724} +[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84, 20.92, 20.84, 20.84, 20.72, 21.0, 21.0, 21.28, 21.24, 20.92, 20.6] +373.91999999999996 +18.695999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724, 'J_1KI': 681.8069603091379, 'W_1KI': 38.958007185637534, 'W_D': 40.714960958097244, 'J_D': 712.5555380096434, 'W_D_1KI': 26.698335054489995, 'J_D_1KI': 17.507104953763932} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..c30df0a --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 60.445369720458984, "TIME_S_1KI": 60.445369720458984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5178.136594352722, "W": 75.0095899661512, "J_1KI": 5178.136594352722, "W_1KI": 75.0095899661512, "W_D": 55.72458996615119, "J_D": 3846.835299849509, "W_D_1KI": 55.72458996615119, "J_D_1KI": 55.72458996615119} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..1aeb456 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,45 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 60.445369720458984} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 42, 88, ..., 2499911, + 2499959, 2500000]), + col_indices=tensor([ 784, 2104, 3070, ..., 44692, 45478, 45799]), + values=tensor([0.0569, 0.3731, 0.2156, ..., 0.1856, 0.5823, 0.7517]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7969, 0.4843, 0.4078, ..., 0.5644, 0.6126, 0.7864]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 60.445369720458984 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 42, 88, ..., 2499911, + 2499959, 2500000]), + col_indices=tensor([ 784, 2104, 3070, ..., 44692, 45478, 45799]), + values=tensor([0.0569, 0.3731, 0.2156, ..., 0.1856, 0.5823, 0.7517]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.7969, 0.4843, 0.4078, ..., 0.5644, 0.6126, 0.7864]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 60.445369720458984 seconds + +[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04] +[22.08, 22.0, 22.16, 23.24, 24.16, 35.0, 49.88, 62.44, 76.6, 76.6, 84.16, 86.2, 86.12, 85.64, 84.84, 83.12, 82.96, 81.88, 81.44, 81.36, 81.2, 82.36, 83.24, 83.28, 84.08, 84.08, 84.48, 84.24, 83.64, 83.96, 83.6, 83.68, 83.56, 84.48, 83.84, 84.08, 84.36, 84.48, 84.04, 84.4, 85.08, 85.08, 84.88, 84.76, 84.72, 84.08, 83.2, 83.08, 83.4, 83.56, 83.6, 83.56, 83.72, 83.4, 84.96, 85.84, 86.72, 86.72, 87.6, 88.08, 87.2, 87.32, 87.24, 86.88, 86.4, 85.96, 84.76] +69.0329942703247 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512} +[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04, 21.4, 21.44, 21.4, 21.2, 21.2, 21.2, 21.2, 21.04, 20.96, 21.24] +385.70000000000005 +19.285000000000004 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512, 'J_1KI': 5178.136594352722, 'W_1KI': 75.0095899661512, 'W_D': 55.72458996615119, 'J_D': 3846.835299849509, 'W_D_1KI': 55.72458996615119, 'J_D_1KI': 55.72458996615119} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..0a48909 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8439, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.30658531188965, "TIME_S_1KI": 1.9322888152493953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 993.3751641559601, "W": 60.56268692312065, "J_1KI": 117.71242613531936, "W_1KI": 7.176524105121538, "W_D": 41.768686923120654, "J_D": 685.1079160590172, "W_D_1KI": 4.949482986505588, "J_D_1KI": 0.5865011241267435} diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..ee322a5 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.2654190063476562} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24998, 25000, 25000]), + col_indices=tensor([ 6514, 22496, 11789, ..., 40007, 5149, 28458]), + values=tensor([0.4327, 0.6473, 0.1491, ..., 0.8954, 0.9190, 0.6593]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4414, 0.3955, 0.1417, ..., 0.3292, 0.0955, 0.0474]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 3.2654190063476562 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3215 -ss 50000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.000005722045898} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([27536, 25934, 37963, ..., 3997, 32688, 28318]), + values=tensor([0.1759, 0.2893, 0.0177, ..., 0.2344, 0.0283, 0.5475]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4720, 0.7633, 0.9347, ..., 0.8863, 0.6224, 0.2346]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 4.000005722045898 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8439 -ss 50000 -sd 1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.30658531188965} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 24999, 24999, 25000]), + col_indices=tensor([22959, 5139, 40799, ..., 46493, 8579, 7673]), + values=tensor([0.4149, 0.3641, 0.9895, ..., 0.4042, 0.1062, 0.3479]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8723, 0.6408, 0.2457, ..., 0.3733, 0.2625, 0.6379]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 16.30658531188965 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 24999, 24999, 25000]), + col_indices=tensor([22959, 5139, 40799, ..., 46493, 8579, 7673]), + values=tensor([0.4149, 0.3641, 0.9895, ..., 0.4042, 0.1062, 0.3479]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8723, 0.6408, 0.2457, ..., 0.3733, 0.2625, 0.6379]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 16.30658531188965 seconds + +[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92] +[21.0, 20.72, 20.88, 25.2, 27.52, 41.48, 58.76, 70.32, 84.16, 91.04, 91.68, 91.84, 91.84, 92.08, 92.44, 91.88] +16.40242886543274 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065} +[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92, 20.8, 20.96, 20.92, 20.92, 20.76, 20.88, 20.8, 20.92, 20.76, 20.56] +375.88 +18.794 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065, 'J_1KI': 117.71242613531936, 'W_1KI': 7.176524105121538, 'W_D': 41.768686923120654, 'J_D': 685.1079160590172, 'W_D_1KI': 4.949482986505588, 'J_D_1KI': 0.5865011241267435} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..87aabb6 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 66395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.71402621269226, "TIME_S_1KI": 0.16136796765859268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1809.3928852605818, "W": 143.23, "J_1KI": 27.251944954598716, "W_1KI": 2.15724075608103, "W_D": 107.452, "J_D": 1357.417330915451, "W_D_1KI": 1.6183748776263271, "J_D_1KI": 0.02437495109008701} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..bc4163e --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.2295377254486084} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 9, 19, ..., 999980, + 999992, 1000000]), + col_indices=tensor([ 2595, 16687, 29551, ..., 82666, 84305, 92330]), + values=tensor([0.2399, 0.6496, 0.1067, ..., 0.4780, 0.9034, 0.0304]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9784, 0.5709, 0.3671, ..., 0.6067, 0.7821, 0.8363]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 0.2295377254486084 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45744', '-ss', '100000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.2341063022613525} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 10, 18, ..., 999977, + 999994, 1000000]), + col_indices=tensor([ 464, 33291, 41816, ..., 39255, 78479, 83666]), + values=tensor([0.4695, 0.4859, 0.9230, ..., 0.6746, 0.1683, 0.8174]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.0937, 0.3379, 0.3499, ..., 0.6520, 0.3862, 0.7030]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 7.2341063022613525 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66395', '-ss', '100000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.71402621269226} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 21, ..., 999982, + 999990, 1000000]), + col_indices=tensor([ 7090, 12502, 14648, ..., 47720, 74306, 81506]), + values=tensor([0.0325, 0.8127, 0.1017, ..., 0.2993, 0.6676, 0.4101]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9849, 0.0117, 0.6257, ..., 0.6699, 0.0244, 0.0988]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.71402621269226 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 21, ..., 999982, + 999990, 1000000]), + col_indices=tensor([ 7090, 12502, 14648, ..., 47720, 74306, 81506]), + values=tensor([0.0325, 0.8127, 0.1017, ..., 0.2993, 0.6676, 0.4101]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9849, 0.0117, 0.6257, ..., 0.6699, 0.0244, 0.0988]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.71402621269226 seconds + +[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03] +[143.23] +12.632778644561768 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23} +[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03, 39.88, 40.13, 39.21, 40.07, 39.16, 39.33, 39.33, 40.09, 39.55, 40.29] +715.56 +35.778 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23, 'J_1KI': 27.251944954598716, 'W_1KI': 2.15724075608103, 'W_D': 107.452, 'J_D': 1357.417330915451, 'W_D_1KI': 1.6183748776263271, 'J_D_1KI': 0.02437495109008701} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..9f14449 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.207890748977661, "TIME_S_1KI": 0.10889376486740501, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1545.566165971756, "W": 113.81, "J_1KI": 15.016431051462288, "W_1KI": 1.1057566188972554, "W_D": 78.15, "J_D": 1061.2951047420502, "W_D_1KI": 0.7592907456886082, "J_D_1KI": 0.007377126506568941} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..73316f3 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1461803913116455} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 99996, 99998, + 100000]), + col_indices=tensor([53462, 64739, 8211, ..., 77032, 12066, 66338]), + values=tensor([0.7526, 0.8412, 0.0484, ..., 0.1652, 0.9362, 0.7970]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.2578, 0.3705, 0.8367, ..., 0.6623, 0.7950, 0.3656]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.1461803913116455 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71829', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.327654123306274} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 99999, 99999, + 100000]), + col_indices=tensor([53445, 61427, 55256, ..., 99710, 79743, 76910]), + values=tensor([0.2043, 0.7921, 0.3637, ..., 0.3183, 0.9272, 0.3273]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6989, 0.9157, 0.2952, ..., 0.1186, 0.5845, 0.8882]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 7.327654123306274 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102925', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.207890748977661} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 100000, 100000, + 100000]), + col_indices=tensor([13249, 39443, 49972, ..., 18781, 78628, 93775]), + values=tensor([0.7488, 0.1329, 0.0380, ..., 0.8918, 0.6119, 0.7720]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.5554, 0.6245, 0.8914, ..., 0.6605, 0.7651, 0.7091]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 11.207890748977661 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 100000, 100000, + 100000]), + col_indices=tensor([13249, 39443, 49972, ..., 18781, 78628, 93775]), + values=tensor([0.7488, 0.1329, 0.0380, ..., 0.8918, 0.6119, 0.7720]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.5554, 0.6245, 0.8914, ..., 0.6605, 0.7651, 0.7091]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 11.207890748977661 seconds + +[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14] +[113.81] +13.580231666564941 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81} +[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14, 41.44, 39.12, 40.01, 39.13, 39.64, 39.12, 40.27, 39.05, 39.93, 38.94] +713.2 +35.660000000000004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81, 'J_1KI': 15.016431051462288, 'W_1KI': 1.1057566188972554, 'W_D': 78.15, 'J_D': 1061.2951047420502, 'W_D_1KI': 0.7592907456886082, 'J_D_1KI': 0.007377126506568941} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..5c9115a --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289350, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.562561988830566, "TIME_S_1KI": 0.036504447861864756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1275.0872325897217, "W": 96.48, "J_1KI": 4.406729678900023, "W_1KI": 0.3334370139968896, "W_D": 61.3225, "J_D": 810.4429604113102, "W_D_1KI": 0.21193191636426473, "J_D_1KI": 0.0007324413905797986} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..1a5d3e8 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.052317142486572266} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 4, ..., 9999, 10000, 10000]), + col_indices=tensor([1023, 5622, 6334, ..., 8476, 7727, 1588]), + values=tensor([0.9992, 0.3273, 0.0949, ..., 0.9070, 0.7782, 0.9129]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3589, 0.4614, 0.1782, ..., 0.3543, 0.5532, 0.1489]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.052317142486572266 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200699', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.282996416091919} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 3, ..., 9998, 10000, 10000]), + col_indices=tensor([5654, 2010, 6092, ..., 8357, 4618, 8765]), + values=tensor([0.6548, 0.7548, 0.4241, ..., 0.2252, 0.7987, 0.4358]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.1881, 0.6615, 0.7402, ..., 0.4130, 0.3712, 0.1085]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 7.282996416091919 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289350', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.562561988830566} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 9997, 9999, 10000]), + col_indices=tensor([ 41, 4057, 4525, ..., 395, 6429, 4913]), + values=tensor([0.6795, 0.3093, 0.3215, ..., 0.9868, 0.7022, 0.9945]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0893, 0.5810, 0.8251, ..., 0.0535, 0.5355, 0.1364]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.562561988830566 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 9997, 9999, 10000]), + col_indices=tensor([ 41, 4057, 4525, ..., 395, 6429, 4913]), + values=tensor([0.6795, 0.3093, 0.3215, ..., 0.9868, 0.7022, 0.9945]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0893, 0.5810, 0.8251, ..., 0.0535, 0.5355, 0.1364]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.562561988830566 seconds + +[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53] +[96.48] +13.216078281402588 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48} +[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53, 39.33, 38.72, 38.74, 39.53, 38.66, 39.45, 38.52, 39.57, 38.65, 38.7] +703.1500000000001 +35.157500000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48, 'J_1KI': 4.406729678900023, 'W_1KI': 0.3334370139968896, 'W_D': 61.3225, 'J_D': 810.4429604113102, 'W_D_1KI': 0.21193191636426473, 'J_D_1KI': 0.0007324413905797986} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..b5102bb --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 187965, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.065882205963135, "TIME_S_1KI": 0.053551896395409436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.2036644935608, "W": 106.8, "J_1KI": 7.1513508604982885, "W_1KI": 0.568190886601229, "W_D": 70.53074999999998, "J_D": 887.7124776168464, "W_D_1KI": 0.3752334211156331, "J_D_1KI": 0.001996294103240673} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..81bf5e6 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07673120498657227} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 16, ..., 99979, 99991, + 100000]), + col_indices=tensor([ 168, 470, 1159, ..., 7824, 8386, 8755]), + values=tensor([0.2770, 0.4979, 0.7971, ..., 0.1786, 0.3153, 0.6794]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8782, 0.5630, 0.5978, ..., 0.9864, 0.4940, 0.0083]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.07673120498657227 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '136841', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.644104957580566} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 23, ..., 99978, 99990, + 100000]), + col_indices=tensor([1562, 4109, 4242, ..., 5789, 5816, 7878]), + values=tensor([0.3397, 0.5295, 0.0107, ..., 0.2250, 0.1834, 0.1775]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8704, 0.9073, 0.5102, ..., 0.5120, 0.6818, 0.6416]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 7.644104957580566 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '187965', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.065882205963135} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 25, ..., 99981, 99991, + 100000]), + col_indices=tensor([ 564, 1289, 1589, ..., 8514, 9743, 9976]), + values=tensor([0.9535, 0.4673, 0.4047, ..., 0.1356, 0.2907, 0.4698]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6755, 0.5642, 0.0135, ..., 0.9982, 0.6342, 0.7704]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.065882205963135 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 13, 25, ..., 99981, 99991, + 100000]), + col_indices=tensor([ 564, 1289, 1589, ..., 8514, 9743, 9976]), + values=tensor([0.9535, 0.4673, 0.4047, ..., 0.1356, 0.2907, 0.4698]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6755, 0.5642, 0.0135, ..., 0.9982, 0.6342, 0.7704]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.065882205963135 seconds + +[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58] +[106.8] +12.586176633834839 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8} +[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58, 39.31, 39.68, 39.37, 39.04, 45.6, 48.05, 38.82, 39.58, 38.6, 39.88] +725.3850000000001 +36.26925000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8, 'J_1KI': 7.1513508604982885, 'W_1KI': 0.568190886601229, 'W_D': 70.53074999999998, 'J_D': 887.7124776168464, 'W_D_1KI': 0.3752334211156331, 'J_D_1KI': 0.001996294103240673} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..be0819d --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 105478, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5971040725708, "TIME_S_1KI": 0.10046743465529115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1740.769259557724, "W": 131.98, "J_1KI": 16.503624069073396, "W_1KI": 1.2512561861241205, "W_D": 96.29974999999999, "J_D": 1270.159452213168, "W_D_1KI": 0.912984224198411, "J_D_1KI": 0.008655683879087687} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..1731635 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.16547083854675293} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 96, 207, ..., 999810, + 999906, 1000000]), + col_indices=tensor([ 26, 37, 76, ..., 9653, 9723, 9999]), + values=tensor([0.3241, 0.3803, 0.4811, ..., 0.7106, 0.6386, 0.1440]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9687, 0.4748, 0.5344, ..., 0.6395, 0.7779, 0.2708]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 0.16547083854675293 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63455', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.316709756851196} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 90, 176, ..., 999794, + 999890, 1000000]), + col_indices=tensor([ 23, 147, 291, ..., 9810, 9851, 9893]), + values=tensor([0.8158, 0.9343, 0.8649, ..., 0.9539, 0.1935, 0.2240]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.7787, 0.2300, 0.4854, ..., 0.5355, 0.5696, 0.8377]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 6.316709756851196 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105478', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5971040725708} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 112, 216, ..., 999816, + 999921, 1000000]), + col_indices=tensor([ 50, 64, 228, ..., 9846, 9935, 9998]), + values=tensor([0.2081, 0.8355, 0.6203, ..., 0.0415, 0.1924, 0.6602]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3579, 0.4434, 0.7372, ..., 0.2272, 0.7887, 0.7519]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.5971040725708 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 112, 216, ..., 999816, + 999921, 1000000]), + col_indices=tensor([ 50, 64, 228, ..., 9846, 9935, 9998]), + values=tensor([0.2081, 0.8355, 0.6203, ..., 0.0415, 0.1924, 0.6602]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3579, 0.4434, 0.7372, ..., 0.2272, 0.7887, 0.7519]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.5971040725708 seconds + +[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85] +[131.98] +13.18964433670044 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98} +[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85, 39.66, 39.98, 38.99, 40.02, 39.22, 39.28, 39.07, 41.17, 38.93, 45.27] +713.6050000000001 +35.68025000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98, 'J_1KI': 16.503624069073396, 'W_1KI': 1.2512561861241205, 'W_D': 96.29974999999999, 'J_D': 1270.159452213168, 'W_D_1KI': 0.912984224198411, 'J_D_1KI': 0.008655683879087687} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..2c99a83 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28261, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.545162916183472, "TIME_S_1KI": 0.37313481179659147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2163.6921325206754, "W": 147.39, "J_1KI": 76.56106056122131, "W_1KI": 5.2153143908566575, "W_D": 111.76774999999999, "J_D": 1640.7558270204065, "W_D_1KI": 3.9548405930434165, "J_D_1KI": 0.13993986741599437} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..d58d64d --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.4614067077636719} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 523, 1040, ..., 4999055, + 4999519, 5000000]), + col_indices=tensor([ 1, 5, 26, ..., 9948, 9962, 9996]), + values=tensor([0.6869, 0.8475, 0.6936, ..., 0.3132, 0.2618, 0.7215]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5929, 0.6414, 0.0366, ..., 0.9216, 0.5044, 0.3359]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 0.4614067077636719 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22756', '-ss', '10000', '-sd', '0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.454672574996948} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 518, 995, ..., 4998951, + 4999482, 5000000]), + col_indices=tensor([ 3, 5, 12, ..., 9960, 9985, 9990]), + values=tensor([0.0194, 0.0116, 0.2988, ..., 0.0510, 0.2477, 0.0241]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.8184, 0.3974, 0.7641, ..., 0.0303, 0.5906, 0.4265]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 8.454672574996948 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28261', '-ss', '10000', '-sd', '0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.545162916183472} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 472, 967, ..., 4998984, + 4999479, 5000000]), + col_indices=tensor([ 28, 36, 55, ..., 9923, 9953, 9987]), + values=tensor([0.3537, 0.0932, 0.3681, ..., 0.2268, 0.3044, 0.8997]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5459, 0.4301, 0.8105, ..., 0.9349, 0.4459, 0.6946]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.545162916183472 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 472, 967, ..., 4998984, + 4999479, 5000000]), + col_indices=tensor([ 28, 36, 55, ..., 9923, 9953, 9987]), + values=tensor([0.3537, 0.0932, 0.3681, ..., 0.2268, 0.3044, 0.8997]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.5459, 0.4301, 0.8105, ..., 0.9349, 0.4459, 0.6946]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.545162916183472 seconds + +[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94] +[147.39] +14.680047035217285 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39} +[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94, 39.9, 39.27, 40.14, 39.15, 40.0, 39.07, 39.99, 39.51, 39.37, 39.14] +712.4449999999999 +35.622249999999994 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39, 'J_1KI': 76.56106056122131, 'W_1KI': 5.2153143908566575, 'W_D': 111.76774999999999, 'J_D': 1640.7558270204065, 'W_D_1KI': 3.9548405930434165, 'J_D_1KI': 0.13993986741599437} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..e9f145d --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 352057, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363084554672241, "TIME_S_1KI": 0.029435814526262056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.9154741740226, "W": 94.69, "J_1KI": 3.4764696460346554, "W_1KI": 0.2689621282917255, "W_D": 59.05925, "J_D": 763.3702605144381, "W_D_1KI": 0.1677547953882468, "J_D_1KI": 0.0004764989629186376} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..f1c1f4f --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1414 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05632638931274414} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([6812, 5345, 3814, 3851, 1180, 2370, 9747, 9157, 9309, + 1844, 451, 6602, 4443, 8006, 5413, 9948, 6902, 4781, + 5273, 3102, 9193, 6090, 9660, 8598, 9786, 3453, 7823, + 8095, 5864, 1933, 5014, 3401, 1663, 8599, 9714, 5815, + 973, 4504, 306, 2971, 7185, 220, 7724, 5778, 2532, + 0, 8277, 8525, 5899, 2513, 5457, 8721, 2772, 4422, + 997, 2101, 9163, 4690, 3655, 646, 1228, 2676, 5080, + 9204, 4653, 8512, 580, 9554, 3549, 201, 5889, 9262, + 3348, 7948, 7695, 1711, 5747, 7743, 1681, 5808, 2747, + 7029, 7665, 8165, 7858, 569, 2064, 4739, 7568, 177, + 9310, 4386, 8240, 6642, 4389, 3996, 4876, 1054, 4163, + 3621, 8213, 1627, 3052, 4037, 3228, 47, 4120, 8716, + 1140, 654, 1138, 8841, 9286, 6853, 8247, 7250, 6739, + 1808, 169, 5660, 5955, 2424, 5623, 268, 7108, 2287, + 739, 2574, 9748, 9883, 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'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '186413', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.5597083568573} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 875, 2191, 8400, 7819, 1359, 1371, 2712, 1430, 699, + 6200, 2366, 7177, 863, 6066, 3455, 4404, 1664, 5210, + 4431, 2329, 8118, 7744, 8468, 6759, 56, 4135, 8355, + 1937, 8979, 8770, 7419, 5776, 718, 8064, 3859, 6591, + 2824, 3492, 4055, 3388, 2836, 5059, 5536, 4952, 4131, + 4038, 8683, 413, 5705, 359, 3435, 651, 1108, 9531, + 6875, 4330, 1115, 5593, 2969, 6345, 1365, 6966, 630, + 8757, 209, 7065, 9539, 2263, 5307, 3566, 6539, 5643, + 3281, 4970, 9273, 8736, 4719, 8846, 4254, 1009, 7367, + 2015, 364, 1240, 851, 7365, 8720, 4893, 9717, 9512, + 3001, 5085, 106, 3869, 9655, 8756, 4703, 6792, 2300, + 7273, 7994, 8012, 1150, 5161, 4585, 4463, 3174, 2598, + 1009, 114, 1091, 647, 6685, 1799, 5606, 4368, 8317, + 6800, 8461, 2401, 9532, 5943, 3524, 9561, 3530, 7573, + 7996, 276, 5910, 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synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 5.5597083568573 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '352057', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363084554672241} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([5193, 2755, 2619, 8774, 5321, 6802, 9831, 2285, 7852, + 3999, 9956, 6338, 4303, 3557, 3117, 6782, 5048, 7592, + 3942, 736, 4361, 9482, 6490, 3337, 2778, 8169, 2811, + 209, 2573, 8564, 5262, 8591, 5293, 8927, 3544, 51, + 2528, 4507, 4161, 5578, 9752, 6784, 2306, 938, 2449, + 5328, 718, 7617, 6097, 864, 5625, 9977, 6328, 2206, + 1192, 3645, 3508, 3808, 3742, 5641, 1622, 4352, 9099, + 7155, 1778, 6225, 7403, 1744, 1586, 3123, 5186, 9952, + 4753, 6792, 5057, 2040, 1903, 4935, 4855, 6732, 8949, + 5033, 9687, 8172, 2973, 4285, 3263, 8170, 5631, 2665, + 2030, 1676, 7190, 9261, 1374, 5085, 6991, 7291, 5365, + 8790, 2603, 5128, 4726, 7347, 7445, 5508, 2405, 6862, + 927, 1040, 3233, 8284, 1163, 7143, 7742, 2101, 6504, + 7643, 3848, 7449, 288, 874, 4468, 4224, 1484, 7263, + 4340, 7167, 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+Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.363084554672241 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([5193, 2755, 2619, 8774, 5321, 6802, 9831, 2285, 7852, + 3999, 9956, 6338, 4303, 3557, 3117, 6782, 5048, 7592, + 3942, 736, 4361, 9482, 6490, 3337, 2778, 8169, 2811, + 209, 2573, 8564, 5262, 8591, 5293, 8927, 3544, 51, + 2528, 4507, 4161, 5578, 9752, 6784, 2306, 938, 2449, + 5328, 718, 7617, 6097, 864, 5625, 9977, 6328, 2206, + 1192, 3645, 3508, 3808, 3742, 5641, 1622, 4352, 9099, + 7155, 1778, 6225, 7403, 1744, 1586, 3123, 5186, 9952, + 4753, 6792, 5057, 2040, 1903, 4935, 4855, 6732, 8949, + 5033, 9687, 8172, 2973, 4285, 3263, 8170, 5631, 2665, + 2030, 1676, 7190, 9261, 1374, 5085, 6991, 7291, 5365, + 8790, 2603, 5128, 4726, 7347, 7445, 5508, 2405, 6862, + 927, 1040, 3233, 8284, 1163, 7143, 7742, 2101, 6504, + 7643, 3848, 7449, 288, 874, 4468, 4224, 1484, 7263, + 4340, 7167, 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+Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.363084554672241 seconds + +[39.17, 38.6, 39.23, 38.35, 39.37, 38.46, 39.25, 43.56, 39.01, 38.43] +[94.69] +12.925498723983765 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 352057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.363084554672241, 'TIME_S_1KI': 0.029435814526262056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9154741740226, 'W': 94.69} +[39.17, 38.6, 39.23, 38.35, 39.37, 38.46, 39.25, 43.56, 39.01, 38.43, 39.13, 39.47, 39.53, 39.29, 44.98, 39.37, 38.9, 38.56, 38.54, 39.56] +712.615 +35.63075 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 352057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.363084554672241, 'TIME_S_1KI': 0.029435814526262056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9154741740226, 'W': 94.69, 'J_1KI': 3.4764696460346554, 'W_1KI': 0.2689621282917255, 'W_D': 59.05925, 'J_D': 763.3702605144381, 'W_D_1KI': 0.1677547953882468, 'J_D_1KI': 0.0004764989629186376} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..9ddd17e --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.323282241821289, "TIME_S_1KI": 0.482509102211792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2021.2716293263436, "W": 152.47, "J_1KI": 94.47401866447038, "W_1KI": 7.1264314092077585, "W_D": 115.044, "J_D": 1525.1208324537276, "W_D_1KI": 5.377144192568356, "J_D_1KI": 0.2513271415082195} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..50f6332 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.532757043838501} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 8, ..., 2499994, + 2499995, 2500000]), + col_indices=tensor([298854, 299868, 317882, ..., 208197, 239895, + 321556]), + values=tensor([0.0947, 0.1899, 0.7776, ..., 0.8480, 0.0740, 0.2913]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.2732, 0.7262, 0.3001, ..., 0.8229, 0.3388, 0.7233]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 0.532757043838501 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19708', '-ss', '500000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.672011375427246} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 7, ..., 2499994, + 2500000, 2500000]), + col_indices=tensor([ 49185, 277910, 351023, ..., 230263, 378248, + 487183]), + values=tensor([0.7966, 0.8451, 0.5460, ..., 0.3570, 0.2848, 0.9857]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8196, 0.2368, 0.8865, ..., 0.6520, 0.2281, 0.7931]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 9.672011375427246 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21395', '-ss', '500000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.323282241821289} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 8, ..., 2499995, + 2499998, 2500000]), + col_indices=tensor([ 50735, 77236, 160897, ..., 492852, 393041, + 457835]), + values=tensor([0.2461, 0.0110, 0.8932, ..., 0.0580, 0.2778, 0.4102]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4625, 0.6924, 0.9316, ..., 0.4127, 0.3248, 0.5422]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.323282241821289 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 8, ..., 2499995, + 2499998, 2500000]), + col_indices=tensor([ 50735, 77236, 160897, ..., 492852, 393041, + 457835]), + values=tensor([0.2461, 0.0110, 0.8932, ..., 0.0580, 0.2778, 0.4102]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4625, 0.6924, 0.9316, ..., 0.4127, 0.3248, 0.5422]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.323282241821289 seconds + +[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64] +[152.47] +13.256848096847534 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47} +[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64, 42.68, 45.88, 39.43, 39.56, 39.31, 40.35, 46.4, 49.8, 39.76, 40.2] +748.52 +37.426 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47, 'J_1KI': 94.47401866447038, 'W_1KI': 7.1264314092077585, 'W_D': 115.044, 'J_D': 1525.1208324537276, 'W_D_1KI': 5.377144192568356, 'J_D_1KI': 0.2513271415082195} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..f0cbadb --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 97887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.967289686203003, "TIME_S_1KI": 0.11204030858237563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1548.6584901952745, "W": 117.34, "J_1KI": 15.820880098432626, "W_1KI": 1.1987291468734356, "W_D": 81.498, "J_D": 1075.6141949372293, "W_D_1KI": 0.8325722516779552, "J_D_1KI": 0.008505442517167297} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..a9ce226 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1396017074584961} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 11, ..., 249990, 249993, + 250000]), + col_indices=tensor([ 1901, 17696, 37644, ..., 22666, 31352, 38471]), + values=tensor([0.6079, 0.0811, 0.7282, ..., 0.2667, 0.3886, 0.6657]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5204, 0.6126, 0.8277, ..., 0.7159, 0.4461, 0.9246]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.1396017074584961 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75213', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.06783390045166} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 4, 10, ..., 249992, 249996, + 250000]), + col_indices=tensor([ 3649, 15078, 16220, ..., 32895, 36388, 49599]), + values=tensor([0.6393, 0.2992, 0.9532, ..., 0.0270, 0.3430, 0.6378]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.0844, 0.1224, 0.7905, ..., 0.3661, 0.3101, 0.4173]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 8.06783390045166 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '97887', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.967289686203003} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 14, ..., 249992, 249996, + 250000]), + col_indices=tensor([ 9116, 23500, 25241, ..., 7305, 15035, 46474]), + values=tensor([0.8636, 0.6633, 0.2645, ..., 0.7208, 0.8992, 0.1134]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.3603, 0.4772, 0.1653, ..., 0.3951, 0.3400, 0.6722]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.967289686203003 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 14, ..., 249992, 249996, + 250000]), + col_indices=tensor([ 9116, 23500, 25241, ..., 7305, 15035, 46474]), + values=tensor([0.8636, 0.6633, 0.2645, ..., 0.7208, 0.8992, 0.1134]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.3603, 0.4772, 0.1653, ..., 0.3951, 0.3400, 0.6722]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.967289686203003 seconds + +[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15] +[117.34] +13.198044061660767 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34} +[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15, 49.08, 39.91, 39.13, 39.81, 39.05, 39.67, 39.05, 40.19, 39.16, 39.9] +716.84 +35.842 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34, 'J_1KI': 15.820880098432626, 'W_1KI': 1.1987291468734356, 'W_D': 81.498, 'J_D': 1075.6141949372293, 'W_D_1KI': 0.8325722516779552, 'J_D_1KI': 0.008505442517167297} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..dcf99f8 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 47277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432827234268188, "TIME_S_1KI": 0.2206744766856651, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.9828476619723, "W": 146.36, "J_1KI": 41.393972706854754, "W_1KI": 3.095797110645769, "W_D": 110.02925000000002, "J_D": 1471.2035733199718, "W_D_1KI": 2.3273314719631117, "J_D_1KI": 0.04922756249260976} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..c946bac --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2981231212615967} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 43, 103, ..., 2499901, + 2499951, 2500000]), + col_indices=tensor([ 154, 1105, 2164, ..., 43048, 45641, 46786]), + values=tensor([0.5353, 0.9585, 0.2831, ..., 0.0513, 0.1909, 0.0614]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3993, 0.6905, 0.7348, ..., 0.6851, 0.9182, 0.5409]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 0.2981231212615967 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35220', '-ss', '50000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.822157621383667} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 48, 100, ..., 2499912, + 2499953, 2500000]), + col_indices=tensor([ 120, 161, 363, ..., 47642, 48044, 49939]), + values=tensor([0.7949, 0.8676, 0.3054, ..., 0.9459, 0.0848, 0.8977]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4844, 0.7866, 0.3385, ..., 0.0837, 0.3382, 0.6328]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 7.822157621383667 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '47277', '-ss', '50000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432827234268188} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 62, 109, ..., 2499897, + 2499942, 2500000]), + col_indices=tensor([ 2040, 2609, 3779, ..., 46933, 47654, 47998]), + values=tensor([0.9101, 0.3119, 0.8580, ..., 0.1192, 0.4361, 0.9803]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1061, 0.6227, 0.1589, ..., 0.5507, 0.9975, 0.5119]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.432827234268188 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 62, 109, ..., 2499897, + 2499942, 2500000]), + col_indices=tensor([ 2040, 2609, 3779, ..., 46933, 47654, 47998]), + values=tensor([0.9101, 0.3119, 0.8580, ..., 0.1192, 0.4361, 0.9803]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.1061, 0.6227, 0.1589, ..., 0.5507, 0.9975, 0.5119]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.432827234268188 seconds + +[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06] +[146.36] +13.371022462844849 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36} +[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06, 40.74, 39.25, 45.03, 39.17, 39.46, 39.16, 40.2, 39.66, 39.96, 39.62] +726.615 +36.33075 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36, 'J_1KI': 41.393972706854754, 'W_1KI': 3.095797110645769, 'W_D': 110.02925000000002, 'J_D': 1471.2035733199718, 'W_D_1KI': 2.3273314719631117, 'J_D_1KI': 0.04922756249260976} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..a40aca5 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129830, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.498366355895996, "TIME_S_1KI": 0.08086240742429328, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1343.8986691188811, "W": 102.66, "J_1KI": 10.351218278663492, "W_1KI": 0.7907263344373412, "W_D": 67.04849999999999, "J_D": 877.7166366298197, "W_D_1KI": 0.5164330278055919, "J_D_1KI": 0.003977763443006947} diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..7b7017b --- /dev/null +++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,101 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11773824691772461} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([16845, 2751, 33930, ..., 33536, 38018, 30474]), + values=tensor([0.6858, 0.5470, 0.3190, ..., 0.3110, 0.3011, 0.6040]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7348, 0.5937, 0.8612, ..., 0.8920, 0.9109, 0.1161]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.11773824691772461 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89180', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.942249059677124} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 25000, 25000, 25000]), + col_indices=tensor([ 5133, 25494, 8495, ..., 18153, 14682, 27268]), + values=tensor([0.7177, 0.6433, 0.0497, ..., 0.6766, 0.5365, 0.3286]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.6426, 0.1118, 0.3197, ..., 0.9296, 0.1873, 0.3702]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 7.942249059677124 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '117899', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.535074234008789} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 24998, 24998, 25000]), + col_indices=tensor([ 1468, 1704, 43281, ..., 3197, 24132, 30286]), + values=tensor([1.4228e-01, 5.9740e-01, 9.5210e-06, ..., + 2.4125e-01, 6.2955e-01, 4.9169e-01]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.8788, 0.8743, 0.0964, ..., 0.0391, 0.4204, 0.2909]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 9.535074234008789 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '129830', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.498366355895996} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([16477, 45779, 22583, ..., 30055, 21515, 45820]), + values=tensor([0.8893, 0.7790, 0.5329, ..., 0.5529, 0.2667, 0.0404]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.0469, 0.9963, 0.7558, ..., 0.9652, 0.6676, 0.7778]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.498366355895996 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 1, ..., 25000, 25000, 25000]), + col_indices=tensor([16477, 45779, 22583, ..., 30055, 21515, 45820]), + values=tensor([0.8893, 0.7790, 0.5329, ..., 0.5529, 0.2667, 0.0404]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.0469, 0.9963, 0.7558, ..., 0.9652, 0.6676, 0.7778]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.498366355895996 seconds + +[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2] +[102.66] +13.090772151947021 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66} +[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2, 39.71, 39.93, 38.98, 39.86, 39.33, 39.67, 39.03, 39.93, 39.08, 39.73] +712.23 +35.6115 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66, 'J_1KI': 10.351218278663492, 'W_1KI': 0.7907263344373412, 'W_D': 67.04849999999999, 'J_D': 877.7166366298197, 'W_D_1KI': 0.5164330278055919, 'J_D_1KI': 0.003977763443006947} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json new file mode 100644 index 0000000..1cb7a6a --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33560, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.490610837936401, "TIME_S_1KI": 0.3125926948133612, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1237.3838691329956, "W": 88.17, "J_1KI": 36.87079467023229, "W_1KI": 2.6272348033373065, "W_D": 71.61225, "J_D": 1005.0112621335984, "W_D_1KI": 2.133857270560191, "J_D_1KI": 0.06358335132777686} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output new file mode 100644 index 0000000..14d045d --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3128688335418701} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 17, 23, ..., 999976, + 999990, 1000000]), + col_indices=tensor([ 283, 794, 12077, ..., 88041, 96002, 98956]), + values=tensor([0.6667, 0.7061, 0.4936, ..., 0.0020, 0.2226, 0.8107]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3746, 0.1787, 0.3326, ..., 0.2981, 0.5262, 0.4171]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 0.3128688335418701 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33560', '-ss', '100000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.490610837936401} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 24, ..., 999975, + 999989, 1000000]), + col_indices=tensor([ 291, 3246, 3703, ..., 78390, 83116, 86469]), + values=tensor([0.7026, 0.5046, 0.5818, ..., 0.3671, 0.4061, 0.2873]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3720, 0.0968, 0.4099, ..., 0.6733, 0.7032, 0.3728]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.490610837936401 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 24, ..., 999975, + 999989, 1000000]), + col_indices=tensor([ 291, 3246, 3703, ..., 78390, 83116, 86469]), + values=tensor([0.7026, 0.5046, 0.5818, ..., 0.3671, 0.4061, 0.2873]), + size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.3720, 0.0968, 0.4099, ..., 0.6733, 0.7032, 0.3728]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 1000000 +Density: 0.0001 +Time: 10.490610837936401 seconds + +[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94] +[88.17] +14.034069061279297 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17} +[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94, 18.37, 20.96, 17.82, 18.03, 18.16, 17.77, 17.89, 17.87, 17.71, 18.52] +331.155 +16.55775 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17, 'J_1KI': 36.87079467023229, 'W_1KI': 2.6272348033373065, 'W_D': 71.61225, 'J_D': 1005.0112621335984, 'W_D_1KI': 2.133857270560191, 'J_D_1KI': 0.06358335132777686} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json new file mode 100644 index 0000000..0f4c2a6 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 65588, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.838059663772583, "TIME_S_1KI": 0.16524455180479025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1179.1915578985215, "W": 82.78, "J_1KI": 17.97876986489177, "W_1KI": 1.2621211197170215, "W_D": 66.50475, "J_D": 947.3524977065921, "W_D_1KI": 1.0139774044032446, "J_D_1KI": 0.015459800640410512} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output new file mode 100644 index 0000000..7b64a62 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.17682647705078125} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 6, ..., 99999, 99999, + 100000]), + col_indices=tensor([ 3198, 22722, 88522, ..., 47695, 53177, 56584]), + values=tensor([0.0931, 0.9110, 0.9063, ..., 0.1473, 0.7899, 0.0419]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.4850, 0.3145, 0.7013, ..., 0.1298, 0.2149, 0.6470]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 0.17682647705078125 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59380', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.506051540374756} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 2, ..., 99999, 99999, + 100000]), + col_indices=tensor([45126, 76716, 27115, ..., 82599, 76675, 53817]), + values=tensor([0.5870, 0.5895, 0.9992, ..., 0.5279, 0.4372, 0.6677]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.8372, 0.3480, 0.3478, ..., 0.9164, 0.0517, 0.0932]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 9.506051540374756 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65588', '-ss', '100000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.838059663772583} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 100000, 100000, + 100000]), + col_indices=tensor([69179, 69629, 89362, ..., 28216, 37414, 39020]), + values=tensor([0.6325, 0.8110, 0.8083, ..., 0.4927, 0.7217, 0.7562]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6752, 0.8314, 0.5534, ..., 0.1964, 0.0025, 0.5959]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.838059663772583 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 3, ..., 100000, 100000, + 100000]), + col_indices=tensor([69179, 69629, 89362, ..., 28216, 37414, 39020]), + values=tensor([0.6325, 0.8110, 0.8083, ..., 0.4927, 0.7217, 0.7562]), + size=(100000, 100000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6752, 0.8314, 0.5534, ..., 0.1964, 0.0025, 0.5959]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([100000, 100000]) +Rows: 100000 +Size: 10000000000 +NNZ: 100000 +Density: 1e-05 +Time: 10.838059663772583 seconds + +[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89] +[82.78] +14.244884729385376 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78} +[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89, 18.4, 17.89, 17.79, 17.88, 18.16, 18.32, 18.28, 17.67, 18.23, 18.52] +325.505 +16.27525 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78, 'J_1KI': 17.97876986489177, 'W_1KI': 1.2621211197170215, 'W_D': 66.50475, 'J_D': 947.3524977065921, 'W_D_1KI': 1.0139774044032446, 'J_D_1KI': 0.015459800640410512} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json new file mode 100644 index 0000000..ff48609 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 240931, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.228987216949463, "TIME_S_1KI": 0.04245608583764423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 972.0923814868927, "W": 74.28, "J_1KI": 4.034733519085932, "W_1KI": 0.30830403725547983, "W_D": 58.167500000000004, "J_D": 761.2302584832908, "W_D_1KI": 0.2414280437137604, "J_D_1KI": 0.0010020630127038877} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output new file mode 100644 index 0000000..36a267f --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.062392234802246094} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 3, ..., 9997, 9999, 10000]), + col_indices=tensor([7179, 9532, 8081, ..., 4031, 8581, 2872]), + values=tensor([0.3998, 0.4929, 0.1773, ..., 0.2243, 0.6349, 0.5923]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.2436, 0.6971, 0.0487, ..., 0.2986, 0.9140, 0.9941]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 0.062392234802246094 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '168290', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.3342225551605225} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 2, ..., 10000, 10000, 10000]), + col_indices=tensor([7117, 7845, 2903, ..., 807, 7859, 5458]), + values=tensor([0.8544, 0.9061, 0.0037, ..., 0.6594, 0.1915, 0.6916]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.0592, 0.4192, 0.0774, ..., 0.7897, 0.5835, 0.6060]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 7.3342225551605225 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '240931', '-ss', '10000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.228987216949463} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 4, ..., 9999, 10000, 10000]), + col_indices=tensor([1962, 399, 6914, ..., 7707, 7379, 8204]), + values=tensor([0.6427, 0.2940, 0.2788, ..., 0.7421, 0.9158, 0.7396]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3387, 0.7040, 0.3501, ..., 0.4098, 0.3396, 0.7875]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.228987216949463 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1, 4, ..., 9999, 10000, 10000]), + col_indices=tensor([1962, 399, 6914, ..., 7707, 7379, 8204]), + values=tensor([0.6427, 0.2940, 0.2788, ..., 0.7421, 0.9158, 0.7396]), + size=(10000, 10000), nnz=10000, layout=torch.sparse_csr) +tensor([0.3387, 0.7040, 0.3501, ..., 0.4098, 0.3396, 0.7875]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 10000 +Density: 0.0001 +Time: 10.228987216949463 seconds + +[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05] +[74.28] +13.086865663528442 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28} +[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05, 18.35, 17.85, 17.96, 17.83, 18.33, 17.85, 17.85, 17.82, 18.0, 17.86] +322.25 +16.1125 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28, 'J_1KI': 4.034733519085932, 'W_1KI': 0.30830403725547983, 'W_D': 58.167500000000004, 'J_D': 761.2302584832908, 'W_D_1KI': 0.2414280437137604, 'J_D_1KI': 0.0010020630127038877} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json new file mode 100644 index 0000000..4c2a534 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 201421, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7230703830719, "TIME_S_1KI": 0.053237102303493176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1117.283116903305, "W": 79.97, "J_1KI": 5.547004120242204, "W_1KI": 0.3970291081863361, "W_D": 63.60725, "J_D": 888.6745846898556, "W_D_1KI": 0.31579254397505724, "J_D_1KI": 0.001567823335079546} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output new file mode 100644 index 0000000..1cf380c --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06886577606201172} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 12, 19, ..., 99984, 99991, + 100000]), + col_indices=tensor([1627, 2251, 2667, ..., 7083, 9414, 9995]), + values=tensor([0.7763, 0.8562, 0.0227, ..., 0.7081, 0.0734, 0.4206]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.6749, 0.4550, 0.5239, ..., 0.7938, 0.7493, 0.7052]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 0.06886577606201172 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152470', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.948191404342651} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 14, 22, ..., 99977, 99992, + 100000]), + col_indices=tensor([ 579, 1179, 1463, ..., 6326, 6539, 6627]), + values=tensor([0.4661, 0.6191, 0.1376, ..., 0.4152, 0.1640, 0.4813]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.0160, 0.8279, 0.2510, ..., 0.4302, 0.2870, 0.5452]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 7.948191404342651 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '201421', '-ss', '10000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7230703830719} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 17, ..., 99977, 99988, + 100000]), + col_indices=tensor([ 243, 1001, 2007, ..., 7428, 8081, 8733]), + values=tensor([0.5597, 0.5588, 0.7631, ..., 0.2707, 0.4657, 0.9680]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1756, 0.9887, 0.2623, ..., 0.3846, 0.9664, 0.0716]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.7230703830719 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 11, 17, ..., 99977, 99988, + 100000]), + col_indices=tensor([ 243, 1001, 2007, ..., 7428, 8081, 8733]), + values=tensor([0.5597, 0.5588, 0.7631, ..., 0.2707, 0.4657, 0.9680]), + size=(10000, 10000), nnz=100000, layout=torch.sparse_csr) +tensor([0.1756, 0.9887, 0.2623, ..., 0.3846, 0.9664, 0.0716]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 100000 +Density: 0.001 +Time: 10.7230703830719 seconds + +[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45] +[79.97] +13.971278190612793 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97} +[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45, 18.1, 18.11, 18.32, 17.8, 18.31, 17.97, 17.94, 17.86, 17.95, 18.22] +327.255 +16.36275 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97, 'J_1KI': 5.547004120242204, 'W_1KI': 0.3970291081863361, 'W_D': 63.60725, 'J_D': 888.6745846898556, 'W_D_1KI': 0.31579254397505724, 'J_D_1KI': 0.001567823335079546} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json new file mode 100644 index 0000000..7e50353 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.521214962005615, "TIME_S_1KI": 0.1790601273359477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1248.3679735660553, "W": 87.25, "J_1KI": 21.245923509412425, "W_1KI": 1.484904183260152, "W_D": 70.41275, "J_D": 1007.4615705525875, "W_D_1KI": 1.1983517138091835, "J_D_1KI": 0.020394698829251906} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output new file mode 100644 index 0000000..560bbb8 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.19649839401245117} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 90, 190, ..., 999800, + 999902, 1000000]), + col_indices=tensor([ 52, 87, 188, ..., 9706, 9893, 9952]), + values=tensor([0.1675, 0.8959, 0.7675, ..., 0.1378, 0.1178, 0.3486]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9304, 0.9814, 0.5110, ..., 0.0040, 0.2898, 0.8662]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 0.19649839401245117 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53435', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.548681497573853} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 94, 197, ..., 999809, + 999893, 1000000]), + col_indices=tensor([ 61, 165, 222, ..., 9905, 9907, 9919]), + values=tensor([0.6376, 0.5545, 0.9458, ..., 0.6333, 0.2848, 0.3343]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.2834, 0.7754, 0.6738, ..., 0.4578, 0.3713, 0.7996]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 9.548681497573853 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58758', '-ss', '10000', '-sd', '0.01'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.521214962005615} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 109, 219, ..., 999802, + 999904, 1000000]), + col_indices=tensor([ 63, 137, 260, ..., 9828, 9873, 9905]), + values=tensor([0.1449, 0.8321, 0.3255, ..., 0.3929, 0.1108, 0.3040]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9048, 0.1055, 0.1608, ..., 0.3713, 0.7919, 0.0232]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.521214962005615 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 109, 219, ..., 999802, + 999904, 1000000]), + col_indices=tensor([ 63, 137, 260, ..., 9828, 9873, 9905]), + values=tensor([0.1449, 0.8321, 0.3255, ..., 0.3929, 0.1108, 0.3040]), + size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr) +tensor([0.9048, 0.1055, 0.1608, ..., 0.3713, 0.7919, 0.0232]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000000 +Density: 0.01 +Time: 10.521214962005615 seconds + +[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87] +[87.25] +14.307942390441895 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25} +[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87, 18.33, 21.51, 17.89, 18.53, 18.27, 17.81, 18.21, 18.47, 18.07, 19.18] +336.745 +16.83725 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25, 'J_1KI': 21.245923509412425, 'W_1KI': 1.484904183260152, 'W_D': 70.41275, 'J_D': 1007.4615705525875, 'W_D_1KI': 1.1983517138091835, 'J_D_1KI': 0.020394698829251906} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json new file mode 100644 index 0000000..c7df7b8 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496035814285278, "TIME_S_1KI": 1.1925958202801135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1351.109428257942, "W": 82.21, "J_1KI": 153.5177171069131, "W_1KI": 9.340983979093284, "W_D": 65.94874999999999, "J_D": 1083.858142644763, "W_D_1KI": 7.493324622202022, "J_D_1KI": 0.8514174096354984} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output new file mode 100644 index 0000000..7df5a22 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1929755210876465} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 485, 983, ..., 4998996, + 4999523, 5000000]), + col_indices=tensor([ 11, 47, 113, ..., 9897, 9981, 9996]), + values=tensor([0.8953, 0.8081, 0.2668, ..., 0.4279, 0.4927, 0.2076]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.3301, 0.9128, 0.0218, ..., 0.3705, 0.4449, 0.9102]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 1.1929755210876465 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8801', '-ss', '10000', '-sd', '0.05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496035814285278} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 509, 1022, ..., 4998954, + 4999475, 5000000]), + col_indices=tensor([ 16, 27, 72, ..., 9970, 9971, 9996]), + values=tensor([0.8982, 0.6195, 0.1567, ..., 0.8636, 0.4059, 0.3830]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.3042, 0.6883, 0.8193, ..., 0.9178, 0.9438, 0.4311]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.496035814285278 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 509, 1022, ..., 4998954, + 4999475, 5000000]), + col_indices=tensor([ 16, 27, 72, ..., 9970, 9971, 9996]), + values=tensor([0.8982, 0.6195, 0.1567, ..., 0.8636, 0.4059, 0.3830]), + size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr) +tensor([0.3042, 0.6883, 0.8193, ..., 0.9178, 0.9438, 0.4311]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 5000000 +Density: 0.05 +Time: 10.496035814285278 seconds + +[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19] +[82.21] +16.434854984283447 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21} +[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19, 18.32, 17.88, 17.95, 17.82, 18.37, 18.15, 18.04, 17.93, 18.22, 17.79] +325.225 +16.26125 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21, 'J_1KI': 153.5177171069131, 'W_1KI': 9.340983979093284, 'W_D': 65.94874999999999, 'J_D': 1083.858142644763, 'W_D_1KI': 7.493324622202022, 'J_D_1KI': 0.8514174096354984} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json new file mode 100644 index 0000000..e2fe041 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 282031, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.372447967529297, "TIME_S_1KI": 0.036777687444037345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1003.1455927085877, "W": 73.48, "J_1KI": 3.5568628721969846, "W_1KI": 0.26053873510358794, "W_D": 57.203, "J_D": 780.9327346177101, "W_D_1KI": 0.20282522134091643, "J_D_1KI": 0.0007191593170286829} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output new file mode 100644 index 0000000..e246539 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output @@ -0,0 +1,1414 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05941200256347656} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([9369, 4292, 7681, 931, 2083, 7410, 1936, 8751, 948, + 4175, 1888, 1345, 5560, 8024, 9671, 1813, 200, 3639, + 4209, 3914, 3664, 7493, 1335, 4478, 2357, 7163, 3091, + 1065, 1561, 1003, 6097, 7558, 3015, 3713, 2757, 4724, + 4711, 6411, 2158, 193, 8251, 155, 227, 7018, 2516, + 6344, 9278, 6665, 5922, 3495, 9818, 2312, 5020, 771, + 4972, 1230, 8287, 7235, 5784, 9154, 6363, 9057, 5066, + 5544, 6958, 630, 2095, 1478, 1039, 1263, 2930, 2777, + 7763, 5296, 5286, 2070, 731, 3847, 9033, 1007, 9514, + 5297, 1206, 2620, 5020, 6860, 5221, 4179, 9153, 5412, + 6136, 2995, 6232, 9878, 9074, 9378, 3413, 1935, 7692, + 8374, 9520, 993, 5604, 4102, 1183, 6775, 1244, 8245, + 5932, 1440, 3804, 7398, 4378, 8195, 8257, 8791, 1040, + 7963, 4734, 2450, 6959, 5246, 9222, 1, 7047, 8234, + 866, 6402, 4633, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 0.05941200256347656 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '176731', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.579680919647217} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([1504, 3099, 4004, 1150, 1392, 5460, 1366, 7098, 1310, + 7653, 7132, 7485, 6491, 1708, 1741, 5889, 6633, 4763, + 2335, 7667, 7189, 7712, 8830, 6994, 9528, 3923, 7748, + 9751, 3527, 4817, 1065, 9038, 1439, 7778, 7797, 1760, + 581, 9181, 5233, 2380, 1312, 6119, 1318, 8532, 5773, + 7950, 4559, 535, 3122, 484, 3449, 8220, 8300, 4045, + 9446, 2552, 6931, 9875, 6005, 9524, 8628, 424, 6643, + 755, 101, 4097, 978, 5632, 3675, 2949, 5286, 7265, + 1772, 8623, 6738, 3008, 3529, 8115, 1631, 9342, 745, + 4959, 994, 6574, 6399, 4493, 4340, 4457, 6066, 5468, + 9796, 6503, 4529, 8546, 3580, 1327, 3981, 3795, 190, + 8899, 4487, 1151, 981, 8161, 9891, 7554, 1606, 2191, + 501, 6416, 6764, 6915, 8693, 842, 667, 5088, 2889, + 2340, 4198, 1848, 9366, 7349, 1938, 9093, 4810, 7574, + 4872, 6494, 3389, 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'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '282031', '-ss', '10000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.372447967529297} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 641, 1820, 5085, 7616, 6287, 2843, 3768, 139, 557, + 8943, 8505, 7281, 43, 6135, 7303, 4893, 489, 615, + 6714, 3680, 855, 4855, 479, 9230, 4436, 9603, 1635, + 9190, 9268, 3004, 1125, 8864, 107, 6184, 8970, 8700, + 7610, 2464, 2526, 7595, 3071, 5215, 1177, 6775, 4184, + 7851, 6577, 5571, 7909, 9344, 735, 6183, 9381, 8186, + 7299, 7523, 9047, 1302, 3301, 6829, 1465, 8532, 8991, + 1047, 5588, 9587, 3024, 6187, 7730, 4690, 6326, 2702, + 2537, 5158, 9461, 7448, 9578, 6012, 7028, 226, 6053, + 1967, 8146, 5831, 6774, 2244, 6191, 9992, 2390, 9133, + 8890, 766, 5014, 4790, 2155, 4691, 2161, 5599, 1756, + 7675, 496, 9605, 711, 5336, 9031, 2531, 2338, 9491, + 3768, 7092, 9040, 599, 4662, 8394, 522, 7316, 1506, + 525, 4754, 5479, 3359, 6765, 8131, 5941, 6009, 2305, + 1065, 3240, 5116, 987, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.372447967529297 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 1000, 1000, 1000]), + col_indices=tensor([ 641, 1820, 5085, 7616, 6287, 2843, 3768, 139, 557, + 8943, 8505, 7281, 43, 6135, 7303, 4893, 489, 615, + 6714, 3680, 855, 4855, 479, 9230, 4436, 9603, 1635, + 9190, 9268, 3004, 1125, 8864, 107, 6184, 8970, 8700, + 7610, 2464, 2526, 7595, 3071, 5215, 1177, 6775, 4184, + 7851, 6577, 5571, 7909, 9344, 735, 6183, 9381, 8186, + 7299, 7523, 9047, 1302, 3301, 6829, 1465, 8532, 8991, + 1047, 5588, 9587, 3024, 6187, 7730, 4690, 6326, 2702, + 2537, 5158, 9461, 7448, 9578, 6012, 7028, 226, 6053, + 1967, 8146, 5831, 6774, 2244, 6191, 9992, 2390, 9133, + 8890, 766, 5014, 4790, 2155, 4691, 2161, 5599, 1756, + 7675, 496, 9605, 711, 5336, 9031, 2531, 2338, 9491, + 3768, 7092, 9040, 599, 4662, 8394, 522, 7316, 1506, + 525, 4754, 5479, 3359, 6765, 8131, 5941, 6009, 2305, + 1065, 3240, 5116, 987, 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+Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 1000 +Density: 1e-05 +Time: 10.372447967529297 seconds + +[18.31, 17.96, 18.02, 17.94, 18.39, 17.81, 17.97, 18.58, 17.92, 17.98] +[73.48] +13.651954174041748 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 282031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.372447967529297, 'TIME_S_1KI': 0.036777687444037345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1003.1455927085877, 'W': 73.48} +[18.31, 17.96, 18.02, 17.94, 18.39, 17.81, 17.97, 18.58, 17.92, 17.98, 18.3, 18.16, 17.88, 17.97, 17.91, 17.97, 18.81, 17.78, 18.15, 18.05] +325.53999999999996 +16.276999999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 282031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.372447967529297, 'TIME_S_1KI': 0.036777687444037345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1003.1455927085877, 'W': 73.48, 'J_1KI': 3.5568628721969846, 'W_1KI': 0.26053873510358794, 'W_D': 57.203, 'J_D': 780.9327346177101, 'W_D_1KI': 0.20282522134091643, 'J_D_1KI': 0.0007191593170286829} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json new file mode 100644 index 0000000..49601d0 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8372, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.861924409866333, "TIME_S_1KI": 1.297410942411172, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1310.9924754476547, "W": 87.74, "J_1KI": 156.5925078174456, "W_1KI": 10.480172001911132, "W_D": 71.52799999999999, "J_D": 1068.7562090702056, "W_D_1KI": 8.543717152412803, "J_D_1KI": 1.0205108877702822} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output new file mode 100644 index 0000000..5b5d205 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2540500164031982} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 3, 6, ..., 2499994, + 2499996, 2500000]), + col_indices=tensor([ 37595, 347043, 415637, ..., 145391, 181131, + 323148]), + values=tensor([0.9304, 0.5156, 0.8153, ..., 0.0582, 0.6116, 0.3872]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.4551, 0.3395, 0.9990, ..., 0.2154, 0.7020, 0.1344]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 1.2540500164031982 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8372', '-ss', '500000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.861924409866333} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 10, ..., 2499992, + 2499994, 2500000]), + col_indices=tensor([140767, 212572, 418184, ..., 257460, 329048, + 350732]), + values=tensor([0.1302, 0.7593, 0.7287, ..., 0.1348, 0.8551, 0.2122]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3362, 0.7821, 0.5665, ..., 0.5113, 0.4644, 0.7174]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.861924409866333 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 10, ..., 2499992, + 2499994, 2500000]), + col_indices=tensor([140767, 212572, 418184, ..., 257460, 329048, + 350732]), + values=tensor([0.1302, 0.7593, 0.7287, ..., 0.1348, 0.8551, 0.2122]), + size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.3362, 0.7821, 0.5665, ..., 0.5113, 0.4644, 0.7174]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 2500000 +Density: 1e-05 +Time: 10.861924409866333 seconds + +[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77] +[87.74] +14.941787958145142 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74} +[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77, 18.44, 18.07, 17.99, 17.91, 18.26, 18.15, 17.93, 17.77, 18.22, 18.2] +324.24 +16.212 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74, 'J_1KI': 156.5925078174456, 'W_1KI': 10.480172001911132, 'W_D': 71.52799999999999, 'J_D': 1068.7562090702056, 'W_D_1KI': 8.543717152412803, 'J_D_1KI': 1.0205108877702822} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json new file mode 100644 index 0000000..bafcc4c --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78280, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.048285484313965, "TIME_S_1KI": 0.12836338125081712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1146.701253862381, "W": 82.44, "J_1KI": 14.648713002840841, "W_1KI": 1.053142565150741, "W_D": 66.134, "J_D": 919.8925366683006, "W_D_1KI": 0.8448390393459376, "J_D_1KI": 0.010792527329406458} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output new file mode 100644 index 0000000..d22c5fa --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1482532024383545} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2, 8, ..., 249991, 249997, + 250000]), + col_indices=tensor([11188, 48325, 9835, ..., 16403, 16442, 24121]), + values=tensor([0.5273, 0.3289, 0.0892, ..., 0.0153, 0.8132, 0.4919]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.4620, 0.4376, 0.8938, ..., 0.9801, 0.7388, 0.7080]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 0.1482532024383545 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70824', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.499845743179321} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 5, 12, ..., 249991, 249995, + 250000]), + col_indices=tensor([ 9700, 17110, 17880, ..., 40636, 42079, 45237]), + values=tensor([0.5791, 0.9394, 0.7161, ..., 0.4792, 0.4698, 0.8140]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.2158, 0.6632, 0.3616, ..., 0.9096, 0.8324, 0.6259]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 9.499845743179321 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78280', '-ss', '50000', '-sd', '0.0001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.048285484313965} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 11, ..., 249990, 249995, + 250000]), + col_indices=tensor([ 1806, 10529, 23120, ..., 17166, 35800, 40447]), + values=tensor([0.3161, 0.7150, 0.6424, ..., 0.5169, 0.8858, 0.3422]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5244, 0.0456, 0.6715, ..., 0.9006, 0.5240, 0.6616]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.048285484313965 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 6, 11, ..., 249990, 249995, + 250000]), + col_indices=tensor([ 1806, 10529, 23120, ..., 17166, 35800, 40447]), + values=tensor([0.3161, 0.7150, 0.6424, ..., 0.5169, 0.8858, 0.3422]), + size=(50000, 50000), nnz=250000, layout=torch.sparse_csr) +tensor([0.5244, 0.0456, 0.6715, ..., 0.9006, 0.5240, 0.6616]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 250000 +Density: 0.0001 +Time: 10.048285484313965 seconds + +[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16] +[82.44] +13.909525156021118 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44} +[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16, 18.33, 19.12, 18.1, 18.07, 17.96, 17.95, 18.08, 18.08, 18.21, 18.0] +326.12 +16.306 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44, 'J_1KI': 14.648713002840841, 'W_1KI': 1.053142565150741, 'W_D': 66.134, 'J_D': 919.8925366683006, 'W_D_1KI': 0.8448390393459376, 'J_D_1KI': 0.010792527329406458} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json new file mode 100644 index 0000000..9c50222 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17475, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.743166208267212, "TIME_S_1KI": 0.6147734597005557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2754010248184, "W": 87.46, "J_1KI": 74.12162523747172, "W_1KI": 5.004864091559369, "W_D": 70.91274999999999, "J_D": 1050.2119905559418, "W_D_1KI": 4.057954220314734, "J_D_1KI": 0.2322148337805284} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output new file mode 100644 index 0000000..95a3055 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6008265018463135} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 42, 90, ..., 2499902, + 2499947, 2500000]), + col_indices=tensor([ 1236, 2335, 2455, ..., 44227, 44372, 44789]), + values=tensor([0.4453, 0.9405, 0.8001, ..., 0.3243, 0.3638, 0.0708]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.5116, 0.1335, 0.5143, ..., 0.8718, 0.6117, 0.3765]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 0.6008265018463135 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17475', '-ss', '50000', '-sd', '0.001'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.743166208267212} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 107, ..., 2499873, + 2499945, 2500000]), + col_indices=tensor([ 1803, 2168, 2288, ..., 48770, 49205, 49605]), + values=tensor([0.1814, 0.9281, 0.5481, ..., 0.9692, 0.2397, 0.8106]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8810, 0.5797, 0.1795, ..., 0.7146, 0.8135, 0.6945]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.743166208267212 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 107, ..., 2499873, + 2499945, 2500000]), + col_indices=tensor([ 1803, 2168, 2288, ..., 48770, 49205, 49605]), + values=tensor([0.1814, 0.9281, 0.5481, ..., 0.9692, 0.2397, 0.8106]), + size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8810, 0.5797, 0.1795, ..., 0.7146, 0.8135, 0.6945]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 2500000 +Density: 0.001 +Time: 10.743166208267212 seconds + +[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61] +[87.46] +14.809917688369751 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46} +[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61, 18.37, 18.2, 17.93, 17.83, 17.81, 18.07, 18.0, 17.85, 21.15, 18.15] +330.94500000000005 +16.547250000000002 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46, 'J_1KI': 74.12162523747172, 'W_1KI': 5.004864091559369, 'W_D': 70.91274999999999, 'J_D': 1050.2119905559418, 'W_D_1KI': 4.057954220314734, 'J_D_1KI': 0.2322148337805284} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json new file mode 100644 index 0000000..598f4f1 --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 112560, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.602921962738037, "TIME_S_1KI": 0.0941979563143038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1061.1344814062118, "W": 76.1, "J_1KI": 9.427278619458171, "W_1KI": 0.6760838663823738, "W_D": 59.91175, "J_D": 835.4063569827675, "W_D_1KI": 0.532265014214641, "J_D_1KI": 0.004728722585417919} diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output new file mode 100644 index 0000000..fbbef6c --- /dev/null +++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output @@ -0,0 +1,81 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.10953974723815918} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([20679, 38088, 21453, ..., 14604, 22112, 37567]), + values=tensor([0.0203, 0.9911, 0.7304, ..., 0.1348, 0.2520, 0.4128]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.2000, 0.8382, 0.5478, ..., 0.6017, 0.0874, 0.6263]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 0.10953974723815918 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '95855', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.94165301322937} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([31700, 16272, 20084, ..., 46363, 9221, 39878]), + values=tensor([0.3577, 0.1970, 0.2573, ..., 0.9498, 0.8667, 0.9638]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.7704, 0.6386, 0.5878, ..., 0.7750, 0.3511, 0.4334]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 8.94165301322937 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '112560', '-ss', '50000', '-sd', '1e-05'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.602921962738037} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([11228, 2410, 48293, ..., 48555, 29403, 27641]), + values=tensor([0.9662, 0.4123, 0.9370, ..., 0.4524, 0.0602, 0.8924]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4903, 0.0715, 0.0009, ..., 0.3750, 0.8526, 0.7709]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.602921962738037 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 0, 0, ..., 25000, 25000, 25000]), + col_indices=tensor([11228, 2410, 48293, ..., 48555, 29403, 27641]), + values=tensor([0.9662, 0.4123, 0.9370, ..., 0.4524, 0.0602, 0.8924]), + size=(50000, 50000), nnz=25000, layout=torch.sparse_csr) +tensor([0.4903, 0.0715, 0.0009, ..., 0.3750, 0.8526, 0.7709]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000 +Density: 1e-05 +Time: 10.602921962738037 seconds + +[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99] +[76.1] +13.94394850730896 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1} +[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99, 18.3, 17.89, 18.04, 18.07, 17.99, 17.83, 18.01, 18.29, 17.73, 18.17] +323.765 +16.18825 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1, 'J_1KI': 9.427278619458171, 'W_1KI': 0.6760838663823738, 'W_D': 59.91175, 'J_D': 835.4063569827675, 'W_D_1KI': 0.532265014214641, 'J_D_1KI': 0.004728722585417919} diff --git a/pytorch/synthetic_densities b/pytorch/synthetic_densities new file mode 100644 index 0000000..fcd06c2 --- /dev/null +++ b/pytorch/synthetic_densities @@ -0,0 +1,6 @@ +0.00001 +0.0001 +0.001 +0.01 +0.05 +0.1 diff --git a/pytorch/synthetic_sizes b/pytorch/synthetic_sizes new file mode 100644 index 0000000..fc7c6bc --- /dev/null +++ b/pytorch/synthetic_sizes @@ -0,0 +1,5 @@ +10000 +50000 +100000 +500000 +1000000