From a339f2d2fe038e006c6603932b7e644510d11a15 Mon Sep 17 00:00:00 2001 From: cephi Date: Tue, 17 Dec 2024 14:27:55 -0500 Subject: [PATCH] MOAR --- pytorch/batch.py | 2 +- ...a_16_csr_10_10_10_synthetic_10000_0.1.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.1.output | 44 ++++---- ...a_16_csr_10_10_10_synthetic_10000_0.2.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.2.output | 44 ++++---- ...a_16_csr_10_10_10_synthetic_10000_0.3.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.3.output | 44 ++++---- ...ra_16_csr_10_10_10_synthetic_5000_0.1.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.1.output | 64 +++++++---- ...ra_16_csr_10_10_10_synthetic_5000_0.2.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.2.output | 44 ++++---- ...ra_16_csr_10_10_10_synthetic_5000_0.3.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.3.output | 44 ++++---- ...ra_16_csr_10_10_10_synthetic_5000_0.4.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.4.output | 45 ++++++++ ...ra_16_csr_10_10_10_synthetic_5000_0.5.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.5.output | 45 ++++++++ ...p_16_csr_10_10_10_synthetic_10000_0.1.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.1.output | 58 +++++----- ...p_16_csr_10_10_10_synthetic_10000_0.2.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.2.output | 58 +++++----- ...p_16_csr_10_10_10_synthetic_10000_0.3.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.3.output | 58 +++++----- ...3p_16_csr_10_10_10_synthetic_5000_0.1.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.1.output | 74 ++++++------ ...3p_16_csr_10_10_10_synthetic_5000_0.2.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.2.output | 94 ++++++---------- ...3p_16_csr_10_10_10_synthetic_5000_0.3.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.3.output | 74 ++++++------ ...3p_16_csr_10_10_10_synthetic_5000_0.4.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.4.output | 105 ++++++++++++++++++ ...3p_16_csr_10_10_10_synthetic_5000_0.5.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.5.output | 65 +++++++++++ ...6_16_csr_10_10_10_synthetic_10000_0.1.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.1.output | 58 +++++----- ...6_16_csr_10_10_10_synthetic_10000_0.2.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.2.output | 58 +++++----- ...6_16_csr_10_10_10_synthetic_10000_0.3.json | 2 +- ...16_csr_10_10_10_synthetic_10000_0.3.output | 42 +++---- ...16_16_csr_10_10_10_synthetic_5000_0.1.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.1.output | 58 +++++----- ...16_16_csr_10_10_10_synthetic_5000_0.2.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.2.output | 58 +++++----- ...16_16_csr_10_10_10_synthetic_5000_0.3.json | 2 +- ..._16_csr_10_10_10_synthetic_5000_0.3.output | 58 +++++----- ...16_16_csr_10_10_10_synthetic_5000_0.4.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.4.output | 85 ++++++++++++++ ...16_16_csr_10_10_10_synthetic_5000_0.5.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.5.output | 85 ++++++++++++++ pytorch/synthetic_densities | 3 + 50 files changed, 974 insertions(+), 535 deletions(-) create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output diff --git a/pytorch/batch.py b/pytorch/batch.py index f6726a5..42211cf 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 <= 20000000]) + if size ** 2 * density <= 30000000]) #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_10000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json index ce0e17e..f51fe75 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json @@ -1 +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": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 215.95656299591064, "TIME_S_1KI": 215.95656299591064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4458.621953725812, "W": 20.314604170190528, "J_1KI": 4458.621953725812, "W_1KI": 20.314604170190528, "W_D": 5.2026041701905275, "J_D": 1141.8605538864108, "W_D_1KI": 5.2026041701905275, "J_D_1KI": 5.2026041701905275} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 22.49751091003418, "TIME_S_1KI": 224.9751091003418, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 714.621188735962, "W": 24.29855166830563, "J_1KI": 7146.211887359621, "W_1KI": 242.98551668305632, "W_D": 5.645551668305629, "J_D": 166.03585676002507, "W_D_1KI": 56.45551668305629, "J_D_1KI": 564.5551668305628} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output index 3241974..09e73ec 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output @@ -1,14 +1,14 @@ -['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 0.1 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 215.95656299591064} +['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 100 -ss 10000 -sd 0.1 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 22.49751091003418} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 987, 1996, ..., 9998019, - 9999013, 10000000]), - col_indices=tensor([ 25, 29, 35, ..., 9989, 9993, 9996]), - values=tensor([0.8438, 0.2270, 0.6737, ..., 0.5218, 0.6879, 0.5182]), +tensor(crow_indices=tensor([ 0, 1002, 1924, ..., 9998003, + 9998995, 10000000]), + col_indices=tensor([ 0, 2, 31, ..., 9969, 9996, 9997]), + values=tensor([0.9177, 0.7034, 0.7745, ..., 0.5598, 0.0709, 0.5319]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.6846, 0.7185, 0.0206, ..., 0.2576, 0.7966, 0.0945]) +tensor([0.7893, 0.1843, 0.8169, ..., 0.5734, 0.3496, 0.5102]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,16 +16,16 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 215.95656299591064 seconds +Time: 22.49751091003418 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, 987, 1996, ..., 9998019, - 9999013, 10000000]), - col_indices=tensor([ 25, 29, 35, ..., 9989, 9993, 9996]), - values=tensor([0.8438, 0.2270, 0.6737, ..., 0.5218, 0.6879, 0.5182]), +tensor(crow_indices=tensor([ 0, 1002, 1924, ..., 9998003, + 9998995, 10000000]), + col_indices=tensor([ 0, 2, 31, ..., 9969, 9996, 9997]), + values=tensor([0.9177, 0.7034, 0.7745, ..., 0.5598, 0.0709, 0.5319]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.6846, 0.7185, 0.0206, ..., 0.2576, 0.7966, 0.0945]) +tensor([0.7893, 0.1843, 0.8169, ..., 0.5734, 0.3496, 0.5102]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -33,13 +33,13 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 215.95656299591064 seconds +Time: 22.49751091003418 seconds -[16.56, 16.44, 16.64, 16.6, 16.56, 16.92, 16.84, 16.92, 16.96, 16.72] -[16.52, 16.4, 17.44, 17.44, 19.24, 21.04, 24.24, 25.48, 25.32, 24.52, 22.6, 22.16, 20.6, 20.6, 21.08, 21.08, 20.92, 21.04, 20.88, 20.64, 20.6, 20.72, 20.52, 20.76, 20.76, 20.52, 20.44, 20.28, 20.28, 20.28, 20.44, 20.68, 20.72, 20.96, 20.68, 20.72, 20.48, 20.4, 20.2, 20.48, 20.48, 20.24, 20.28, 20.28, 20.2, 20.04, 20.16, 20.28, 20.4, 20.56, 20.6, 20.52, 20.56, 20.56, 20.6, 20.64, 20.72, 20.52, 20.4, 20.28, 20.44, 20.56, 20.52, 20.6, 20.56, 20.56, 20.4, 20.48, 20.28, 20.24, 20.36, 20.44, 20.48, 20.52, 20.36, 20.44, 20.36, 20.36, 20.32, 20.52, 20.52, 20.64, 20.56, 20.52, 20.56, 20.64, 20.36, 20.64, 20.64, 20.72, 20.72, 20.64, 20.8, 20.52, 20.36, 20.32, 20.44, 20.4, 20.56, 20.8, 21.08, 20.84, 20.84, 20.84, 20.76, 20.4, 20.36, 20.48, 20.6, 20.56, 20.76, 20.64, 20.68, 20.72, 20.72, 20.56, 20.56, 20.56, 20.8, 20.6, 20.56, 20.44, 20.44, 20.28, 20.48, 20.56, 20.72, 20.72, 20.56, 20.72, 20.76, 20.68, 20.72, 20.6, 20.64, 20.76, 20.88, 21.08, 20.96, 20.96, 20.72, 20.64, 20.52, 20.44, 20.32, 20.48, 20.6, 20.56, 20.6, 20.84, 20.68, 20.68, 20.64, 20.64, 20.6, 20.44, 20.28, 20.4, 20.16, 20.52, 20.76, 20.92, 20.96, 20.68, 20.68, 20.64, 20.24, 20.16, 20.36, 20.56, 20.6, 20.72, 20.48, 20.48, 20.4, 20.24, 20.24, 20.32, 20.44, 20.28, 20.64, 20.8, 20.88, 21.0, 21.16, 20.76, 20.68, 20.4, 20.4, 20.48, 20.48, 20.52, 20.6, 20.56, 20.32, 20.2, 20.04, 20.04, 20.16, 20.36, 20.4, 20.4, 20.4, 20.4, 20.28, 20.36, 20.4, 20.52, 20.8, 21.04, 21.2, 21.04, 20.72, 20.72, 20.6, 20.6, 20.52] -219.47865271568298 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 215.95656299591064, 'TIME_S_1KI': 215.95656299591064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4458.621953725812, 'W': 20.314604170190528} -[16.56, 16.44, 16.64, 16.6, 16.56, 16.92, 16.84, 16.92, 16.96, 16.72, 16.8, 17.08, 16.92, 17.2, 17.12, 16.88, 16.68, 16.52, 16.6, 16.64] -302.24 -15.112 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 215.95656299591064, 'TIME_S_1KI': 215.95656299591064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4458.621953725812, 'W': 20.314604170190528, 'J_1KI': 4458.621953725812, 'W_1KI': 20.314604170190528, 'W_D': 5.2026041701905275, 'J_D': 1141.8605538864108, 'W_D_1KI': 5.2026041701905275, 'J_D_1KI': 5.2026041701905275} +[20.52, 20.56, 20.88, 20.92, 21.04, 21.0, 20.96, 20.72, 20.72, 20.72] +[20.52, 20.44, 20.56, 24.12, 26.0, 27.96, 31.12, 31.04, 30.6, 29.2, 26.72, 25.56, 25.56, 24.04, 24.24, 24.24, 24.04, 24.2, 24.16, 24.08, 24.12, 24.28, 24.16, 24.28, 24.28, 24.28, 24.36, 24.48, 24.44] +29.410032272338867 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 22.49751091003418, 'TIME_S_1KI': 224.9751091003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 714.621188735962, 'W': 24.29855166830563} +[20.52, 20.56, 20.88, 20.92, 21.04, 21.0, 20.96, 20.72, 20.72, 20.72, 20.32, 20.68, 20.68, 20.64, 20.64, 20.6, 20.6, 20.68, 20.64, 20.64] +373.06000000000006 +18.653000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 22.49751091003418, 'TIME_S_1KI': 224.9751091003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 714.621188735962, 'W': 24.29855166830563, 'J_1KI': 7146.211887359621, 'W_1KI': 242.98551668305632, 'W_D': 5.645551668305629, 'J_D': 166.03585676002507, 'W_D_1KI': 56.45551668305629, 'J_D_1KI': 564.5551668305628} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json index fffbd6e..f0f567c 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json @@ -1 +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": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4422960281372, "TIME_S_1KI": 424.4422960281372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10577.914468402869, "W": 24.381232025818772, "J_1KI": 10577.914468402869, "W_1KI": 24.381232025818772, "W_D": 5.810232025818774, "J_D": 2520.797035424717, "W_D_1KI": 5.810232025818774, "J_D_1KI": 5.810232025818774} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.36744213104248, "TIME_S_1KI": 423.6744213104248, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1267.0327228164674, "W": 24.507310404611225, "J_1KI": 12670.327228164673, "W_1KI": 245.07310404611223, "W_D": 5.971310404611227, "J_D": 308.71791134262116, "W_D_1KI": 59.71310404611227, "J_D_1KI": 597.1310404611228} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output index 409ed65..7ca9c1e 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output @@ -1,14 +1,14 @@ -['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 0.2 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4422960281372} +['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 100 -ss 10000 -sd 0.2 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.36744213104248} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1997, 3940, ..., 19995978, - 19998013, 20000000]), - col_indices=tensor([ 1, 6, 9, ..., 9994, 9996, 9997]), - values=tensor([0.5415, 0.5931, 0.4382, ..., 0.2191, 0.0907, 0.5464]), +tensor(crow_indices=tensor([ 0, 1958, 3991, ..., 19996077, + 19998030, 20000000]), + col_indices=tensor([ 7, 14, 15, ..., 9992, 9994, 9996]), + values=tensor([0.5268, 0.0225, 0.1392, ..., 0.0209, 0.3714, 0.4352]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.6696, 0.2101, 0.2330, ..., 0.2876, 0.3503, 0.9145]) +tensor([0.3357, 0.5966, 0.1485, ..., 0.1370, 0.3281, 0.6890]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,16 +16,16 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 424.4422960281372 seconds +Time: 42.36744213104248 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, 1997, 3940, ..., 19995978, - 19998013, 20000000]), - col_indices=tensor([ 1, 6, 9, ..., 9994, 9996, 9997]), - values=tensor([0.5415, 0.5931, 0.4382, ..., 0.2191, 0.0907, 0.5464]), +tensor(crow_indices=tensor([ 0, 1958, 3991, ..., 19996077, + 19998030, 20000000]), + col_indices=tensor([ 7, 14, 15, ..., 9992, 9994, 9996]), + values=tensor([0.5268, 0.0225, 0.1392, ..., 0.0209, 0.3714, 0.4352]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.6696, 0.2101, 0.2330, ..., 0.2876, 0.3503, 0.9145]) +tensor([0.3357, 0.5966, 0.1485, ..., 0.1370, 0.3281, 0.6890]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -33,13 +33,13 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 424.4422960281372 seconds +Time: 42.36744213104248 seconds -[20.28, 20.2, 20.08, 20.08, 20.28, 20.44, 21.04, 20.84, 20.88, 20.76] -[20.6, 20.28, 23.28, 24.56, 27.04, 28.72, 32.56, 32.56, 30.16, 30.32, 30.0, 27.84, 26.92, 26.16, 25.36, 24.2, 24.52, 24.6, 24.56, 24.56, 24.44, 24.36, 24.2, 24.16, 24.32, 24.52, 24.8, 24.88, 24.84, 24.96, 24.88, 24.88, 24.88, 24.68, 24.56, 24.52, 24.56, 24.52, 24.56, 24.6, 24.32, 24.28, 24.24, 24.36, 24.36, 24.44, 24.44, 24.36, 24.36, 24.36, 24.36, 24.4, 24.4, 24.56, 24.64, 24.68, 24.68, 24.92, 24.88, 24.92, 24.88, 24.8, 24.56, 24.56, 24.6, 24.4, 24.32, 24.44, 24.4, 24.4, 24.4, 24.52, 24.6, 24.64, 24.64, 24.36, 24.44, 24.32, 24.36, 24.32, 24.36, 24.36, 24.28, 24.36, 24.24, 24.4, 24.52, 24.72, 24.76, 24.68, 24.68, 24.24, 24.44, 24.32, 24.32, 24.48, 24.52, 24.4, 24.32, 24.24, 24.2, 24.4, 24.56, 24.64, 24.84, 24.68, 24.68, 24.68, 24.6, 24.6, 24.64, 25.0, 24.76, 24.56, 24.52, 24.4, 24.44, 24.56, 24.56, 24.72, 25.0, 24.92, 24.88, 25.04, 24.68, 24.52, 24.52, 24.32, 24.16, 24.08, 24.12, 24.12, 24.32, 24.52, 24.64, 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24.28, 24.28] -433.8547968864441 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4422960281372, 'TIME_S_1KI': 424.4422960281372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10577.914468402869, 'W': 24.381232025818772} -[20.28, 20.2, 20.08, 20.08, 20.28, 20.44, 21.04, 20.84, 20.88, 20.76, 20.28, 20.6, 20.88, 21.24, 21.28, 21.28, 20.88, 20.36, 20.24, 20.32] -371.41999999999996 -18.570999999999998 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4422960281372, 'TIME_S_1KI': 424.4422960281372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10577.914468402869, 'W': 24.381232025818772, 'J_1KI': 10577.914468402869, 'W_1KI': 24.381232025818772, 'W_D': 5.810232025818774, 'J_D': 2520.797035424717, 'W_D_1KI': 5.810232025818774, 'J_D_1KI': 5.810232025818774} +[20.84, 20.72, 20.6, 20.6, 20.4, 20.36, 20.4, 20.32, 20.32, 20.4] +[20.44, 20.6, 20.96, 23.28, 24.24, 25.88, 29.52, 30.04, 29.56, 29.8, 30.12, 27.4, 27.4, 26.84, 25.8, 24.52, 24.6, 24.56, 24.52, 24.48, 24.4, 24.48, 24.4, 24.52, 24.52, 24.68, 24.84, 24.72, 24.8, 24.72, 24.68, 24.52, 24.56, 24.52, 24.4, 24.32, 24.48, 24.48, 24.2, 24.28, 24.4, 24.12, 24.24, 24.16, 24.32, 24.44, 24.36, 24.32, 24.36, 24.36, 24.24] +51.70019483566284 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.36744213104248, 'TIME_S_1KI': 423.6744213104248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.0327228164674, 'W': 24.507310404611225} +[20.84, 20.72, 20.6, 20.6, 20.4, 20.36, 20.4, 20.32, 20.32, 20.4, 20.56, 20.36, 20.44, 20.72, 20.76, 20.8, 20.96, 20.92, 20.76, 20.76] +370.71999999999997 +18.535999999999998 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.36744213104248, 'TIME_S_1KI': 423.6744213104248, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.0327228164674, 'W': 24.507310404611225, 'J_1KI': 12670.327228164673, 'W_1KI': 245.07310404611223, 'W_D': 5.971310404611227, 'J_D': 308.71791134262116, 'W_D_1KI': 59.71310404611227, 'J_D_1KI': 597.1310404611228} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json index 28452bf..db63233 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json @@ -1 +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": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 644.013694524765, "TIME_S_1KI": 644.013694524765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 15694.744610672022, "W": 24.244425924397902, "J_1KI": 15694.744610672022, "W_1KI": 24.244425924397902, "W_D": 5.900425924397901, "J_D": 3819.668828884149, "W_D_1KI": 5.900425924397901, "J_D_1KI": 5.900425924397901} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 64.48762941360474, "TIME_S_1KI": 644.8762941360474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.6232081508633, "W": 24.560001352289554, "J_1KI": 18666.23208150863, "W_1KI": 245.60001352289555, "W_D": 6.134001352289552, "J_D": 466.19986370420406, "W_D_1KI": 61.34001352289552, "J_D_1KI": 613.4001352289552} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output index 90f3629..fdb7cdc 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output @@ -1,14 +1,14 @@ -['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 0.3 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 644.013694524765} +['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 100 -ss 10000 -sd 0.3 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 64.48762941360474} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 3023, 6063, ..., 29994048, - 29997006, 30000000]), - col_indices=tensor([ 0, 6, 7, ..., 9989, 9991, 9992]), - values=tensor([0.2093, 0.3818, 0.7570, ..., 0.3871, 0.5537, 0.9186]), +tensor(crow_indices=tensor([ 0, 3018, 6012, ..., 29994075, + 29997046, 30000000]), + col_indices=tensor([ 12, 13, 14, ..., 9975, 9986, 9989]), + values=tensor([0.7172, 0.9331, 0.0150, ..., 0.2187, 0.8326, 0.8808]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882]) +tensor([0.9528, 0.5520, 0.9475, ..., 0.9876, 0.0053, 0.6635]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,16 +16,16 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 644.013694524765 seconds +Time: 64.48762941360474 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, 3023, 6063, ..., 29994048, - 29997006, 30000000]), - col_indices=tensor([ 0, 6, 7, ..., 9989, 9991, 9992]), - values=tensor([0.2093, 0.3818, 0.7570, ..., 0.3871, 0.5537, 0.9186]), +tensor(crow_indices=tensor([ 0, 3018, 6012, ..., 29994075, + 29997046, 30000000]), + col_indices=tensor([ 12, 13, 14, ..., 9975, 9986, 9989]), + values=tensor([0.7172, 0.9331, 0.0150, ..., 0.2187, 0.8326, 0.8808]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882]) +tensor([0.9528, 0.5520, 0.9475, ..., 0.9876, 0.0053, 0.6635]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -33,13 +33,13 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 644.013694524765 seconds +Time: 64.48762941360474 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-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 644.013694524765, 'TIME_S_1KI': 644.013694524765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15694.744610672022, 'W': 24.244425924397902, 'J_1KI': 15694.744610672022, 'W_1KI': 24.244425924397902, 'W_D': 5.900425924397901, 'J_D': 3819.668828884149, 'W_D_1KI': 5.900425924397901, 'J_D_1KI': 5.900425924397901} +[20.6, 20.44, 20.32, 20.24, 20.36, 20.64, 20.68, 20.8, 20.92, 20.92] +[20.68, 20.44, 20.2, 24.28, 25.52, 26.88, 28.28, 30.32, 31.12, 31.36, 32.28, 31.6, 29.96, 28.72, 28.72, 27.68, 26.76, 25.88, 24.68, 25.0, 25.04, 25.08, 24.72, 24.36, 24.4, 24.2, 24.2, 24.16, 24.36, 24.32, 24.36, 24.28, 24.28, 24.32, 24.44, 24.56, 24.52, 24.84, 24.84, 24.84, 25.0, 24.92, 24.92, 24.6, 24.52, 24.48, 24.4, 24.32, 24.4, 24.0, 24.24, 24.24, 24.32, 24.4, 24.6, 24.72, 24.92, 24.72, 24.64, 24.64, 24.8, 24.88, 24.8, 24.8, 24.92, 24.8, 24.52, 24.44, 24.24, 24.16, 24.16, 24.12, 24.16, 24.32, 24.6] +76.00256943702698 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 64.48762941360474, 'TIME_S_1KI': 644.8762941360474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.6232081508633, 'W': 24.560001352289554} +[20.6, 20.44, 20.32, 20.24, 20.36, 20.64, 20.68, 20.8, 20.92, 20.92, 20.28, 20.52, 20.52, 20.56, 20.2, 20.36, 20.4, 20.2, 20.28, 20.36] +368.52000000000004 +18.426000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 64.48762941360474, 'TIME_S_1KI': 644.8762941360474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.6232081508633, 'W': 24.560001352289554, 'J_1KI': 18666.23208150863, 'W_1KI': 245.60001352289555, 'W_D': 6.134001352289552, 'J_D': 466.19986370420406, 'W_D_1KI': 61.34001352289552, 'J_D_1KI': 613.4001352289552} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json index 3a570df..31e2013 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.914990186691284, "TIME_S_1KI": 53.914990186691284, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1144.4719800853732, "W": 20.188888585576834, "J_1KI": 1144.4719800853732, "W_1KI": 20.188888585576834, "W_D": 5.141888585576837, "J_D": 291.48446611952824, "W_D_1KI": 5.141888585576837, "J_D_1KI": 5.141888585576837} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 199, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.763426303863525, "TIME_S_1KI": 54.08756936614837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 349.11062700271606, "W": 22.846498625739535, "J_1KI": 1754.3247588076183, "W_1KI": 114.80652575748509, "W_D": 4.138498625739533, "J_D": 63.23918048667905, "W_D_1KI": 20.79647550622881, "J_D_1KI": 104.50490204135079} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output index aab9423..8549e52 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output @@ -1,14 +1,14 @@ -['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 5000 -sd 0.1 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.914990186691284} +['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 100 -ss 5000 -sd 0.1 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.264772415161133} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 478, 982, ..., 2498978, - 2499487, 2500000]), - col_indices=tensor([ 3, 18, 24, ..., 4984, 4986, 4997]), - values=tensor([0.0150, 0.0039, 0.1247, ..., 0.8538, 0.3013, 0.1357]), +tensor(crow_indices=tensor([ 0, 501, 994, ..., 2498989, + 2499501, 2500000]), + col_indices=tensor([ 0, 2, 18, ..., 4969, 4994, 4999]), + values=tensor([0.5742, 0.5865, 0.7760, ..., 0.1661, 0.0806, 0.2598]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.4451, 0.4302, 0.3190, ..., 0.9031, 0.3775, 0.0047]) +tensor([0.0961, 0.5757, 0.9033, ..., 0.7471, 0.5676, 0.5058]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,16 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 53.914990186691284 seconds +Time: 5.264772415161133 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 199 -ss 5000 -sd 0.1 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.763426303863525} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 478, 982, ..., 2498978, - 2499487, 2500000]), - col_indices=tensor([ 3, 18, 24, ..., 4984, 4986, 4997]), - values=tensor([0.0150, 0.0039, 0.1247, ..., 0.8538, 0.3013, 0.1357]), +tensor(crow_indices=tensor([ 0, 497, 1003, ..., 2498980, + 2499516, 2500000]), + col_indices=tensor([ 3, 16, 28, ..., 4972, 4988, 4998]), + values=tensor([0.4080, 0.5007, 0.4190, ..., 0.3549, 0.8542, 0.4591]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.4451, 0.4302, 0.3190, ..., 0.9031, 0.3775, 0.0047]) +tensor([0.8009, 0.9770, 0.1809, ..., 0.4100, 0.2770, 0.6242]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -33,13 +36,30 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 53.914990186691284 seconds +Time: 10.763426303863525 seconds -[16.52, 16.4, 16.4, 16.56, 16.36, 16.52, 16.8, 16.56, 16.56, 16.76] -[16.48, 16.28, 19.4, 20.36, 22.04, 22.04, 24.88, 25.48, 22.92, 22.96, 20.08, 20.12, 20.12, 20.04, 19.92, 20.0, 20.24, 20.32, 20.32, 20.4, 20.4, 20.04, 20.08, 20.4, 20.48, 20.68, 20.96, 20.6, 20.48, 20.44, 20.44, 20.12, 20.08, 20.2, 19.96, 20.0, 20.32, 20.44, 20.44, 20.48, 20.64, 20.44, 20.44, 20.56, 20.76, 20.64, 20.8, 20.6, 20.64, 20.44, 20.4, 20.08, 20.08, 20.2, 20.36, 20.36] -56.68821120262146 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.914990186691284, 'TIME_S_1KI': 53.914990186691284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.4719800853732, 'W': 20.188888585576834} -[16.52, 16.4, 16.4, 16.56, 16.36, 16.52, 16.8, 16.56, 16.56, 16.76, 16.72, 16.68, 16.8, 16.84, 16.84, 16.84, 17.0, 17.2, 17.0, 17.16] -300.93999999999994 -15.046999999999997 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.914990186691284, 'TIME_S_1KI': 53.914990186691284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.4719800853732, 'W': 20.188888585576834, 'J_1KI': 1144.4719800853732, 'W_1KI': 20.188888585576834, 'W_D': 5.141888585576837, 'J_D': 291.48446611952824, 'W_D_1KI': 5.141888585576837, 'J_D_1KI': 5.141888585576837} +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 497, 1003, ..., 2498980, + 2499516, 2500000]), + col_indices=tensor([ 3, 16, 28, ..., 4972, 4988, 4998]), + values=tensor([0.4080, 0.5007, 0.4190, ..., 0.3549, 0.8542, 0.4591]), + size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) +tensor([0.8009, 0.9770, 0.1809, ..., 0.4100, 0.2770, 0.6242]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 2500000 +Density: 0.1 +Time: 10.763426303863525 seconds + +[20.76, 20.6, 20.8, 20.68, 20.68, 20.84, 20.92, 20.84, 20.72, 20.72] +[20.88, 20.92, 22.2, 23.32, 23.32, 25.32, 26.76, 27.36, 26.4, 26.08, 24.52, 24.48, 24.6, 24.56, 24.12] +15.28070592880249 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.763426303863525, 'TIME_S_1KI': 54.08756936614837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 349.11062700271606, 'W': 22.846498625739535} +[20.76, 20.6, 20.8, 20.68, 20.68, 20.84, 20.92, 20.84, 20.72, 20.72, 20.68, 20.48, 20.92, 20.92, 20.92, 20.88, 20.92, 20.88, 20.68, 20.8] +374.16 +18.708000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.763426303863525, 'TIME_S_1KI': 54.08756936614837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 349.11062700271606, 'W': 22.846498625739535, 'J_1KI': 1754.3247588076183, 'W_1KI': 114.80652575748509, 'W_D': 4.138498625739533, 'J_D': 63.23918048667905, 'W_D_1KI': 20.79647550622881, 'J_D_1KI': 104.50490204135079} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json index 103e112..79f4256 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.13774371147156, "TIME_S_1KI": 105.13774371147156, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2659.356458015442, "W": 24.098855953533207, "J_1KI": 2659.356458015442, "W_1KI": 24.098855953533207, "W_D": 5.604855953533207, "J_D": 618.5069492516519, "W_D_1KI": 5.604855953533207, "J_D_1KI": 5.604855953533207} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.224013328552246, "TIME_S_1KI": 112.24013328552246, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 352.91180219650266, "W": 23.07981204778166, "J_1KI": 3529.118021965027, "W_1KI": 230.7981204778166, "W_D": 4.68881204778166, "J_D": 71.69629919505113, "W_D_1KI": 46.8881204778166, "J_D_1KI": 468.88120477816597} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output index ba30685..83f94db 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output @@ -1,14 +1,14 @@ -['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 5000 -sd 0.2 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.13774371147156} +['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 100 -ss 5000 -sd 0.2 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.224013328552246} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1002, 2008, ..., 4998043, - 4999009, 5000000]), - col_indices=tensor([ 2, 3, 6, ..., 4986, 4991, 4996]), - values=tensor([0.2244, 0.9982, 0.0019, ..., 0.8643, 0.5557, 0.2909]), +tensor(crow_indices=tensor([ 0, 1006, 2027, ..., 4998032, + 4998984, 5000000]), + col_indices=tensor([ 1, 6, 9, ..., 4994, 4996, 4999]), + values=tensor([0.8034, 0.7589, 0.9109, ..., 0.7227, 0.1781, 0.2537]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.1159, 0.5733, 0.3535, ..., 0.0131, 0.4436, 0.7693]) +tensor([0.0665, 0.6506, 0.6868, ..., 0.4823, 0.5910, 0.0127]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,16 +16,16 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 105.13774371147156 seconds +Time: 11.224013328552246 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, 1002, 2008, ..., 4998043, - 4999009, 5000000]), - col_indices=tensor([ 2, 3, 6, ..., 4986, 4991, 4996]), - values=tensor([0.2244, 0.9982, 0.0019, ..., 0.8643, 0.5557, 0.2909]), +tensor(crow_indices=tensor([ 0, 1006, 2027, ..., 4998032, + 4998984, 5000000]), + col_indices=tensor([ 1, 6, 9, ..., 4994, 4996, 4999]), + values=tensor([0.8034, 0.7589, 0.9109, ..., 0.7227, 0.1781, 0.2537]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.1159, 0.5733, 0.3535, ..., 0.0131, 0.4436, 0.7693]) +tensor([0.0665, 0.6506, 0.6868, ..., 0.4823, 0.5910, 0.0127]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -33,13 +33,13 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 105.13774371147156 seconds +Time: 11.224013328552246 seconds -[20.56, 20.6, 20.6, 20.56, 20.72, 20.56, 20.56, 20.4, 20.28, 20.32] -[20.52, 20.36, 21.08, 21.64, 23.52, 27.0, 27.0, 27.8, 28.08, 27.68, 25.2, 24.2, 24.68, 24.52, 24.52, 24.6, 24.52, 24.04, 24.04, 24.2, 24.04, 24.16, 24.4, 24.28, 24.28, 24.4, 24.68, 24.64, 24.72, 24.8, 24.8, 24.64, 24.52, 24.52, 24.36, 24.28, 24.2, 24.16, 24.32, 24.64, 24.76, 25.0, 25.0, 25.0, 24.96, 24.68, 24.64, 24.64, 24.6, 24.64, 24.6, 24.6, 24.52, 24.6, 24.68, 24.68, 24.68, 24.6, 24.56, 24.44, 24.36, 24.44, 24.48, 24.4, 24.64, 24.6, 24.32, 24.24, 24.24, 24.32, 24.32, 24.2, 24.24, 24.32, 24.28, 24.32, 24.44, 24.48, 24.44, 24.6, 24.6, 24.44, 24.32, 24.64, 24.52, 24.76, 24.48, 24.6, 24.44, 24.28, 24.28, 24.32, 24.32, 24.48, 24.56, 24.68, 24.72, 24.76, 24.88, 24.8, 24.72, 25.04, 25.0, 24.88, 24.72, 24.72, 24.64, 24.56, 24.48] -110.35197949409485 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.13774371147156, 'TIME_S_1KI': 105.13774371147156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2659.356458015442, 'W': 24.098855953533207} -[20.56, 20.6, 20.6, 20.56, 20.72, 20.56, 20.56, 20.4, 20.28, 20.32, 20.32, 20.48, 20.56, 20.56, 20.76, 20.76, 20.68, 20.48, 20.52, 20.4] -369.88 -18.494 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.13774371147156, 'TIME_S_1KI': 105.13774371147156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2659.356458015442, 'W': 24.098855953533207, 'J_1KI': 2659.356458015442, 'W_1KI': 24.098855953533207, 'W_D': 5.604855953533207, 'J_D': 618.5069492516519, 'W_D_1KI': 5.604855953533207, 'J_D_1KI': 5.604855953533207} +[20.76, 20.72, 20.88, 20.84, 20.8, 20.64, 20.6, 20.44, 20.64, 20.6] +[20.56, 20.4, 21.2, 21.2, 22.36, 25.64, 28.2, 28.88, 28.52, 27.12, 25.64, 24.52, 24.64, 24.76, 24.72] +15.29093050956726 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.224013328552246, 'TIME_S_1KI': 112.24013328552246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 352.91180219650266, 'W': 23.07981204778166} +[20.76, 20.72, 20.88, 20.84, 20.8, 20.64, 20.6, 20.44, 20.64, 20.6, 20.16, 20.04, 20.04, 20.24, 20.04, 20.16, 20.12, 20.2, 20.4, 20.52] +367.82000000000005 +18.391000000000002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.224013328552246, 'TIME_S_1KI': 112.24013328552246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 352.91180219650266, 'W': 23.07981204778166, 'J_1KI': 3529.118021965027, 'W_1KI': 230.7981204778166, 'W_D': 4.68881204778166, 'J_D': 71.69629919505113, 'W_D_1KI': 46.8881204778166, 'J_D_1KI': 468.88120477816597} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json index 3d5cf8e..479e897 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json @@ -1 +1 @@ -{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 161.33362221717834, "TIME_S_1KI": 161.33362221717834, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4173.615943679809, "W": 24.123159694380117, "J_1KI": 4173.615943679809, "W_1KI": 24.123159694380117, "W_D": 5.260159694380114, "J_D": 910.0750749447333, "W_D_1KI": 5.260159694380114, "J_D_1KI": 5.260159694380114} +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.860999822616577, "TIME_S_1KI": 168.60999822616577, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.9433403301238, "W": 23.72394119074236, "J_1KI": 5069.433403301237, "W_1KI": 237.2394119074236, "W_D": 5.171941190742359, "J_D": 110.51625537872305, "W_D_1KI": 51.71941190742359, "J_D_1KI": 517.1941190742359} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output index c0efb69..228fd73 100644 --- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output @@ -1,14 +1,14 @@ -['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 5000 -sd 0.3 -c 16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 161.33362221717834} +['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 100 -ss 5000 -sd 0.3 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.860999822616577} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1457, 2918, ..., 7497005, - 7498478, 7500000]), - col_indices=tensor([ 5, 6, 7, ..., 4995, 4996, 4998]), - values=tensor([0.1779, 0.8323, 0.7588, ..., 0.2380, 0.4426, 0.6569]), +tensor(crow_indices=tensor([ 0, 1507, 3002, ..., 7496981, + 7498462, 7500000]), + col_indices=tensor([ 0, 3, 8, ..., 4995, 4997, 4998]), + values=tensor([0.0922, 0.0923, 0.7842, ..., 0.1175, 0.1649, 0.6291]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689]) +tensor([0.7654, 0.0824, 0.1261, ..., 0.9395, 0.8981, 0.0893]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,16 +16,16 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 161.33362221717834 seconds +Time: 16.860999822616577 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, 1457, 2918, ..., 7497005, - 7498478, 7500000]), - col_indices=tensor([ 5, 6, 7, ..., 4995, 4996, 4998]), - values=tensor([0.1779, 0.8323, 0.7588, ..., 0.2380, 0.4426, 0.6569]), +tensor(crow_indices=tensor([ 0, 1507, 3002, ..., 7496981, + 7498462, 7500000]), + col_indices=tensor([ 0, 3, 8, ..., 4995, 4997, 4998]), + values=tensor([0.0922, 0.0923, 0.7842, ..., 0.1175, 0.1649, 0.6291]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689]) +tensor([0.7654, 0.0824, 0.1261, ..., 0.9395, 0.8981, 0.0893]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -33,13 +33,13 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 161.33362221717834 seconds +Time: 16.860999822616577 seconds -[20.88, 20.96, 21.04, 20.92, 21.44, 21.32, 21.2, 21.24, 21.24, 20.44] -[20.36, 20.52, 23.44, 23.44, 24.92, 27.36, 30.32, 31.72, 29.36, 27.84, 25.72, 25.0, 24.32, 24.2, 24.2, 24.2, 24.2, 24.12, 24.0, 24.2, 24.36, 24.32, 24.24, 24.08, 24.08, 24.16, 24.28, 24.44, 24.44, 24.64, 24.44, 24.44, 24.8, 24.64, 24.56, 24.6, 24.68, 24.36, 24.52, 24.52, 24.56, 24.56, 24.88, 24.64, 24.56, 24.48, 24.04, 24.12, 24.28, 24.24, 24.28, 24.36, 24.36, 24.36, 24.48, 24.64, 24.6, 24.68, 24.48, 24.44, 24.44, 24.4, 24.28, 24.6, 24.44, 24.32, 24.32, 24.24, 24.2, 24.16, 24.0, 24.08, 24.36, 24.44, 24.64, 24.72, 24.8, 24.6, 24.6, 24.68, 24.6, 24.28, 24.2, 24.08, 24.24, 24.16, 24.28, 24.48, 24.56, 24.88, 24.88, 24.84, 25.04, 24.84, 24.76, 24.56, 24.2, 24.16, 24.24, 24.32, 24.24, 24.24, 24.2, 24.2, 24.2, 24.32, 24.32, 24.4, 24.4, 24.4, 24.64, 24.68, 24.52, 24.48, 24.48, 24.48, 24.56, 24.6, 24.88, 24.8, 24.64, 24.48, 24.6, 24.6, 24.84, 24.76, 24.8, 24.8, 24.72, 24.6, 24.64, 24.28, 24.12, 24.32, 24.2, 24.28, 24.4, 24.48, 24.28, 24.12, 24.12, 24.28, 24.24, 24.2, 24.2, 24.16, 24.04, 24.24, 24.36, 24.48, 24.48, 24.6, 24.6, 24.76, 24.56, 24.6, 24.56, 24.44, 24.4, 24.32, 24.36, 24.52, 24.48, 24.52, 24.52, 24.52, 24.72, 24.6, 24.6, 24.68, 24.64] -173.01282238960266 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 161.33362221717834, 'TIME_S_1KI': 161.33362221717834, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4173.615943679809, 'W': 24.123159694380117} -[20.88, 20.96, 21.04, 20.92, 21.44, 21.32, 21.2, 21.24, 21.24, 20.44, 20.6, 20.44, 20.8, 20.72, 20.84, 21.0, 20.96, 20.96, 20.88, 20.68] -377.26000000000005 -18.863000000000003 -{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 161.33362221717834, 'TIME_S_1KI': 161.33362221717834, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4173.615943679809, 'W': 24.123159694380117, 'J_1KI': 4173.615943679809, 'W_1KI': 24.123159694380117, 'W_D': 5.260159694380114, 'J_D': 910.0750749447333, 'W_D_1KI': 5.260159694380114, 'J_D_1KI': 5.260159694380114} +[20.72, 20.68, 20.76, 20.76, 20.72, 20.56, 20.6, 20.4, 20.4, 20.28] +[20.28, 20.12, 20.36, 22.84, 23.44, 27.24, 29.08, 29.68, 28.6, 28.0, 25.76, 24.4, 24.4, 24.44, 24.48, 24.56, 24.52, 24.52, 24.48, 24.52, 24.6] +21.368428468704224 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.860999822616577, 'TIME_S_1KI': 168.60999822616577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.9433403301238, 'W': 23.72394119074236} +[20.72, 20.68, 20.76, 20.76, 20.72, 20.56, 20.6, 20.4, 20.4, 20.28, 20.48, 20.36, 20.4, 20.28, 20.48, 20.48, 20.84, 20.96, 21.12, 21.0] +371.04 +18.552 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.860999822616577, 'TIME_S_1KI': 168.60999822616577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.9433403301238, 'W': 23.72394119074236, 'J_1KI': 5069.433403301237, 'W_1KI': 237.2394119074236, 'W_D': 5.171941190742359, 'J_D': 110.51625537872305, 'W_D_1KI': 51.71941190742359, 'J_D_1KI': 517.1941190742359} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json new file mode 100644 index 0000000..a05f0de --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 20.996277332305908, "TIME_S_1KI": 209.96277332305908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 668.5575802040102, "W": 24.39593264447633, "J_1KI": 6685.575802040102, "W_1KI": 243.9593264447633, "W_D": 5.836932644476324, "J_D": 159.9580397877693, "W_D_1KI": 58.36932644476324, "J_D_1KI": 583.6932644476324} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output new file mode 100644 index 0000000..b7814dc --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.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 100 -ss 5000 -sd 0.4 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 20.996277332305908} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2028, 3979, ..., 9995991, + 9997998, 10000000]), + col_indices=tensor([ 0, 3, 5, ..., 4997, 4998, 4999]), + values=tensor([0.5630, 0.7878, 0.5063, ..., 0.7425, 0.7666, 0.8643]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.1259, 0.6554, 0.1049, ..., 0.7276, 0.1818, 0.8981]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 20.996277332305908 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, 2028, 3979, ..., 9995991, + 9997998, 10000000]), + col_indices=tensor([ 0, 3, 5, ..., 4997, 4998, 4999]), + values=tensor([0.5630, 0.7878, 0.5063, ..., 0.7425, 0.7666, 0.8643]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.1259, 0.6554, 0.1049, ..., 0.7276, 0.1818, 0.8981]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 20.996277332305908 seconds + +[20.64, 20.64, 20.68, 20.84, 20.64, 20.56, 20.36, 20.48, 20.44, 20.6] +[20.92, 21.16, 21.52, 23.96, 23.96, 25.64, 28.24, 30.08, 29.68, 29.36, 27.16, 26.72, 25.12, 25.24, 25.48, 25.4, 25.28, 25.28, 25.08, 24.72, 24.36, 24.44, 24.32, 24.44, 24.68, 24.6, 24.52] +27.40446901321411 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 20.996277332305908, 'TIME_S_1KI': 209.96277332305908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.5575802040102, 'W': 24.39593264447633} +[20.64, 20.64, 20.68, 20.84, 20.64, 20.56, 20.36, 20.48, 20.44, 20.6, 20.44, 20.32, 20.6, 20.76, 20.76, 20.76, 20.96, 20.76, 20.56, 20.44] +371.18000000000006 +18.559000000000005 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 20.996277332305908, 'TIME_S_1KI': 209.96277332305908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.5575802040102, 'W': 24.39593264447633, 'J_1KI': 6685.575802040102, 'W_1KI': 243.9593264447633, 'W_D': 5.836932644476324, 'J_D': 159.9580397877693, 'W_D_1KI': 58.36932644476324, 'J_D_1KI': 583.6932644476324} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json new file mode 100644 index 0000000..ba2757e --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.35210919380188, "TIME_S_1KI": 263.5210919380188, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 811.2840313434602, "W": 24.192575243793513, "J_1KI": 8112.840313434603, "W_1KI": 241.92575243793513, "W_D": 5.690575243793514, "J_D": 190.8301525540354, "W_D_1KI": 56.90575243793514, "J_D_1KI": 569.0575243793514} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output new file mode 100644 index 0000000..a6d630f --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.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 100 -ss 5000 -sd 0.5 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.35210919380188} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2515, 4982, ..., 12494949, + 12497477, 12500000]), + col_indices=tensor([ 0, 2, 3, ..., 4997, 4998, 4999]), + values=tensor([0.8539, 0.6304, 0.9472, ..., 0.9422, 0.2299, 0.9872]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.9794, 0.7866, 0.7649, ..., 0.0670, 0.2063, 0.9592]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 26.35210919380188 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, 2515, 4982, ..., 12494949, + 12497477, 12500000]), + col_indices=tensor([ 0, 2, 3, ..., 4997, 4998, 4999]), + values=tensor([0.8539, 0.6304, 0.9472, ..., 0.9422, 0.2299, 0.9872]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.9794, 0.7866, 0.7649, ..., 0.0670, 0.2063, 0.9592]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 26.35210919380188 seconds + +[20.32, 20.32, 20.56, 20.52, 20.8, 20.8, 20.84, 20.84, 20.56, 20.4] +[20.56, 20.68, 20.84, 22.32, 23.2, 25.76, 28.56, 30.4, 30.84, 30.24, 27.28, 27.28, 26.24, 24.76, 24.6, 24.68, 24.6, 24.4, 24.44, 24.36, 24.16, 24.28, 24.2, 24.2, 24.24, 24.24, 24.16, 24.04, 24.0, 24.0, 24.24, 24.4, 24.52] +33.534422159194946 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.35210919380188, 'TIME_S_1KI': 263.5210919380188, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 811.2840313434602, 'W': 24.192575243793513} +[20.32, 20.32, 20.56, 20.52, 20.8, 20.8, 20.84, 20.84, 20.56, 20.4, 20.56, 20.68, 20.6, 20.6, 20.6, 20.52, 20.24, 20.28, 20.48, 20.32] +370.03999999999996 +18.502 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.35210919380188, 'TIME_S_1KI': 263.5210919380188, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 811.2840313434602, 'W': 24.192575243793513, 'J_1KI': 8112.840313434603, 'W_1KI': 241.92575243793513, 'W_D': 5.690575243793514, 'J_D': 190.8301525540354, 'W_D_1KI': 56.90575243793514, 'J_D_1KI': 569.0575243793514} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json index 3ce07d9..d5e9941 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4716, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.072229146957397, "TIME_S_1KI": 2.1357568165728154, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1865.214413022995, "W": 124.6, "J_1KI": 395.5077211668777, "W_1KI": 26.42069550466497, "W_D": 88.853, "J_D": 1330.0954754440784, "W_D_1KI": 18.84075487701442, "J_D_1KI": 3.9950710086968657} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.452549695968628, "TIME_S_1KI": 2.6664842132639412, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1764.6434702682495, "W": 122.04, "J_1KI": 410.85994651181596, "W_1KI": 28.41443538998836, "W_D": 80.21600000000001, "J_D": 1159.8872550888063, "W_D_1KI": 18.676600698486613, "J_D_1KI": 4.348451850637163} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output index 3a1030a..1c044b5 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output @@ -1,14 +1,14 @@ ['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', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.2264370918273926} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.4442625045776367} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1050, 2086, ..., 9997973, - 9998977, 10000000]), - col_indices=tensor([ 7, 18, 19, ..., 9986, 9989, 9991]), - values=tensor([0.4594, 0.3854, 0.2627, ..., 0.1030, 0.9821, 0.5221]), +tensor(crow_indices=tensor([ 0, 1035, 2082, ..., 9998102, + 9999051, 10000000]), + col_indices=tensor([ 19, 30, 32, ..., 9959, 9982, 9985]), + values=tensor([0.5712, 0.3257, 0.7476, ..., 0.5702, 0.2998, 0.6598]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.7604, 0.6286, 0.0952, ..., 0.1700, 0.1146, 0.5013]) +tensor([0.5243, 0.8505, 0.9216, ..., 0.4616, 0.9699, 0.2962]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,19 +16,19 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 2.2264370918273926 seconds +Time: 2.4442625045776367 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4716', '-ss', '10000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.072229146957397} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4295', '-ss', '10000', '-sd', '0.1', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.452549695968628} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1024, 2032, ..., 9997988, - 9999031, 10000000]), - col_indices=tensor([ 8, 19, 25, ..., 9964, 9974, 9989]), - values=tensor([0.6442, 0.9503, 0.4324, ..., 0.4734, 0.5264, 0.7582]), +tensor(crow_indices=tensor([ 0, 1025, 1966, ..., 9998059, + 9999093, 10000000]), + col_indices=tensor([ 1, 7, 10, ..., 9966, 9969, 9981]), + values=tensor([0.1039, 0.2120, 0.0962, ..., 0.1651, 0.8455, 0.3269]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.7346, 0.1525, 0.9122, ..., 0.8135, 0.4141, 0.4880]) +tensor([0.9433, 0.5217, 0.8143, ..., 0.2476, 0.9485, 0.9722]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -36,16 +36,16 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 10.072229146957397 seconds +Time: 11.452549695968628 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, 1024, 2032, ..., 9997988, - 9999031, 10000000]), - col_indices=tensor([ 8, 19, 25, ..., 9964, 9974, 9989]), - values=tensor([0.6442, 0.9503, 0.4324, ..., 0.4734, 0.5264, 0.7582]), +tensor(crow_indices=tensor([ 0, 1025, 1966, ..., 9998059, + 9999093, 10000000]), + col_indices=tensor([ 1, 7, 10, ..., 9966, 9969, 9981]), + values=tensor([0.1039, 0.2120, 0.0962, ..., 0.1651, 0.8455, 0.3269]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.7346, 0.1525, 0.9122, ..., 0.8135, 0.4141, 0.4880]) +tensor([0.9433, 0.5217, 0.8143, ..., 0.2476, 0.9485, 0.9722]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -53,13 +53,13 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 10.072229146957397 seconds +Time: 11.452549695968628 seconds -[40.38, 39.18, 39.76, 39.52, 39.78, 39.24, 39.97, 39.14, 39.12, 39.09] -[124.6] -14.969618082046509 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.072229146957397, 'TIME_S_1KI': 2.1357568165728154, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.214413022995, 'W': 124.6} -[40.38, 39.18, 39.76, 39.52, 39.78, 39.24, 39.97, 39.14, 39.12, 39.09, 39.77, 39.88, 39.63, 39.31, 39.15, 39.05, 39.3, 39.01, 43.67, 41.22] -714.94 -35.747 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.072229146957397, 'TIME_S_1KI': 2.1357568165728154, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.214413022995, 'W': 124.6, 'J_1KI': 395.5077211668777, 'W_1KI': 26.42069550466497, 'W_D': 88.853, 'J_D': 1330.0954754440784, 'W_D_1KI': 18.84075487701442, 'J_D_1KI': 3.9950710086968657} +[40.21, 39.72, 39.55, 39.95, 39.94, 40.02, 39.57, 39.94, 44.79, 39.53] +[122.04] +14.459549903869629 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.452549695968628, 'TIME_S_1KI': 2.6664842132639412, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1764.6434702682495, 'W': 122.04} +[40.21, 39.72, 39.55, 39.95, 39.94, 40.02, 39.57, 39.94, 44.79, 39.53, 40.97, 39.48, 39.49, 39.81, 60.91, 51.02, 67.71, 65.48, 67.26, 42.97] +836.48 +41.824 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.452549695968628, 'TIME_S_1KI': 2.6664842132639412, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1764.6434702682495, 'W': 122.04, 'J_1KI': 410.85994651181596, 'W_1KI': 28.41443538998836, 'W_D': 80.21600000000001, 'J_D': 1159.8872550888063, 'W_D_1KI': 18.676600698486613, 'J_D_1KI': 4.348451850637163} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json index e84e4d4..7cbfdaf 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2353, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.32719349861145, "TIME_S_1KI": 4.813936888487654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2176.6533761024475, "W": 119.81, "J_1KI": 925.0545584795782, "W_1KI": 50.91797705057373, "W_D": 83.82675, "J_D": 1522.9261196494103, "W_D_1KI": 35.62547811304718, "J_D_1KI": 15.140449686802882} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2191, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.797947645187378, "TIME_S_1KI": 4.928319326876941, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2022.1372631835936, "W": 118.88, "J_1KI": 922.9289197551773, "W_1KI": 54.25832952989502, "W_D": 82.67325, "J_D": 1406.2639594001769, "W_D_1KI": 37.733112733911454, "J_D_1KI": 17.22186797531331} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output index a699624..61a15aa 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output @@ -1,14 +1,14 @@ ['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', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.462156772613525} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.791375398635864} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1973, 4017, ..., 19996005, - 19998026, 20000000]), - col_indices=tensor([ 9, 13, 23, ..., 9991, 9994, 9996]), - values=tensor([0.6139, 0.9857, 0.8934, ..., 0.0556, 0.4216, 0.1096]), +tensor(crow_indices=tensor([ 0, 1984, 3988, ..., 19996067, + 19998037, 20000000]), + col_indices=tensor([ 4, 5, 9, ..., 9988, 9990, 9993]), + values=tensor([0.6195, 0.8354, 0.3980, ..., 0.7932, 0.7837, 0.6598]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.2039, 0.8822, 0.1274, ..., 0.2057, 0.5761, 0.2633]) +tensor([0.5668, 0.7998, 0.9844, ..., 0.6200, 0.3521, 0.6910]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,19 +16,19 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 4.462156772613525 seconds +Time: 4.791375398635864 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2353', '-ss', '10000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.32719349861145} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2191', '-ss', '10000', '-sd', '0.2', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.797947645187378} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 2048, 4126, ..., 19995999, - 19998042, 20000000]), - col_indices=tensor([ 2, 9, 12, ..., 9990, 9992, 9998]), - values=tensor([0.9897, 0.5727, 0.9448, ..., 0.9783, 0.3588, 0.5443]), +tensor(crow_indices=tensor([ 0, 2053, 4071, ..., 19995990, + 19998011, 20000000]), + col_indices=tensor([ 2, 5, 7, ..., 9978, 9979, 9995]), + values=tensor([0.3718, 0.8252, 0.7364, ..., 0.7436, 0.4903, 0.8833]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.6351, 0.0385, 0.6216, ..., 0.2960, 0.7778, 0.2124]) +tensor([0.1328, 0.8897, 0.5612, ..., 0.9517, 0.3970, 0.1461]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -36,16 +36,16 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 11.32719349861145 seconds +Time: 10.797947645187378 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, 2048, 4126, ..., 19995999, - 19998042, 20000000]), - col_indices=tensor([ 2, 9, 12, ..., 9990, 9992, 9998]), - values=tensor([0.9897, 0.5727, 0.9448, ..., 0.9783, 0.3588, 0.5443]), +tensor(crow_indices=tensor([ 0, 2053, 4071, ..., 19995990, + 19998011, 20000000]), + col_indices=tensor([ 2, 5, 7, ..., 9978, 9979, 9995]), + values=tensor([0.3718, 0.8252, 0.7364, ..., 0.7436, 0.4903, 0.8833]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.6351, 0.0385, 0.6216, ..., 0.2960, 0.7778, 0.2124]) +tensor([0.1328, 0.8897, 0.5612, ..., 0.9517, 0.3970, 0.1461]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -53,13 +53,13 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 11.32719349861145 seconds +Time: 10.797947645187378 seconds -[40.52, 39.82, 39.98, 39.31, 45.78, 39.49, 39.61, 39.59, 39.44, 39.87] -[119.81] -18.167543411254883 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.32719349861145, 'TIME_S_1KI': 4.813936888487654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2176.6533761024475, 'W': 119.81} -[40.52, 39.82, 39.98, 39.31, 45.78, 39.49, 39.61, 39.59, 39.44, 39.87, 41.36, 39.61, 39.47, 39.55, 39.57, 39.39, 39.51, 39.42, 39.39, 39.72] -719.665 -35.98325 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.32719349861145, 'TIME_S_1KI': 4.813936888487654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2176.6533761024475, 'W': 119.81, 'J_1KI': 925.0545584795782, 'W_1KI': 50.91797705057373, 'W_D': 83.82675, 'J_D': 1522.9261196494103, 'W_D_1KI': 35.62547811304718, 'J_D_1KI': 15.140449686802882} +[40.47, 41.35, 39.66, 40.26, 40.03, 39.5, 40.17, 39.67, 39.73, 39.92] +[118.88] +17.009902954101562 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2191, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.797947645187378, 'TIME_S_1KI': 4.928319326876941, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.1372631835936, 'W': 118.88} +[40.47, 41.35, 39.66, 40.26, 40.03, 39.5, 40.17, 39.67, 39.73, 39.92, 40.22, 39.71, 39.72, 40.09, 39.98, 40.13, 39.63, 39.44, 45.03, 39.46] +724.135 +36.20675 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2191, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.797947645187378, 'TIME_S_1KI': 4.928319326876941, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.1372631835936, 'W': 118.88, 'J_1KI': 922.9289197551773, 'W_1KI': 54.25832952989502, 'W_D': 82.67325, 'J_D': 1406.2639594001769, 'W_D_1KI': 37.733112733911454, 'J_D_1KI': 17.22186797531331} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json index 0688fa1..faf8197 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1423, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.225417137145996, "TIME_S_1KI": 7.185816681058324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2165.9509678459167, "W": 114.32, "J_1KI": 1522.1018748038769, "W_1KI": 80.33731553056921, "W_D": 78.57325, "J_D": 1488.679206475675, "W_D_1KI": 55.21661981728742, "J_D_1KI": 38.802965437306696} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1433, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.876516819000244, "TIME_S_1KI": 7.5900326720169184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2139.1934151315686, "W": 115.81999999999998, "J_1KI": 1492.8076867631323, "W_1KI": 80.82344731332866, "W_D": 79.70624999999998, "J_D": 1472.173071531951, "W_D_1KI": 55.62194696441031, "J_D_1KI": 38.81503626267293} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output index cc1271e..ef840f6 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output @@ -1,14 +1,14 @@ ['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', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.375466823577881} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.326756000518799} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 2918, 5897, ..., 29994058, - 29997053, 30000000]), - col_indices=tensor([ 2, 5, 6, ..., 9983, 9996, 9997]), - values=tensor([0.7175, 0.6857, 0.0471, ..., 0.3859, 0.1988, 0.0619]), +tensor(crow_indices=tensor([ 0, 3009, 6089, ..., 29994053, + 29996999, 30000000]), + col_indices=tensor([ 1, 9, 11, ..., 9985, 9993, 9999]), + values=tensor([0.2577, 0.0805, 0.4624, ..., 0.3276, 0.0599, 0.1828]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.8128, 0.9693, 0.2211, ..., 0.0242, 0.9676, 0.8056]) +tensor([0.9046, 0.5378, 0.0670, ..., 0.4938, 0.1002, 0.8451]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,19 +16,19 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 7.375466823577881 seconds +Time: 7.326756000518799 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1423', '-ss', '10000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.225417137145996} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1433', '-ss', '10000', '-sd', '0.3', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.876516819000244} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 3014, 6025, ..., 29993963, - 29996988, 30000000]), - col_indices=tensor([ 3, 15, 23, ..., 9994, 9996, 9999]), - values=tensor([0.7948, 0.4683, 0.9975, ..., 0.7409, 0.6873, 0.4627]), +tensor(crow_indices=tensor([ 0, 2984, 5966, ..., 29993973, + 29997036, 30000000]), + col_indices=tensor([ 2, 6, 8, ..., 9989, 9994, 9995]), + values=tensor([0.0728, 0.9990, 0.4694, ..., 0.7329, 0.2175, 0.4979]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614]) +tensor([0.9122, 0.6942, 0.6684, ..., 0.3538, 0.9426, 0.8104]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -36,16 +36,16 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 10.225417137145996 seconds +Time: 10.876516819000244 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, 3014, 6025, ..., 29993963, - 29996988, 30000000]), - col_indices=tensor([ 3, 15, 23, ..., 9994, 9996, 9999]), - values=tensor([0.7948, 0.4683, 0.9975, ..., 0.7409, 0.6873, 0.4627]), +tensor(crow_indices=tensor([ 0, 2984, 5966, ..., 29993973, + 29997036, 30000000]), + col_indices=tensor([ 2, 6, 8, ..., 9989, 9994, 9995]), + values=tensor([0.0728, 0.9990, 0.4694, ..., 0.7329, 0.2175, 0.4979]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614]) +tensor([0.9122, 0.6942, 0.6684, ..., 0.3538, 0.9426, 0.8104]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -53,13 +53,13 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 10.225417137145996 seconds +Time: 10.876516819000244 seconds -[40.04, 41.53, 39.4, 39.29, 40.65, 39.48, 39.42, 39.79, 39.24, 39.4] -[114.32] -18.94638705253601 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1423, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.225417137145996, 'TIME_S_1KI': 7.185816681058324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2165.9509678459167, 'W': 114.32} -[40.04, 41.53, 39.4, 39.29, 40.65, 39.48, 39.42, 39.79, 39.24, 39.4, 40.83, 40.13, 39.25, 39.79, 39.29, 39.6, 39.88, 39.16, 39.35, 39.1] -714.935 -35.74675 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1423, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.225417137145996, 'TIME_S_1KI': 7.185816681058324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2165.9509678459167, 'W': 114.32, 'J_1KI': 1522.1018748038769, 'W_1KI': 80.33731553056921, 'W_D': 78.57325, 'J_D': 1488.679206475675, 'W_D_1KI': 55.21661981728742, 'J_D_1KI': 38.802965437306696} +[40.68, 39.67, 39.67, 39.75, 39.81, 39.61, 39.83, 39.6, 39.64, 40.06] +[115.82] +18.469982862472534 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1433, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.876516819000244, 'TIME_S_1KI': 7.5900326720169184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2139.1934151315686, 'W': 115.81999999999998} +[40.68, 39.67, 39.67, 39.75, 39.81, 39.61, 39.83, 39.6, 39.64, 40.06, 40.61, 39.6, 39.69, 39.61, 40.38, 44.77, 40.11, 39.94, 40.18, 39.48] +722.2750000000001 +36.11375 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1433, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.876516819000244, 'TIME_S_1KI': 7.5900326720169184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2139.1934151315686, 'W': 115.81999999999998, 'J_1KI': 1492.8076867631323, 'W_1KI': 80.82344731332866, 'W_D': 79.70624999999998, 'J_D': 1472.173071531951, 'W_D_1KI': 55.62194696441031, 'J_D_1KI': 38.81503626267293} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json index 074b9e9..925268b 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53642, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.719228744506836, "TIME_S_1KI": 0.19982902845730652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1911.0154657673836, "W": 139.47, "J_1KI": 35.62535822242615, "W_1KI": 2.600014913687036, "W_D": 104.0105, "J_D": 1425.1500258277654, "W_D_1KI": 1.9389750568584316, "J_D_1KI": 0.036146583961418885} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53196, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.621366739273071, "TIME_S_1KI": 0.19966476312642062, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1859.8867098927496, "W": 135.45, "J_1KI": 34.962905291614966, "W_1KI": 2.5462440785021427, "W_D": 100.0975, "J_D": 1374.4555920523405, "W_D_1KI": 1.8816734340927888, "J_D_1KI": 0.03537246097625364} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output index 687e6a4..52d3031 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output @@ -1,14 +1,14 @@ ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.2534494400024414} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3697686195373535} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 505, 999, ..., 2498983, - 2499471, 2500000]), - col_indices=tensor([ 5, 18, 40, ..., 4969, 4978, 4986]), - values=tensor([0.5163, 0.4412, 0.3185, ..., 0.4202, 0.6886, 0.7408]), +tensor(crow_indices=tensor([ 0, 482, 953, ..., 2499004, + 2499522, 2500000]), + col_indices=tensor([ 8, 9, 10, ..., 4989, 4992, 4993]), + values=tensor([0.1808, 0.3744, 0.3617, ..., 0.4915, 0.9116, 0.1304]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.4458, 0.7168, 0.1041, ..., 0.9922, 0.1900, 0.6310]) +tensor([0.8787, 0.8438, 0.3305, ..., 0.0934, 0.4772, 0.3202]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 0.2534494400024414 seconds +Time: 0.3697686195373535 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41428', '-ss', '5000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.109162092208862} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28396', '-ss', '5000', '-sd', '0.1', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.604789972305298} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 495, 993, ..., 2498983, - 2499503, 2500000]), - col_indices=tensor([ 3, 19, 21, ..., 4946, 4955, 4989]), - values=tensor([0.8083, 0.9559, 0.4619, ..., 0.5259, 0.1142, 0.6698]), +tensor(crow_indices=tensor([ 0, 510, 995, ..., 2498986, + 2499511, 2500000]), + col_indices=tensor([ 1, 2, 8, ..., 4984, 4986, 4997]), + values=tensor([0.0748, 0.9968, 0.9443, ..., 0.0526, 0.3043, 0.5465]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.9615, 0.1263, 0.8854, ..., 0.2773, 0.4703, 0.0965]) +tensor([0.9671, 0.4248, 0.6295, ..., 0.7757, 0.3159, 0.1103]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,19 +36,19 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 8.109162092208862 seconds +Time: 5.604789972305298 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53642', '-ss', '5000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.719228744506836} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53196', '-ss', '5000', '-sd', '0.1', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.621366739273071} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 514, 1060, ..., 2499048, - 2499512, 2500000]), - col_indices=tensor([ 10, 15, 21, ..., 4947, 4988, 4996]), - values=tensor([0.5424, 0.5712, 0.8006, ..., 0.9771, 0.7885, 0.2387]), +tensor(crow_indices=tensor([ 0, 515, 1007, ..., 2498937, + 2499481, 2500000]), + col_indices=tensor([ 6, 15, 19, ..., 4986, 4991, 4993]), + values=tensor([0.5542, 0.8952, 0.8012, ..., 0.8325, 0.7201, 0.7015]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.8250, 0.9268, 0.6213, ..., 0.2000, 0.5207, 0.9721]) +tensor([0.4746, 0.7865, 0.3346, ..., 0.0574, 0.7499, 0.4825]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -56,16 +56,16 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 10.719228744506836 seconds +Time: 10.621366739273071 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, 514, 1060, ..., 2499048, - 2499512, 2500000]), - col_indices=tensor([ 10, 15, 21, ..., 4947, 4988, 4996]), - values=tensor([0.5424, 0.5712, 0.8006, ..., 0.9771, 0.7885, 0.2387]), +tensor(crow_indices=tensor([ 0, 515, 1007, ..., 2498937, + 2499481, 2500000]), + col_indices=tensor([ 6, 15, 19, ..., 4986, 4991, 4993]), + values=tensor([0.5542, 0.8952, 0.8012, ..., 0.8325, 0.7201, 0.7015]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.8250, 0.9268, 0.6213, ..., 0.2000, 0.5207, 0.9721]) +tensor([0.4746, 0.7865, 0.3346, ..., 0.0574, 0.7499, 0.4825]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -73,13 +73,13 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 10.719228744506836 seconds +Time: 10.621366739273071 seconds -[40.33, 39.84, 39.58, 39.84, 39.14, 39.05, 39.58, 39.29, 39.44, 39.24] -[139.47] -13.701982259750366 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.719228744506836, 'TIME_S_1KI': 0.19982902845730652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.0154657673836, 'W': 139.47} -[40.33, 39.84, 39.58, 39.84, 39.14, 39.05, 39.58, 39.29, 39.44, 39.24, 39.75, 39.09, 39.67, 39.12, 39.04, 39.58, 39.1, 39.15, 39.53, 38.98] -709.19 -35.459500000000006 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.719228744506836, 'TIME_S_1KI': 0.19982902845730652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.0154657673836, 'W': 139.47, 'J_1KI': 35.62535822242615, 'W_1KI': 2.600014913687036, 'W_D': 104.0105, 'J_D': 1425.1500258277654, 'W_D_1KI': 1.9389750568584316, 'J_D_1KI': 0.036146583961418885} +[39.76, 40.98, 39.03, 38.9, 39.03, 39.28, 39.39, 38.84, 39.07, 38.79] +[135.45] +13.731168031692505 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.621366739273071, 'TIME_S_1KI': 0.19966476312642062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.8867098927496, 'W': 135.45} +[39.76, 40.98, 39.03, 38.9, 39.03, 39.28, 39.39, 38.84, 39.07, 38.79, 39.74, 39.0, 39.18, 39.17, 39.18, 38.97, 39.43, 39.52, 39.42, 39.03] +707.05 +35.3525 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.621366739273071, 'TIME_S_1KI': 0.19966476312642062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.8867098927496, 'W': 135.45, 'J_1KI': 34.962905291614966, 'W_1KI': 2.5462440785021427, 'W_D': 100.0975, 'J_D': 1374.4555920523405, 'W_D_1KI': 1.8816734340927888, 'J_D_1KI': 0.03537246097625364} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json index cc46ed6..a26af98 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28937, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 14.827638626098633, "TIME_S_1KI": 0.5124110524967561, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3609.586873168945, "W": 118.08, "J_1KI": 124.73949867536183, "W_1KI": 4.080588865466358, "W_D": 72.93325, "J_D": 2229.496119728565, "W_D_1KI": 2.5204150395687184, "J_D_1KI": 0.08710008085042398} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28983, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.485858678817749, "TIME_S_1KI": 0.36179341955000344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1935.3792489004136, "W": 138.58, "J_1KI": 66.77636024222522, "W_1KI": 4.781423593140807, "W_D": 102.671, "J_D": 1433.8816774704458, "W_D_1KI": 3.542455922437291, "J_D_1KI": 0.12222530181269332} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output index b0c2e89..5fa6361 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output @@ -1,14 +1,14 @@ ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43933725357055664} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43319129943847656} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1026, 2006, ..., 4997956, - 4998952, 5000000]), - col_indices=tensor([ 2, 17, 24, ..., 4985, 4989, 4995]), - values=tensor([0.8274, 0.9158, 0.7152, ..., 0.4764, 0.4337, 0.1760]), +tensor(crow_indices=tensor([ 0, 992, 2019, ..., 4998034, + 4999022, 5000000]), + col_indices=tensor([ 6, 7, 25, ..., 4976, 4987, 4995]), + values=tensor([0.8659, 0.6827, 0.8740, ..., 0.4360, 0.6938, 0.9338]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.9697, 0.2900, 0.2967, ..., 0.8048, 0.6311, 0.8937]) +tensor([0.7963, 0.9033, 0.3372, ..., 0.2486, 0.4072, 0.8365]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 0.43933725357055664 seconds +Time: 0.43319129943847656 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23899', '-ss', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.217516660690308} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24238', '-ss', '5000', '-sd', '0.2', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 8.780949115753174} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1007, 2063, ..., 4997980, - 4998987, 5000000]), - col_indices=tensor([ 1, 11, 20, ..., 4979, 4988, 4999]), - values=tensor([0.6304, 0.9983, 0.6257, ..., 0.2269, 0.0229, 0.1968]), +tensor(crow_indices=tensor([ 0, 970, 1988, ..., 4997978, + 4998978, 5000000]), + col_indices=tensor([ 16, 17, 19, ..., 4996, 4997, 4999]), + values=tensor([0.1543, 0.7354, 0.5297, ..., 0.9670, 0.3753, 0.9860]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.1233, 0.6806, 0.4968, ..., 0.5662, 0.5626, 0.1071]) +tensor([0.8318, 0.0236, 0.3656, ..., 0.1748, 0.2631, 0.0655]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,19 +36,19 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 9.217516660690308 seconds +Time: 8.780949115753174 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27224', '-ss', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.878192901611328} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28983', '-ss', '5000', '-sd', '0.2', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.485858678817749} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 990, 1993, ..., 4997978, - 4998966, 5000000]), - col_indices=tensor([ 3, 4, 10, ..., 4987, 4994, 4995]), - values=tensor([0.4002, 0.6649, 0.4467, ..., 0.0548, 0.1613, 0.2598]), +tensor(crow_indices=tensor([ 0, 1046, 2086, ..., 4998016, + 4999015, 5000000]), + col_indices=tensor([ 1, 2, 7, ..., 4963, 4988, 4989]), + values=tensor([0.8714, 0.5660, 0.3384, ..., 0.1998, 0.0405, 0.9940]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.8115, 0.3462, 0.9342, ..., 0.0919, 0.3442, 0.4570]) +tensor([0.6062, 0.0119, 0.7376, ..., 0.3549, 0.0670, 0.7680]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -56,19 +56,16 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 9.878192901611328 seconds - -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28937', '-ss', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 14.827638626098633} +Time: 10.485858678817749 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, 969, 2001, ..., 4997956, - 4998975, 5000000]), - col_indices=tensor([ 6, 11, 14, ..., 4991, 4992, 4999]), - values=tensor([0.1794, 0.8821, 0.0709, ..., 0.4822, 0.4100, 0.3846]), +tensor(crow_indices=tensor([ 0, 1046, 2086, ..., 4998016, + 4999015, 5000000]), + col_indices=tensor([ 1, 2, 7, ..., 4963, 4988, 4989]), + values=tensor([0.8714, 0.5660, 0.3384, ..., 0.1998, 0.0405, 0.9940]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.9695, 0.1954, 0.0199, ..., 0.8094, 0.5782, 0.4777]) +tensor([0.6062, 0.0119, 0.7376, ..., 0.3549, 0.0670, 0.7680]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -76,30 +73,13 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 14.827638626098633 seconds +Time: 10.485858678817749 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, 969, 2001, ..., 4997956, - 4998975, 5000000]), - col_indices=tensor([ 6, 11, 14, ..., 4991, 4992, 4999]), - values=tensor([0.1794, 0.8821, 0.0709, ..., 0.4822, 0.4100, 0.3846]), - size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.9695, 0.1954, 0.0199, ..., 0.8094, 0.5782, 0.4777]) -Matrix Type: synthetic -Matrix Format: csr -Shape: torch.Size([5000, 5000]) -Rows: 5000 -Size: 25000000 -NNZ: 5000000 -Density: 0.2 -Time: 14.827638626098633 seconds - -[40.27, 40.67, 45.31, 61.44, 66.02, 68.38, 70.5, 73.12, 64.38, 67.33] -[118.08] -30.568994522094727 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 14.827638626098633, 'TIME_S_1KI': 0.5124110524967561, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3609.586873168945, 'W': 118.08} -[40.27, 40.67, 45.31, 61.44, 66.02, 68.38, 70.5, 73.12, 64.38, 67.33, 41.67, 40.18, 39.68, 39.83, 39.75, 39.7, 39.65, 40.28, 39.58, 39.66] -902.935 -45.14675 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 14.827638626098633, 'TIME_S_1KI': 0.5124110524967561, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3609.586873168945, 'W': 118.08, 'J_1KI': 124.73949867536183, 'W_1KI': 4.080588865466358, 'W_D': 72.93325, 'J_D': 2229.496119728565, 'W_D_1KI': 2.5204150395687184, 'J_D_1KI': 0.08710008085042398} +[40.02, 39.97, 39.89, 40.7, 39.75, 39.29, 39.24, 40.56, 39.28, 39.34] +[138.58] +13.965790510177612 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28983, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.485858678817749, 'TIME_S_1KI': 0.36179341955000344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1935.3792489004136, 'W': 138.58} +[40.02, 39.97, 39.89, 40.7, 39.75, 39.29, 39.24, 40.56, 39.28, 39.34, 40.14, 39.41, 39.41, 40.87, 40.15, 39.38, 39.46, 39.42, 41.94, 39.42] +718.1800000000001 +35.909000000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28983, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.485858678817749, 'TIME_S_1KI': 0.36179341955000344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1935.3792489004136, 'W': 138.58, 'J_1KI': 66.77636024222522, 'W_1KI': 4.781423593140807, 'W_D': 102.671, 'J_D': 1433.8816774704458, 'W_D_1KI': 3.542455922437291, 'J_D_1KI': 0.12222530181269332} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json index 5fa7d17..6353fbe 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json @@ -1 +1 @@ -{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.466439723968506, "TIME_S_1KI": 0.5417972732150588, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1987.1806412553788, "W": 136.19, "J_1KI": 102.86678958770985, "W_1KI": 7.04990164613314, "W_D": 100.84025, "J_D": 1471.3840418485402, "W_D_1KI": 5.2200150119059945, "J_D_1KI": 0.2702150849935808} +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19322, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.55522608757019, "TIME_S_1KI": 0.5462802032693401, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1980.0031997823714, "W": 138.03, "J_1KI": 102.47402959229746, "W_1KI": 7.143670427491978, "W_D": 102.176, "J_D": 1465.6872197418213, "W_D_1KI": 5.288065417658628, "J_D_1KI": 0.2736810587754181} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output index 6a5e106..b56758b 100644 --- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output @@ -1,14 +1,14 @@ ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.6186139583587646} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.6236028671264648} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1488, 3016, ..., 7497085, - 7498552, 7500000]), - col_indices=tensor([ 6, 10, 18, ..., 4990, 4991, 4992]), - values=tensor([0.0319, 0.7689, 0.5437, ..., 0.3456, 0.3746, 0.7534]), +tensor(crow_indices=tensor([ 0, 1508, 3089, ..., 7496976, + 7498474, 7500000]), + col_indices=tensor([ 7, 9, 15, ..., 4993, 4994, 4999]), + values=tensor([0.5303, 0.4048, 0.9163, ..., 0.8482, 0.2205, 0.3341]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.3033, 0.5024, 0.5149, ..., 0.3250, 0.9742, 0.7218]) +tensor([0.9586, 0.6247, 0.6872, ..., 0.0060, 0.2602, 0.2097]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 0.6186139583587646 seconds +Time: 0.6236028671264648 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16973', '-ss', '5000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.225086212158203} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16837', '-ss', '5000', '-sd', '0.3', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.149490594863892} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1496, 2988, ..., 7496994, - 7498506, 7500000]), - col_indices=tensor([ 3, 6, 16, ..., 4989, 4992, 4998]), - values=tensor([0.5767, 0.7515, 0.3496, ..., 0.2965, 0.3271, 0.6066]), +tensor(crow_indices=tensor([ 0, 1490, 2963, ..., 7497013, + 7498520, 7500000]), + col_indices=tensor([ 3, 5, 7, ..., 4995, 4998, 4999]), + values=tensor([0.0553, 0.4159, 0.6036, ..., 0.7243, 0.9131, 0.1926]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.6092, 0.5985, 0.2131, ..., 0.5175, 0.3653, 0.9642]) +tensor([0.5885, 0.8517, 0.1610, ..., 0.2789, 0.8066, 0.5429]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,19 +36,19 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 9.225086212158203 seconds +Time: 9.149490594863892 seconds -['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19318', '-ss', '5000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.466439723968506} +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19322', '-ss', '5000', '-sd', '0.3', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.55522608757019} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1490, 3014, ..., 7497019, - 7498487, 7500000]), - col_indices=tensor([ 4, 5, 6, ..., 4990, 4995, 4998]), - values=tensor([0.5409, 0.4022, 0.8537, ..., 0.7192, 0.8151, 0.0953]), +tensor(crow_indices=tensor([ 0, 1484, 2984, ..., 7497085, + 7498530, 7500000]), + col_indices=tensor([ 5, 8, 9, ..., 4987, 4993, 4996]), + values=tensor([0.5644, 0.8179, 0.2626, ..., 0.6489, 0.4956, 0.4262]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666]) +tensor([0.0893, 0.0032, 0.8731, ..., 0.8525, 0.4366, 0.3134]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -56,16 +56,16 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 10.466439723968506 seconds +Time: 10.55522608757019 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, 1490, 3014, ..., 7497019, - 7498487, 7500000]), - col_indices=tensor([ 4, 5, 6, ..., 4990, 4995, 4998]), - values=tensor([0.5409, 0.4022, 0.8537, ..., 0.7192, 0.8151, 0.0953]), +tensor(crow_indices=tensor([ 0, 1484, 2984, ..., 7497085, + 7498530, 7500000]), + col_indices=tensor([ 5, 8, 9, ..., 4987, 4993, 4996]), + values=tensor([0.5644, 0.8179, 0.2626, ..., 0.6489, 0.4956, 0.4262]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666]) +tensor([0.0893, 0.0032, 0.8731, ..., 0.8525, 0.4366, 0.3134]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -73,13 +73,13 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 10.466439723968506 seconds +Time: 10.55522608757019 seconds -[39.82, 39.03, 39.04, 38.94, 39.09, 39.36, 39.4, 38.84, 39.28, 40.17] -[136.19] -14.591237545013428 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.466439723968506, 'TIME_S_1KI': 0.5417972732150588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.1806412553788, 'W': 136.19} -[39.82, 39.03, 39.04, 38.94, 39.09, 39.36, 39.4, 38.84, 39.28, 40.17, 40.47, 39.28, 39.08, 39.29, 39.92, 39.06, 39.2, 39.0, 39.43, 39.05] -706.9950000000001 -35.34975000000001 -{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.466439723968506, 'TIME_S_1KI': 0.5417972732150588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.1806412553788, 'W': 136.19, 'J_1KI': 102.86678958770985, 'W_1KI': 7.04990164613314, 'W_D': 100.84025, 'J_D': 1471.3840418485402, 'W_D_1KI': 5.2200150119059945, 'J_D_1KI': 0.2702150849935808} +[40.26, 39.93, 39.81, 39.63, 39.7, 39.44, 39.58, 40.08, 39.61, 39.95] +[138.03] +14.344730854034424 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.55522608757019, 'TIME_S_1KI': 0.5462802032693401, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.0031997823714, 'W': 138.03} +[40.26, 39.93, 39.81, 39.63, 39.7, 39.44, 39.58, 40.08, 39.61, 39.95, 40.68, 39.57, 40.69, 39.43, 39.89, 39.46, 39.71, 40.58, 39.58, 39.89] +717.0799999999999 +35.854 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.55522608757019, 'TIME_S_1KI': 0.5462802032693401, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1980.0031997823714, 'W': 138.03, 'J_1KI': 102.47402959229746, 'W_1KI': 7.143670427491978, 'W_D': 102.176, 'J_D': 1465.6872197418213, 'W_D_1KI': 5.288065417658628, 'J_D_1KI': 0.2736810587754181} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json new file mode 100644 index 0000000..dd2f60d --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4731, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.47292685508728, "TIME_S_1KI": 2.425053235063894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1834.0436391162873, "W": 121.03, "J_1KI": 387.66511078340466, "W_1KI": 25.582329317269078, "W_D": 85.37275, "J_D": 1293.70692465806, "W_D_1KI": 18.04539209469457, "J_D_1KI": 3.814287062924238} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output new file mode 100644 index 0000000..9c4b6e9 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.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', '100', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2614312171936035} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2042, 4085, ..., 9995953, + 9997989, 10000000]), + col_indices=tensor([ 0, 1, 5, ..., 4996, 4997, 4999]), + values=tensor([0.9986, 0.3546, 0.4267, ..., 0.5093, 0.4180, 0.9781]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.6145, 0.7478, 0.4380, ..., 0.5071, 0.9779, 0.3806]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 0.2614312171936035 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4016', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.667505025863647} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1990, 3989, ..., 9996010, + 9997994, 10000000]), + col_indices=tensor([ 1, 4, 5, ..., 4995, 4997, 4999]), + values=tensor([0.6319, 0.2996, 0.5553, ..., 0.4232, 0.5114, 0.0407]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.4664, 0.6985, 0.4117, ..., 0.0590, 0.5393, 0.9628]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 9.667505025863647 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4361', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.678797960281372} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2007, 3964, ..., 9996001, + 9997987, 10000000]), + col_indices=tensor([ 1, 7, 8, ..., 4994, 4995, 4997]), + values=tensor([0.8466, 0.2679, 0.7670, ..., 0.5879, 0.3748, 0.0373]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.1526, 0.7358, 0.4701, ..., 0.9960, 0.7430, 0.4616]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 9.678797960281372 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4731', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.47292685508728} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1970, 4010, ..., 9995993, + 9998023, 10000000]), + col_indices=tensor([ 8, 9, 11, ..., 4991, 4992, 4994]), + values=tensor([0.6655, 0.5354, 0.4656, ..., 0.4879, 0.8481, 0.6598]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.0638, 0.6144, 0.0359, ..., 0.3011, 0.2543, 0.1471]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 11.47292685508728 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, 1970, 4010, ..., 9995993, + 9998023, 10000000]), + col_indices=tensor([ 8, 9, 11, ..., 4991, 4992, 4994]), + values=tensor([0.6655, 0.5354, 0.4656, ..., 0.4879, 0.8481, 0.6598]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.0638, 0.6144, 0.0359, ..., 0.3011, 0.2543, 0.1471]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 11.47292685508728 seconds + +[39.98, 39.12, 39.4, 39.16, 39.21, 39.21, 39.11, 39.42, 39.73, 39.08] +[121.03] +15.1536283493042 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.47292685508728, 'TIME_S_1KI': 2.425053235063894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.0436391162873, 'W': 121.03} +[39.98, 39.12, 39.4, 39.16, 39.21, 39.21, 39.11, 39.42, 39.73, 39.08, 39.95, 39.19, 39.66, 39.3, 39.21, 39.17, 39.07, 44.88, 39.22, 39.16] +713.145 +35.65725 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.47292685508728, 'TIME_S_1KI': 2.425053235063894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.0436391162873, 'W': 121.03, 'J_1KI': 387.66511078340466, 'W_1KI': 25.582329317269078, 'W_D': 85.37275, 'J_D': 1293.70692465806, 'W_D_1KI': 18.04539209469457, 'J_D_1KI': 3.814287062924238} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json new file mode 100644 index 0000000..d9c77ca --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3442, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.042348861694336, "TIME_S_1KI": 2.9175911858496035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1718.164745798111, "W": 121.03, "J_1KI": 499.1762771057847, "W_1KI": 35.16269610691458, "W_D": 85.32925, "J_D": 1211.350153973341, "W_D_1KI": 24.79060139453806, "J_D_1KI": 7.202382741004667} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output new file mode 100644 index 0000000..977c5c6 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.3049776554107666} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2533, 4997, ..., 12495039, + 12497522, 12500000]), + col_indices=tensor([ 0, 3, 4, ..., 4997, 4998, 4999]), + values=tensor([0.1496, 0.2452, 0.1750, ..., 0.2661, 0.4996, 0.2189]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.2427, 0.7858, 0.7306, ..., 0.9706, 0.6342, 0.3926]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 0.3049776554107666 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3442', '-ss', '5000', '-sd', '0.5', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.042348861694336} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2398, 4864, ..., 12494963, + 12497460, 12500000]), + col_indices=tensor([ 0, 1, 3, ..., 4991, 4993, 4998]), + values=tensor([0.1337, 0.4343, 0.0128, ..., 0.8248, 0.7529, 0.8640]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.5366, 0.6388, 0.1814, ..., 0.4104, 0.8844, 0.8048]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 10.042348861694336 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, 2398, 4864, ..., 12494963, + 12497460, 12500000]), + col_indices=tensor([ 0, 1, 3, ..., 4991, 4993, 4998]), + values=tensor([0.1337, 0.4343, 0.0128, ..., 0.8248, 0.7529, 0.8640]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.5366, 0.6388, 0.1814, ..., 0.4104, 0.8844, 0.8048]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 10.042348861694336 seconds + +[40.84, 39.12, 39.49, 39.13, 39.27, 39.35, 39.11, 39.03, 39.19, 39.06] +[121.03] +14.196188926696777 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3442, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.042348861694336, 'TIME_S_1KI': 2.9175911858496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1718.164745798111, 'W': 121.03} +[40.84, 39.12, 39.49, 39.13, 39.27, 39.35, 39.11, 39.03, 39.19, 39.06, 42.71, 40.11, 39.63, 39.49, 40.35, 39.8, 39.81, 39.58, 40.7, 39.1] +714.0150000000001 +35.700750000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3442, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.042348861694336, 'TIME_S_1KI': 2.9175911858496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1718.164745798111, 'W': 121.03, 'J_1KI': 499.1762771057847, 'W_1KI': 35.16269610691458, 'W_D': 85.32925, 'J_D': 1211.350153973341, 'W_D_1KI': 24.79060139453806, 'J_D_1KI': 7.202382741004667} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json index 916740d..68d9bed 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.2704017162323, "TIME_S_1KI": 3.6125225874893774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1409.3875760412218, "W": 82.18, "J_1KI": 495.73956244854793, "W_1KI": 28.90608512135069, "W_D": 66.09875000000001, "J_D": 1133.5940258196, "W_D_1KI": 23.249648258881464, "J_D_1KI": 8.177857284165128} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2733, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.100283861160278, "TIME_S_1KI": 3.695676495119019, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1379.3402201175688, "W": 80.38, "J_1KI": 504.69821445941045, "W_1KI": 29.410903768752288, "W_D": 64.47149999999999, "J_D": 1106.3465165627001, "W_D_1KI": 23.590010976948406, "J_D_1KI": 8.631544448206515} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output index f77d6ca..dba6b8d 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output @@ -1,14 +1,14 @@ ['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', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.6929545402526855} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.8413217067718506} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 984, 2024, ..., 9998063, - 9998995, 10000000]), - col_indices=tensor([ 8, 13, 17, ..., 9976, 9985, 9991]), - values=tensor([0.9364, 0.6574, 0.1385, ..., 0.6834, 0.0920, 0.4928]), +tensor(crow_indices=tensor([ 0, 1005, 2046, ..., 9997977, + 9998998, 10000000]), + col_indices=tensor([ 2, 12, 23, ..., 9983, 9984, 9993]), + values=tensor([0.7359, 0.8841, 0.1080, ..., 0.9122, 0.7253, 0.6265]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.2645, 0.6514, 0.1258, ..., 0.8959, 0.1836, 0.1827]) +tensor([0.8932, 0.3673, 0.9234, ..., 0.2176, 0.0275, 0.3760]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,19 +16,19 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 3.6929545402526855 seconds +Time: 3.8413217067718506 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', '2843', '-ss', '10000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.2704017162323} +['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', '2733', '-ss', '10000', '-sd', '0.1', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.100283861160278} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 979, 2005, ..., 9997991, - 9998986, 10000000]), - col_indices=tensor([ 33, 43, 63, ..., 9975, 9988, 9994]), - values=tensor([0.7459, 0.7397, 0.9950, ..., 0.6626, 0.6614, 0.8057]), +tensor(crow_indices=tensor([ 0, 961, 1970, ..., 9997917, + 9998991, 10000000]), + col_indices=tensor([ 3, 10, 20, ..., 9978, 9982, 9998]), + values=tensor([0.4857, 0.9168, 0.4028, ..., 0.0388, 0.1577, 0.6588]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.0776, 0.4434, 0.3294, ..., 0.9636, 0.8443, 0.5700]) +tensor([0.1504, 0.7286, 0.9298, ..., 0.2641, 0.6031, 0.0488]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -36,16 +36,16 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 10.2704017162323 seconds +Time: 10.100283861160278 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, 979, 2005, ..., 9997991, - 9998986, 10000000]), - col_indices=tensor([ 33, 43, 63, ..., 9975, 9988, 9994]), - values=tensor([0.7459, 0.7397, 0.9950, ..., 0.6626, 0.6614, 0.8057]), +tensor(crow_indices=tensor([ 0, 961, 1970, ..., 9997917, + 9998991, 10000000]), + col_indices=tensor([ 3, 10, 20, ..., 9978, 9982, 9998]), + values=tensor([0.4857, 0.9168, 0.4028, ..., 0.0388, 0.1577, 0.6588]), size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr) -tensor([0.0776, 0.4434, 0.3294, ..., 0.9636, 0.8443, 0.5700]) +tensor([0.1504, 0.7286, 0.9298, ..., 0.2641, 0.6031, 0.0488]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -53,13 +53,13 @@ Rows: 10000 Size: 100000000 NNZ: 10000000 Density: 0.1 -Time: 10.2704017162323 seconds +Time: 10.100283861160278 seconds -[18.3, 17.96, 17.98, 17.52, 17.65, 17.88, 17.7, 17.55, 17.72, 17.81] -[82.18] -17.150007009506226 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.2704017162323, 'TIME_S_1KI': 3.6125225874893774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.3875760412218, 'W': 82.18} -[18.3, 17.96, 17.98, 17.52, 17.65, 17.88, 17.7, 17.55, 17.72, 17.81, 18.29, 17.77, 17.64, 17.69, 18.52, 18.77, 17.66, 17.68, 17.94, 17.59] -321.625 -16.08125 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.2704017162323, 'TIME_S_1KI': 3.6125225874893774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.3875760412218, 'W': 82.18, 'J_1KI': 495.73956244854793, 'W_1KI': 28.90608512135069, 'W_D': 66.09875000000001, 'J_D': 1133.5940258196, 'W_D_1KI': 23.249648258881464, 'J_D_1KI': 8.177857284165128} +[18.01, 17.45, 17.58, 17.83, 17.38, 17.63, 17.79, 17.67, 17.39, 17.59] +[80.38] +17.16024160385132 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.100283861160278, 'TIME_S_1KI': 3.695676495119019, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.3402201175688, 'W': 80.38} +[18.01, 17.45, 17.58, 17.83, 17.38, 17.63, 17.79, 17.67, 17.39, 17.59, 18.23, 17.44, 17.92, 17.53, 18.07, 17.84, 17.81, 17.59, 17.47, 17.73] +318.1700000000001 +15.908500000000004 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.100283861160278, 'TIME_S_1KI': 3.695676495119019, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1379.3402201175688, 'W': 80.38, 'J_1KI': 504.69821445941045, 'W_1KI': 29.410903768752288, 'W_D': 64.47149999999999, 'J_D': 1106.3465165627001, 'W_D_1KI': 23.590010976948406, 'J_D_1KI': 8.631544448206515} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json index df3968b..42fdfeb 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1414, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.28559684753418, "TIME_S_1KI": 7.27411375356024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2220.7405538129806, "W": 61.419999999999995, "J_1KI": 1570.5378739837204, "W_1KI": 43.437057991513434, "W_D": 45.185249999999996, "J_D": 1633.7466152585148, "W_D_1KI": 31.955622347949074, "J_D_1KI": 22.599450033910237} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.305012702941895, "TIME_S_1KI": 7.0293401793600925, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2200.8211415290834, "W": 63.38, "J_1KI": 1501.2422520662235, "W_1KI": 43.23328785811733, "W_D": 47.25450000000001, "J_D": 1640.875712091923, "W_D_1KI": 32.233628922237386, "J_D_1KI": 21.98746856905688} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output index 2d114da..cf3c9fb 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output @@ -1,14 +1,14 @@ ['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', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.425642490386963} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.160099029541016} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 2015, 4097, ..., 19995972, - 19998068, 20000000]), - col_indices=tensor([ 2, 3, 5, ..., 9990, 9995, 9998]), - values=tensor([0.5695, 0.2550, 0.6356, ..., 0.8876, 0.2921, 0.6555]), +tensor(crow_indices=tensor([ 0, 2043, 4140, ..., 19995945, + 19998003, 20000000]), + col_indices=tensor([ 9, 18, 21, ..., 9988, 9992, 9993]), + values=tensor([0.2892, 0.5577, 0.6311, ..., 0.1921, 0.3093, 0.8733]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.8689, 0.2102, 0.7438, ..., 0.4178, 0.9795, 0.1690]) +tensor([0.1991, 0.2737, 0.2500, ..., 0.3139, 0.4046, 0.9981]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,19 +16,19 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 7.425642490386963 seconds +Time: 7.160099029541016 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', '1414', '-ss', '10000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.28559684753418} +['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', '1466', '-ss', '10000', '-sd', '0.2', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.305012702941895} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1956, 4016, ..., 19996011, - 19998026, 20000000]), - col_indices=tensor([ 0, 3, 4, ..., 9992, 9993, 9997]), - values=tensor([0.4860, 0.9038, 0.0641, ..., 0.5614, 0.3748, 0.7950]), +tensor(crow_indices=tensor([ 0, 1974, 3955, ..., 19996112, + 19998028, 20000000]), + col_indices=tensor([ 4, 6, 10, ..., 9992, 9994, 9998]), + values=tensor([0.7033, 0.3961, 0.2301, ..., 0.8377, 0.1653, 0.2698]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.5984, 0.1665, 0.8658, ..., 0.8665, 0.0874, 0.1467]) +tensor([0.6095, 0.7552, 0.1154, ..., 0.2753, 0.8409, 0.9940]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -36,16 +36,16 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 10.28559684753418 seconds +Time: 10.305012702941895 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, 1956, 4016, ..., 19996011, - 19998026, 20000000]), - col_indices=tensor([ 0, 3, 4, ..., 9992, 9993, 9997]), - values=tensor([0.4860, 0.9038, 0.0641, ..., 0.5614, 0.3748, 0.7950]), +tensor(crow_indices=tensor([ 0, 1974, 3955, ..., 19996112, + 19998028, 20000000]), + col_indices=tensor([ 4, 6, 10, ..., 9992, 9994, 9998]), + values=tensor([0.7033, 0.3961, 0.2301, ..., 0.8377, 0.1653, 0.2698]), size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr) -tensor([0.5984, 0.1665, 0.8658, ..., 0.8665, 0.0874, 0.1467]) +tensor([0.6095, 0.7552, 0.1154, ..., 0.2753, 0.8409, 0.9940]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -53,13 +53,13 @@ Rows: 10000 Size: 100000000 NNZ: 20000000 Density: 0.2 -Time: 10.28559684753418 seconds +Time: 10.305012702941895 seconds -[18.83, 17.69, 17.79, 17.74, 18.11, 18.01, 17.84, 17.99, 20.88, 19.1] -[61.42] -36.15663552284241 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1414, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.28559684753418, 'TIME_S_1KI': 7.27411375356024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2220.7405538129806, 'W': 61.419999999999995} -[18.83, 17.69, 17.79, 17.74, 18.11, 18.01, 17.84, 17.99, 20.88, 19.1, 17.89, 17.55, 18.69, 17.55, 17.64, 17.53, 17.47, 17.68, 17.69, 17.87] -324.69500000000005 -16.234750000000002 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1414, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.28559684753418, 'TIME_S_1KI': 7.27411375356024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2220.7405538129806, 'W': 61.419999999999995, 'J_1KI': 1570.5378739837204, 'W_1KI': 43.437057991513434, 'W_D': 45.185249999999996, 'J_D': 1633.7466152585148, 'W_D_1KI': 31.955622347949074, 'J_D_1KI': 22.599450033910237} +[18.39, 17.53, 17.66, 17.9, 17.98, 17.61, 17.98, 18.92, 18.01, 17.69] +[63.38] +34.72422122955322 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.305012702941895, 'TIME_S_1KI': 7.0293401793600925, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2200.8211415290834, 'W': 63.38} +[18.39, 17.53, 17.66, 17.9, 17.98, 17.61, 17.98, 18.92, 18.01, 17.69, 18.14, 18.53, 17.76, 17.48, 17.87, 17.7, 17.84, 17.65, 18.06, 17.84] +322.51 +16.1255 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.305012702941895, 'TIME_S_1KI': 7.0293401793600925, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2200.8211415290834, 'W': 63.38, 'J_1KI': 1501.2422520662235, 'W_1KI': 43.23328785811733, 'W_D': 47.25450000000001, 'J_D': 1640.875712091923, 'W_D_1KI': 32.233628922237386, 'J_D_1KI': 21.98746856905688} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json index 5caf9cb..a084522 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 12.322984457015991, "TIME_S_1KI": 12.322984457015991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4274.3693664526945, "W": 53.17000000000001, "J_1KI": 4274.3693664526945, "W_1KI": 53.17000000000001, "W_D": 36.76275000000001, "J_D": 2955.380335274757, "W_D_1KI": 36.76275000000001, "J_D_1KI": 36.76275000000001} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.716556549072266, "TIME_S_1KI": 11.716556549072266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4241.666789231301, "W": 53.31, "J_1KI": 4241.666789231301, "W_1KI": 53.31, "W_D": 37.065, "J_D": 2949.116104724407, "W_D_1KI": 37.065, "J_D_1KI": 37.065} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output index 18d1cd6..565b66a 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output @@ -1,14 +1,14 @@ ['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', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 12.322984457015991} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.716556549072266} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 3028, 6045, ..., 29993985, - 29996995, 30000000]), - col_indices=tensor([ 0, 1, 2, ..., 9992, 9993, 9997]), - values=tensor([0.0070, 0.5345, 0.8585, ..., 0.9349, 0.6772, 0.9628]), +tensor(crow_indices=tensor([ 0, 3043, 6100, ..., 29993952, + 29997033, 30000000]), + col_indices=tensor([ 1, 6, 7, ..., 9991, 9996, 9999]), + values=tensor([0.2505, 0.5332, 0.4314, ..., 0.9186, 0.8523, 0.0373]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250]) +tensor([0.5706, 0.2901, 0.6021, ..., 0.8123, 0.3136, 0.2413]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -16,16 +16,16 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 12.322984457015991 seconds +Time: 11.716556549072266 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, 3028, 6045, ..., 29993985, - 29996995, 30000000]), - col_indices=tensor([ 0, 1, 2, ..., 9992, 9993, 9997]), - values=tensor([0.0070, 0.5345, 0.8585, ..., 0.9349, 0.6772, 0.9628]), +tensor(crow_indices=tensor([ 0, 3043, 6100, ..., 29993952, + 29997033, 30000000]), + col_indices=tensor([ 1, 6, 7, ..., 9991, 9996, 9999]), + values=tensor([0.2505, 0.5332, 0.4314, ..., 0.9186, 0.8523, 0.0373]), size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) -tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250]) +tensor([0.5706, 0.2901, 0.6021, ..., 0.8123, 0.3136, 0.2413]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([10000, 10000]) @@ -33,13 +33,13 @@ Rows: 10000 Size: 100000000 NNZ: 30000000 Density: 0.3 -Time: 12.322984457015991 seconds +Time: 11.716556549072266 seconds -[18.78, 17.72, 18.05, 18.08, 21.63, 19.29, 17.99, 17.89, 17.96, 17.7] -[53.17] -80.39062190055847 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 12.322984457015991, 'TIME_S_1KI': 12.322984457015991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4274.3693664526945, 'W': 53.17000000000001} -[18.78, 17.72, 18.05, 18.08, 21.63, 19.29, 17.99, 17.89, 17.96, 17.7, 18.37, 17.7, 18.04, 17.87, 17.81, 17.74, 17.89, 18.06, 17.94, 18.12] -328.145 -16.407249999999998 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 12.322984457015991, 'TIME_S_1KI': 12.322984457015991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4274.3693664526945, 'W': 53.17000000000001, 'J_1KI': 4274.3693664526945, 'W_1KI': 53.17000000000001, 'W_D': 36.76275000000001, 'J_D': 2955.380335274757, 'W_D_1KI': 36.76275000000001, 'J_D_1KI': 36.76275000000001} +[18.37, 17.73, 18.07, 17.59, 17.73, 17.59, 17.79, 17.61, 17.6, 17.94] +[53.31] +79.56606245040894 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.716556549072266, 'TIME_S_1KI': 11.716556549072266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4241.666789231301, 'W': 53.31} +[18.37, 17.73, 18.07, 17.59, 17.73, 17.59, 17.79, 17.61, 17.6, 17.94, 18.37, 17.63, 18.42, 21.66, 17.64, 17.42, 18.81, 17.54, 17.75, 17.96] +324.90000000000003 +16.245 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.716556549072266, 'TIME_S_1KI': 11.716556549072266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4241.666789231301, 'W': 53.31, 'J_1KI': 4241.666789231301, 'W_1KI': 53.31, 'W_D': 37.065, 'J_D': 2949.116104724407, 'W_D_1KI': 37.065, 'J_D_1KI': 37.065} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json index 901a9df..d4a20eb 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19209, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.589914798736572, "TIME_S_1KI": 0.5512996407276054, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1296.501946372986, "W": 88.72, "J_1KI": 67.49450499104513, "W_1KI": 4.618668332552449, "W_D": 72.5775, "J_D": 1060.6049370253086, "W_D_1KI": 3.778307043573325, "J_D_1KI": 0.19669462458083842} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 18622, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317775011062622, "TIME_S_1KI": 0.5540637424048234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1264.8904221296311, "W": 88.65, "J_1KI": 67.9245205740324, "W_1KI": 4.760498335302331, "W_D": 72.60650000000001, "J_D": 1035.9759327056408, "W_D_1KI": 3.8989635914509724, "J_D_1KI": 0.2093740517372448} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output index ac30e09..3a93b23 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output @@ -1,14 +1,14 @@ ['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', '5000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5466129779815674} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5638444423675537} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 514, 1026, ..., 2498984, - 2499497, 2500000]), - col_indices=tensor([ 4, 26, 34, ..., 4975, 4994, 4997]), - values=tensor([0.5421, 0.0550, 0.0297, ..., 0.2626, 0.0439, 0.1648]), +tensor(crow_indices=tensor([ 0, 491, 1004, ..., 2498969, + 2499500, 2500000]), + col_indices=tensor([ 22, 38, 49, ..., 4962, 4970, 4976]), + values=tensor([0.5529, 0.3874, 0.4848, ..., 0.5629, 0.9931, 0.9487]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.7995, 0.3197, 0.3485, ..., 0.5295, 0.0131, 0.9723]) +tensor([0.1713, 0.2154, 0.4105, ..., 0.0933, 0.5075, 0.3878]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 0.5466129779815674 seconds +Time: 0.5638444423675537 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', '19209', '-ss', '5000', '-sd', '0.1', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.589914798736572} +['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', '18622', '-ss', '5000', '-sd', '0.1', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317775011062622} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 503, 1003, ..., 2498928, - 2499468, 2500000]), - col_indices=tensor([ 6, 8, 21, ..., 4933, 4958, 4973]), - values=tensor([0.5143, 0.8442, 0.2205, ..., 0.0567, 0.9724, 0.7726]), +tensor(crow_indices=tensor([ 0, 525, 987, ..., 2498981, + 2499483, 2500000]), + col_indices=tensor([ 8, 19, 22, ..., 4987, 4993, 4999]), + values=tensor([0.0390, 0.1537, 0.3143, ..., 0.0698, 0.3942, 0.2310]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.8237, 0.2559, 0.0746, ..., 0.2976, 0.1284, 0.3075]) +tensor([0.3040, 0.7679, 0.7012, ..., 0.6231, 0.1253, 0.7846]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,16 +36,16 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 10.589914798736572 seconds +Time: 10.317775011062622 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, 503, 1003, ..., 2498928, - 2499468, 2500000]), - col_indices=tensor([ 6, 8, 21, ..., 4933, 4958, 4973]), - values=tensor([0.5143, 0.8442, 0.2205, ..., 0.0567, 0.9724, 0.7726]), +tensor(crow_indices=tensor([ 0, 525, 987, ..., 2498981, + 2499483, 2500000]), + col_indices=tensor([ 8, 19, 22, ..., 4987, 4993, 4999]), + values=tensor([0.0390, 0.1537, 0.3143, ..., 0.0698, 0.3942, 0.2310]), size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr) -tensor([0.8237, 0.2559, 0.0746, ..., 0.2976, 0.1284, 0.3075]) +tensor([0.3040, 0.7679, 0.7012, ..., 0.6231, 0.1253, 0.7846]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -53,13 +53,13 @@ Rows: 5000 Size: 25000000 NNZ: 2500000 Density: 0.1 -Time: 10.589914798736572 seconds +Time: 10.317775011062622 seconds -[18.45, 18.0, 17.83, 17.78, 17.91, 17.57, 18.07, 17.82, 17.87, 17.88] -[88.72] -14.613412380218506 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19209, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.589914798736572, 'TIME_S_1KI': 0.5512996407276054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.501946372986, 'W': 88.72} -[18.45, 18.0, 17.83, 17.78, 17.91, 17.57, 18.07, 17.82, 17.87, 17.88, 18.16, 17.97, 18.01, 17.95, 17.55, 18.45, 18.51, 17.65, 17.73, 17.87] -322.85 -16.142500000000002 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19209, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.589914798736572, 'TIME_S_1KI': 0.5512996407276054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.501946372986, 'W': 88.72, 'J_1KI': 67.49450499104513, 'W_1KI': 4.618668332552449, 'W_D': 72.5775, 'J_D': 1060.6049370253086, 'W_D_1KI': 3.778307043573325, 'J_D_1KI': 0.19669462458083842} +[18.14, 17.77, 17.8, 18.17, 17.99, 17.63, 17.94, 18.68, 17.91, 17.77] +[88.65] +14.26836347579956 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317775011062622, 'TIME_S_1KI': 0.5540637424048234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.8904221296311, 'W': 88.65} +[18.14, 17.77, 17.8, 18.17, 17.99, 17.63, 17.94, 18.68, 17.91, 17.77, 17.91, 17.46, 17.97, 17.49, 17.74, 17.66, 17.77, 17.61, 17.59, 17.56] +320.87 +16.0435 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317775011062622, 'TIME_S_1KI': 0.5540637424048234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.8904221296311, 'W': 88.65, 'J_1KI': 67.9245205740324, 'W_1KI': 4.760498335302331, 'W_D': 72.60650000000001, 'J_D': 1035.9759327056408, 'W_D_1KI': 3.8989635914509724, 'J_D_1KI': 0.2093740517372448} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json index 54582f6..1f5daa4 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8907, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.383960962295532, "TIME_S_1KI": 1.1658202494998913, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.8042968559266, "W": 87.12, "J_1KI": 145.7061072028659, "W_1KI": 9.781071067699562, "W_D": 70.95125, "J_D": 1056.9425748082995, "W_D_1KI": 7.965785337375098, "J_D_1KI": 0.8943286558184684} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8928, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.436826467514038, "TIME_S_1KI": 1.1689993803219128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1316.4306947374344, "W": 86.99, "J_1KI": 147.4496745897664, "W_1KI": 9.743503584229389, "W_D": 70.63274999999999, "J_D": 1068.894357440114, "W_D_1KI": 7.911374327956988, "J_D_1KI": 0.8861306370919565} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output index f373582..73819ef 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output @@ -1,14 +1,14 @@ ['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', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.1788151264190674} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.1760613918304443} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1020, 2047, ..., 4997993, - 4999046, 5000000]), - col_indices=tensor([ 0, 5, 11, ..., 4968, 4973, 4981]), - values=tensor([0.5537, 0.3394, 0.2735, ..., 0.5110, 0.5845, 0.1364]), +tensor(crow_indices=tensor([ 0, 978, 2025, ..., 4997968, + 4998985, 5000000]), + col_indices=tensor([ 2, 4, 11, ..., 4986, 4990, 4994]), + values=tensor([0.0122, 0.4949, 0.8008, ..., 0.1011, 0.5261, 0.6800]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.2008, 0.9176, 0.6130, ..., 0.2299, 0.3337, 0.1107]) +tensor([0.4016, 0.7392, 0.9091, ..., 0.8685, 0.7503, 0.4791]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 1.1788151264190674 seconds +Time: 1.1760613918304443 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', '8907', '-ss', '5000', '-sd', '0.2', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.383960962295532} +['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', '8928', '-ss', '5000', '-sd', '0.2', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.436826467514038} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1043, 2083, ..., 4998055, - 4999016, 5000000]), - col_indices=tensor([ 11, 19, 21, ..., 4992, 4995, 4997]), - values=tensor([0.8807, 0.7903, 0.9771, ..., 0.4816, 0.8305, 0.1823]), +tensor(crow_indices=tensor([ 0, 990, 1955, ..., 4998009, + 4998983, 5000000]), + col_indices=tensor([ 11, 14, 16, ..., 4997, 4998, 4999]), + values=tensor([0.2669, 0.2498, 0.4510, ..., 0.5817, 0.0466, 0.6772]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.8731, 0.0343, 0.1598, ..., 0.1067, 0.9050, 0.6111]) +tensor([0.9945, 0.9638, 0.1643, ..., 0.2698, 0.5025, 0.0642]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,16 +36,16 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 10.383960962295532 seconds +Time: 10.436826467514038 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, 1043, 2083, ..., 4998055, - 4999016, 5000000]), - col_indices=tensor([ 11, 19, 21, ..., 4992, 4995, 4997]), - values=tensor([0.8807, 0.7903, 0.9771, ..., 0.4816, 0.8305, 0.1823]), +tensor(crow_indices=tensor([ 0, 990, 1955, ..., 4998009, + 4998983, 5000000]), + col_indices=tensor([ 11, 14, 16, ..., 4997, 4998, 4999]), + values=tensor([0.2669, 0.2498, 0.4510, ..., 0.5817, 0.0466, 0.6772]), size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr) -tensor([0.8731, 0.0343, 0.1598, ..., 0.1067, 0.9050, 0.6111]) +tensor([0.9945, 0.9638, 0.1643, ..., 0.2698, 0.5025, 0.0642]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -53,13 +53,13 @@ Rows: 5000 Size: 25000000 NNZ: 5000000 Density: 0.2 -Time: 10.383960962295532 seconds +Time: 10.436826467514038 seconds -[17.98, 21.91, 18.08, 17.67, 17.68, 17.88, 18.1, 17.78, 17.61, 17.52] -[87.12] -14.896743535995483 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.383960962295532, 'TIME_S_1KI': 1.1658202494998913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8042968559266, 'W': 87.12} -[17.98, 21.91, 18.08, 17.67, 17.68, 17.88, 18.1, 17.78, 17.61, 17.52, 18.28, 17.97, 17.57, 17.52, 17.69, 17.59, 17.67, 17.39, 17.74, 17.27] -323.375 -16.16875 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.383960962295532, 'TIME_S_1KI': 1.1658202494998913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8042968559266, 'W': 87.12, 'J_1KI': 145.7061072028659, 'W_1KI': 9.781071067699562, 'W_D': 70.95125, 'J_D': 1056.9425748082995, 'W_D_1KI': 7.965785337375098, 'J_D_1KI': 0.8943286558184684} +[18.18, 17.97, 22.4, 18.19, 17.69, 18.6, 17.87, 17.63, 17.75, 17.93] +[86.99] +15.133126735687256 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.436826467514038, 'TIME_S_1KI': 1.1689993803219128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.4306947374344, 'W': 86.99} +[18.18, 17.97, 22.4, 18.19, 17.69, 18.6, 17.87, 17.63, 17.75, 17.93, 18.01, 17.97, 18.36, 17.9, 17.82, 17.52, 18.18, 17.58, 17.7, 17.91] +327.145 +16.35725 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.436826467514038, 'TIME_S_1KI': 1.1689993803219128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.4306947374344, 'W': 86.99, 'J_1KI': 147.4496745897664, 'W_1KI': 9.743503584229389, 'W_D': 70.63274999999999, 'J_D': 1068.894357440114, 'W_D_1KI': 7.911374327956988, 'J_D_1KI': 0.8861306370919565} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json index 457cdda..f34db05 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json @@ -1 +1 @@ -{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5572, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.464208841323853, "TIME_S_1KI": 1.8779987152411795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1351.3364907836915, "W": 85.76, "J_1KI": 242.5227011456733, "W_1KI": 15.391241923905243, "W_D": 69.66475, "J_D": 1097.7206016362309, "W_D_1KI": 12.502647164393395, "J_D_1KI": 2.2438347387640696} +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5478, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.325071811676025, "TIME_S_1KI": 1.8848250842782084, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1339.3279536104203, "W": 85.87, "J_1KI": 244.49214195151885, "W_1KI": 15.675428988682002, "W_D": 69.74000000000001, "J_D": 1087.7457957935335, "W_D_1KI": 12.730923694779118, "J_D_1KI": 2.3240094367979403} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output index 0e365cf..b031969 100644 --- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output @@ -1,14 +1,14 @@ ['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', '5000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.8840982913970947} +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9164557456970215} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1512, 3045, ..., 7497001, - 7498469, 7500000]), - col_indices=tensor([ 1, 3, 6, ..., 4985, 4987, 4999]), - values=tensor([0.2926, 0.1712, 0.0979, ..., 0.0306, 0.4197, 0.0742]), +tensor(crow_indices=tensor([ 0, 1507, 2972, ..., 7496967, + 7498514, 7500000]), + col_indices=tensor([ 0, 1, 7, ..., 4979, 4980, 4982]), + values=tensor([0.5806, 0.6588, 0.4500, ..., 0.8030, 0.5443, 0.1304]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.8071, 0.3432, 0.7096, ..., 0.6319, 0.9579, 0.4287]) +tensor([0.5421, 0.7085, 0.6995, ..., 0.1955, 0.6605, 0.4332]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -16,19 +16,19 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 1.8840982913970947 seconds +Time: 1.9164557456970215 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', '5572', '-ss', '5000', '-sd', '0.3', '-c', '16'] -{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.464208841323853} +['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', '5478', '-ss', '5000', '-sd', '0.3', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.325071811676025} /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) matrix = matrix.to_sparse_csr().type(torch.float32) -tensor(crow_indices=tensor([ 0, 1440, 2946, ..., 7496997, - 7498526, 7500000]), - col_indices=tensor([ 4, 11, 17, ..., 4977, 4981, 4999]), - values=tensor([0.5270, 0.2118, 0.1968, ..., 0.5430, 0.9062, 0.9328]), +tensor(crow_indices=tensor([ 0, 1469, 2909, ..., 7496983, + 7498509, 7500000]), + col_indices=tensor([ 2, 8, 9, ..., 4995, 4997, 4998]), + values=tensor([0.1494, 0.3197, 0.4986, ..., 0.6801, 0.8207, 0.3086]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066]) +tensor([0.0051, 0.9261, 0.2746, ..., 0.2239, 0.8025, 0.6653]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -36,16 +36,16 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 10.464208841323853 seconds +Time: 10.325071811676025 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, 1440, 2946, ..., 7496997, - 7498526, 7500000]), - col_indices=tensor([ 4, 11, 17, ..., 4977, 4981, 4999]), - values=tensor([0.5270, 0.2118, 0.1968, ..., 0.5430, 0.9062, 0.9328]), +tensor(crow_indices=tensor([ 0, 1469, 2909, ..., 7496983, + 7498509, 7500000]), + col_indices=tensor([ 2, 8, 9, ..., 4995, 4997, 4998]), + values=tensor([0.1494, 0.3197, 0.4986, ..., 0.6801, 0.8207, 0.3086]), size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) -tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066]) +tensor([0.0051, 0.9261, 0.2746, ..., 0.2239, 0.8025, 0.6653]) Matrix Type: synthetic Matrix Format: csr Shape: torch.Size([5000, 5000]) @@ -53,13 +53,13 @@ Rows: 5000 Size: 25000000 NNZ: 7500000 Density: 0.3 -Time: 10.464208841323853 seconds +Time: 10.325071811676025 seconds -[18.1, 18.05, 18.13, 17.56, 18.21, 17.81, 17.97, 17.66, 18.07, 17.92] -[85.76] -15.757188558578491 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5572, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.464208841323853, 'TIME_S_1KI': 1.8779987152411795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.3364907836915, 'W': 85.76} -[18.1, 18.05, 18.13, 17.56, 18.21, 17.81, 17.97, 17.66, 18.07, 17.92, 17.91, 17.43, 18.3, 17.79, 17.88, 17.63, 18.23, 17.64, 17.78, 17.6] -321.905 -16.09525 -{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5572, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.464208841323853, 'TIME_S_1KI': 1.8779987152411795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.3364907836915, 'W': 85.76, 'J_1KI': 242.5227011456733, 'W_1KI': 15.391241923905243, 'W_D': 69.66475, 'J_D': 1097.7206016362309, 'W_D_1KI': 12.502647164393395, 'J_D_1KI': 2.2438347387640696} +[18.13, 17.6, 17.78, 17.86, 17.7, 17.85, 17.51, 17.77, 17.69, 17.5] +[85.87] +15.597157955169678 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.325071811676025, 'TIME_S_1KI': 1.8848250842782084, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.3279536104203, 'W': 85.87} +[18.13, 17.6, 17.78, 17.86, 17.7, 17.85, 17.51, 17.77, 17.69, 17.5, 18.03, 17.39, 17.78, 17.59, 17.86, 17.49, 17.59, 17.65, 21.57, 18.18] +322.6 +16.130000000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.325071811676025, 'TIME_S_1KI': 1.8848250842782084, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.3279536104203, 'W': 85.87, 'J_1KI': 244.49214195151885, 'W_1KI': 15.675428988682002, 'W_D': 69.74000000000001, 'J_D': 1087.7457957935335, 'W_D_1KI': 12.730923694779118, 'J_D_1KI': 2.3240094367979403} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json new file mode 100644 index 0000000..e67ee55 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2943, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.241667032241821, "TIME_S_1KI": 3.819798515882372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1393.030681221485, "W": 83.67, "J_1KI": 473.3369626984319, "W_1KI": 28.430173292558617, "W_D": 67.793, "J_D": 1128.6928286368848, "W_D_1KI": 23.035338090383963, "J_D_1KI": 7.82716211022221} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output new file mode 100644 index 0000000..6fefdcc --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.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', '100', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.38892388343811035} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2020, 4067, ..., 9996060, + 9998056, 10000000]), + col_indices=tensor([ 1, 4, 6, ..., 4992, 4998, 4999]), + values=tensor([0.0057, 0.0348, 0.0719, ..., 0.9587, 0.3044, 0.9978]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.1784, 0.7215, 0.7937, ..., 0.4492, 0.0838, 0.9744]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 0.38892388343811035 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', '2699', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.626921653747559} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1992, 4044, ..., 9995930, + 9997986, 10000000]), + col_indices=tensor([ 0, 5, 10, ..., 4990, 4997, 4999]), + values=tensor([0.5930, 0.1714, 0.3969, ..., 0.4489, 0.5630, 0.4403]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.5546, 0.5609, 0.4280, ..., 0.4447, 0.9246, 0.2629]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 9.626921653747559 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', '2943', '-ss', '5000', '-sd', '0.4', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.241667032241821} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 1928, 3951, ..., 9996029, + 9997995, 10000000]), + col_indices=tensor([ 1, 3, 6, ..., 4994, 4996, 4997]), + values=tensor([0.0792, 0.4043, 0.0506, ..., 0.1635, 0.6350, 0.0332]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.4326, 0.5030, 0.1276, ..., 0.3674, 0.6623, 0.2020]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 11.241667032241821 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, 1928, 3951, ..., 9996029, + 9997995, 10000000]), + col_indices=tensor([ 1, 3, 6, ..., 4994, 4996, 4997]), + values=tensor([0.0792, 0.4043, 0.0506, ..., 0.1635, 0.6350, 0.0332]), + size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr) +tensor([0.4326, 0.5030, 0.1276, ..., 0.3674, 0.6623, 0.2020]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 10000000 +Density: 0.4 +Time: 11.241667032241821 seconds + +[18.06, 17.64, 17.57, 17.56, 18.11, 17.49, 17.52, 17.53, 17.66, 17.49] +[83.67] +16.64910578727722 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2943, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.241667032241821, 'TIME_S_1KI': 3.819798515882372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1393.030681221485, 'W': 83.67} +[18.06, 17.64, 17.57, 17.56, 18.11, 17.49, 17.52, 17.53, 17.66, 17.49, 17.99, 17.7, 17.78, 17.54, 17.51, 17.38, 17.97, 17.59, 17.4, 17.64] +317.53999999999996 +15.876999999999999 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2943, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.241667032241821, 'TIME_S_1KI': 3.819798515882372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1393.030681221485, 'W': 83.67, 'J_1KI': 473.3369626984319, 'W_1KI': 28.430173292558617, 'W_D': 67.793, 'J_D': 1128.6928286368848, 'W_D_1KI': 23.035338090383963, 'J_D_1KI': 7.82716211022221} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json new file mode 100644 index 0000000..73055e9 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2293, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.488942384719849, "TIME_S_1KI": 4.5743316113039025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1464.8316407203674, "W": 79.5, "J_1KI": 638.827579904216, "W_1KI": 34.67073702573049, "W_D": 63.46325, "J_D": 1169.3456178987026, "W_D_1KI": 27.676951591801135, "J_D_1KI": 12.0701925825561} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output new file mode 100644 index 0000000..90cfd63 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.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', '100', '-ss', '5000', '-sd', '0.5', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.49056053161621094} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2534, 5049, ..., 12494993, + 12497525, 12500000]), + col_indices=tensor([ 2, 3, 7, ..., 4995, 4997, 4999]), + values=tensor([0.8256, 0.5002, 0.0945, ..., 0.8539, 0.0059, 0.0754]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.6389, 0.8699, 0.2648, ..., 0.6441, 0.5427, 0.0050]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 0.49056053161621094 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', '2140', '-ss', '5000', '-sd', '0.5', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.796798944473267} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2473, 4961, ..., 12495026, + 12497501, 12500000]), + col_indices=tensor([ 0, 1, 2, ..., 4993, 4995, 4998]), + values=tensor([0.3292, 0.8142, 0.0983, ..., 0.8374, 0.1805, 0.7414]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.5256, 0.8051, 0.2947, ..., 0.7660, 0.3711, 0.2787]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 9.796798944473267 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', '2293', '-ss', '5000', '-sd', '0.5', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.488942384719849} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 2483, 4907, ..., 12494946, + 12497460, 12500000]), + col_indices=tensor([ 3, 6, 8, ..., 4993, 4997, 4999]), + values=tensor([0.9265, 0.6407, 0.1426, ..., 0.0639, 0.3199, 0.5517]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.6629, 0.1553, 0.8462, ..., 0.7358, 0.1133, 0.5218]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 10.488942384719849 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, 2483, 4907, ..., 12494946, + 12497460, 12500000]), + col_indices=tensor([ 3, 6, 8, ..., 4993, 4997, 4999]), + values=tensor([0.9265, 0.6407, 0.1426, ..., 0.0639, 0.3199, 0.5517]), + size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr) +tensor([0.6629, 0.1553, 0.8462, ..., 0.7358, 0.1133, 0.5218]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 12500000 +Density: 0.5 +Time: 10.488942384719849 seconds + +[18.25, 17.84, 17.64, 17.31, 17.75, 17.54, 17.53, 17.59, 17.7, 17.45] +[79.5] +18.42555522918701 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2293, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.488942384719849, 'TIME_S_1KI': 4.5743316113039025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1464.8316407203674, 'W': 79.5} +[18.25, 17.84, 17.64, 17.31, 17.75, 17.54, 17.53, 17.59, 17.7, 17.45, 18.03, 17.47, 17.56, 17.58, 17.59, 17.37, 21.25, 17.8, 17.44, 17.82] +320.735 +16.03675 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2293, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.488942384719849, 'TIME_S_1KI': 4.5743316113039025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1464.8316407203674, 'W': 79.5, 'J_1KI': 638.827579904216, 'W_1KI': 34.67073702573049, 'W_D': 63.46325, 'J_D': 1169.3456178987026, 'W_D_1KI': 27.676951591801135, 'J_D_1KI': 12.0701925825561} diff --git a/pytorch/synthetic_densities b/pytorch/synthetic_densities index 6084129..45681ce 100644 --- a/pytorch/synthetic_densities +++ b/pytorch/synthetic_densities @@ -6,3 +6,6 @@ 0.05 0.1 0.2 +0.3 +0.4 +0.5