From a512a4dadce2c263a048fbd97f7548208c5feeea Mon Sep 17 00:00:00 2001 From: cephi Date: Tue, 17 Dec 2024 13:27:18 -0500 Subject: [PATCH] Added more synthetic data! --- ...a_16_csr_10_10_10_synthetic_10000_0.3.json | 1 + ...16_csr_10_10_10_synthetic_10000_0.3.output | 45 ++++++++++ ..._csr_10_10_10_synthetic_500000_0.0001.json | 1 + ...sr_10_10_10_synthetic_500000_0.0001.output | 47 ++++++++++ ..._16_csr_10_10_10_synthetic_50000_0.01.json | 1 + ...6_csr_10_10_10_synthetic_50000_0.01.output | 45 ++++++++++ ...ra_16_csr_10_10_10_synthetic_5000_0.3.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.3.output | 45 ++++++++++ ...p_16_csr_10_10_10_synthetic_10000_0.3.json | 1 + ...16_csr_10_10_10_synthetic_10000_0.3.output | 65 ++++++++++++++ ..._csr_10_10_10_synthetic_500000_0.0001.json | 1 + ...sr_10_10_10_synthetic_500000_0.0001.output | 89 +++++++++++++++++++ ..._16_csr_10_10_10_synthetic_50000_0.01.json | 1 + ...6_csr_10_10_10_synthetic_50000_0.01.output | 65 ++++++++++++++ ...3p_16_csr_10_10_10_synthetic_5000_0.3.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.3.output | 85 ++++++++++++++++++ ...6_16_csr_10_10_10_synthetic_10000_0.3.json | 1 + ...16_csr_10_10_10_synthetic_10000_0.3.output | 45 ++++++++++ ..._csr_10_10_10_synthetic_500000_0.0001.json | 1 + ...sr_10_10_10_synthetic_500000_0.0001.output | 47 ++++++++++ ..._16_csr_10_10_10_synthetic_50000_0.01.json | 1 + ...6_csr_10_10_10_synthetic_50000_0.01.output | 45 ++++++++++ ...16_16_csr_10_10_10_synthetic_5000_0.3.json | 1 + ..._16_csr_10_10_10_synthetic_5000_0.3.output | 65 ++++++++++++++ 24 files changed, 700 insertions(+) create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output 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 new file mode 100644 index 0000000..28452bf --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 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} 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 new file mode 100644 index 0000000..90f3629 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.3 -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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 644.013694524765 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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.8581, 0.1742, 0.9495, ..., 0.9731, 0.2987, 0.5882]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 644.013694524765 seconds + +[20.6, 20.56, 20.52, 20.4, 20.44, 20.64, 20.76, 20.6, 20.68, 20.6] +[20.76, 20.88, 20.88, 21.56, 23.0, 25.4, 26.16, 29.0, 30.28, 29.24, 29.48, 30.24, 28.4, 28.36, 28.36, 27.08, 27.08, 26.28, 24.52, 24.16, 23.96, 24.0, 24.16, 24.28, 24.4, 24.36, 24.16, 24.28, 24.28, 24.24, 24.28, 24.52, 24.56, 24.68, 24.56, 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'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} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json new file mode 100644 index 0000000..8015441 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654, "TIME_S_1KI": 78.25872588157654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4354.838327121737, "W": 46.22946318790178, "J_1KI": 4354.838327121737, "W_1KI": 46.22946318790178, "W_D": 27.22746318790178, "J_D": 2564.8405165128734, "W_D_1KI": 27.22746318790178, "J_D_1KI": 27.22746318790178} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output new file mode 100644 index 0000000..f9d34b2 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 0.0001 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 62, 108, ..., 24999902, + 24999957, 25000000]), + col_indices=tensor([ 8122, 13146, 17492, ..., 475877, 485043, + 496973]), + values=tensor([0.3209, 0.8894, 0.3965, ..., 0.0269, 0.4047, 0.8747]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.9161, 0.4114, 0.3584, ..., 0.3416, 0.2120, 0.9282]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 78.25872588157654 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, 62, 108, ..., 24999902, + 24999957, 25000000]), + col_indices=tensor([ 8122, 13146, 17492, ..., 475877, 485043, + 496973]), + values=tensor([0.3209, 0.8894, 0.3965, ..., 0.0269, 0.4047, 0.8747]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.9161, 0.4114, 0.3584, ..., 0.3416, 0.2120, 0.9282]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 78.25872588157654 seconds + +[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8] +[20.96, 20.88, 21.6, 21.6, 22.56, 24.32, 26.6, 27.68, 30.32, 31.76, 31.8, 31.36, 31.8, 32.64, 38.12, 43.32, 49.08, 53.24, 53.16, 52.56, 52.56, 52.56, 52.6, 52.28, 52.68, 53.0, 53.04, 53.24, 53.08, 53.48, 53.36, 53.16, 53.16, 52.68, 52.84, 52.88, 53.12, 52.96, 52.88, 53.2, 53.16, 53.0, 53.0, 52.96, 53.12, 52.84, 52.76, 53.08, 52.96, 52.84, 52.96, 52.92, 53.12, 53.2, 53.04, 53.28, 53.16, 53.12, 52.68, 52.96, 52.88, 52.72, 53.04, 53.08, 52.76, 52.76, 52.92, 53.32, 53.28, 53.32, 53.44, 53.28, 53.32, 53.4, 53.48, 53.48, 53.4, 53.4, 53.44, 53.12, 53.32, 53.44, 53.56, 53.52, 53.4, 53.36, 53.12, 53.12, 53.12, 53.16] +94.20049524307251 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178} +[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8, 21.64, 21.52, 21.28, 21.2, 21.12, 20.8, 20.84, 20.76, 21.0, 21.08] +380.03999999999996 +19.002 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178, 'J_1KI': 4354.838327121737, 'W_1KI': 46.22946318790178, 'W_D': 27.22746318790178, 'J_D': 2564.8405165128734, 'W_D_1KI': 27.22746318790178, 'J_D_1KI': 27.22746318790178} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..cc8d9c0 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317, "TIME_S_1KI": 324.79648518562317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11877.425473241823, "W": 35.646067141867775, "J_1KI": 11877.425473241823, "W_1KI": 35.646067141867775, "W_D": 16.970067141867776, "J_D": 5654.50059192373, "W_D_1KI": 16.970067141867776, "J_D_1KI": 16.970067141867776} diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..b2a5634 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 495, 976, ..., 24999061, + 24999546, 25000000]), + col_indices=tensor([ 151, 490, 496, ..., 49361, 49363, 49505]), + values=tensor([0.8177, 0.6830, 0.3837, ..., 0.7178, 0.0658, 0.9045]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.7632, 0.2578, 0.8207, ..., 0.6267, 0.5426, 0.4264]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 324.79648518562317 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, 495, 976, ..., 24999061, + 24999546, 25000000]), + col_indices=tensor([ 151, 490, 496, ..., 49361, 49363, 49505]), + values=tensor([0.8177, 0.6830, 0.3837, ..., 0.7178, 0.0658, 0.9045]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.7632, 0.2578, 0.8207, ..., 0.6267, 0.5426, 0.4264]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 324.79648518562317 seconds + +[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88] +[21.16, 21.04, 21.12, 26.2, 28.16, 29.12, 31.64, 29.84, 30.96, 30.96, 31.32, 31.32, 31.52, 31.84, 31.92, 33.6, 35.32, 36.84, 37.0, 36.88, 36.84, 36.12, 36.76, 36.76, 36.4, 37.24, 37.32, 37.12, 37.84, 37.0, 37.76, 37.32, 37.0, 35.96, 36.28, 35.4, 35.8, 36.36, 37.12, 37.12, 38.8, 38.96, 38.72, 38.44, 37.4, 38.36, 38.0, 38.04, 37.84, 37.56, 36.92, 36.92, 36.84, 36.48, 36.64, 36.64, 36.52, 35.64, 36.12, 36.84, 37.56, 37.52, 38.6, 37.96, 37.32, 37.44, 36.76, 37.36, 37.44, 37.76, 37.8, 37.8, 38.68, 38.12, 37.96, 37.72, 37.88, 37.92, 37.84, 36.92, 36.6, 36.4, 36.48, 36.4, 37.2, 37.04, 37.16, 36.8, 36.8, 36.72, 37.72, 38.0, 37.96, 37.76, 37.08, 37.48, 36.64, 36.8, 36.88, 37.2, 37.24, 37.44, 37.48, 38.04, 38.04, 38.16, 38.68, 37.96, 38.24, 37.16, 36.68, 36.84, 36.88, 37.16, 37.76, 37.92, 37.76, 37.56, 36.52, 36.0, 37.0, 36.44, 36.36, 36.68, 37.08, 37.4, 37.24, 37.32, 36.6, 36.2, 37.16, 37.32, 37.6, 37.6, 37.6, 37.56, 37.56, 37.24, 36.48, 36.28, 36.48, 36.64, 37.68, 38.24, 37.72, 37.64, 38.24, 37.6, 37.0, 37.0, 36.88, 36.88, 37.28, 38.48, 39.08, 38.28, 38.04, 37.48, 36.64, 36.72, 36.84, 36.84, 37.2, 37.36, 37.76, 37.96, 38.24, 37.88, 37.88, 37.12, 37.6, 36.76, 37.52, 37.68, 36.76, 37.72, 37.48, 38.04, 37.88, 37.6, 37.56, 36.96, 37.0, 37.0, 37.92, 37.08, 37.44, 36.8, 36.84, 36.08, 36.52, 36.48, 36.56, 36.84, 37.2, 37.36, 36.52, 37.0, 36.12, 36.12, 37.0, 36.68, 36.88, 37.56, 37.72, 38.36, 38.2, 38.48, 38.72, 38.36, 38.28, 37.96, 37.76, 36.36, 37.0, 36.48, 36.52, 36.52, 37.16, 36.6, 36.52, 36.6, 37.52, 37.12, 37.8, 37.88, 37.04, 36.64, 36.44, 36.04, 36.32, 37.68, 37.88, 37.88, 38.04, 38.04, 37.68, 37.92, 37.96, 36.92, 37.64, 36.4, 36.32, 36.4, 36.32, 36.2, 37.04, 37.16, 37.68, 37.64, 37.64, 38.12, 38.04, 37.64, 37.24, 36.56, 36.48, 37.28, 36.6, 36.44, 37.08, 37.08, 36.56, 37.48, 38.08, 37.2, 37.2, 36.96, 36.72, 36.64, 36.24, 37.32, 37.96, 38.2, 38.28, 38.36, 38.36, 37.88, 38.36, 37.64, 36.88, 36.88, 37.2, 36.4, 36.52, 37.2, 37.52, 37.44, 36.8, 37.48, 36.92, 37.32, 38.0, 37.76, 36.72, 37.84, 37.64, 37.64, 38.32, 38.32, 37.88, 38.16, 38.24, 37.64, 37.76, 37.12, 37.04, 36.24, 36.68, 36.4, 36.6, 36.6, 36.64, 37.16, 38.08, 37.28, 37.28, 37.24, 36.52, 36.4, 36.88, 36.2, 36.92] +333.20437359809875 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775} +[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88, 20.44, 20.44, 20.64, 20.68, 20.68, 20.68, 20.68, 20.84, 20.88, 20.88] +373.52 +18.676 +{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775, 'J_1KI': 11877.425473241823, 'W_1KI': 35.646067141867775, 'W_D': 16.970067141867776, 'J_D': 5654.50059192373, 'W_D_1KI': 16.970067141867776, 'J_D_1KI': 16.970067141867776} 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 new file mode 100644 index 0000000..3d5cf8e --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json @@ -0,0 +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} 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 new file mode 100644 index 0000000..c0efb69 --- /dev/null +++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 161.33362221717834 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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.9065, 0.9509, 0.6692, ..., 0.3115, 0.1768, 0.8689]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 161.33362221717834 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} 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 new file mode 100644 index 0000000..0688fa1 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json @@ -0,0 +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} 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 new file mode 100644 index 0000000..cc1271e --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.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', '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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.8128, 0.9693, 0.2211, ..., 0.0242, 0.9676, 0.8056]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 7.375466823577881 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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 10.225417137145996 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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.6847, 0.1599, 0.7266, ..., 0.4640, 0.6124, 0.9614]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 10.225417137145996 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} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json new file mode 100644 index 0000000..f4ead28 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1316, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738, "TIME_S_1KI": 7.964192915107703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2349.371359462738, "W": 119.72, "J_1KI": 1785.2365953364272, "W_1KI": 90.9726443768997, "W_D": 83.6515, "J_D": 1641.5673093559742, "W_D_1KI": 63.564969604863215, "J_D_1KI": 48.3016486359143} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output new file mode 100644 index 0000000..a121e20 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output @@ -0,0 +1,89 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.476217031478882} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 58, 110, ..., 24999904, + 24999948, 25000000]), + col_indices=tensor([ 6107, 36475, 44542, ..., 455197, 482838, + 484709]), + values=tensor([0.8741, 0.1087, 0.7265, ..., 0.9387, 0.2139, 0.8984]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.2008, 0.1464, 0.5363, ..., 0.5258, 0.1478, 0.0153]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 8.476217031478882 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1238', '-ss', '500000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.872223138809204} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 42, 103, ..., 24999895, + 24999951, 25000000]), + col_indices=tensor([ 1782, 32597, 35292, ..., 490788, 494408, + 495086]), + values=tensor([0.7532, 0.4055, 0.7849, ..., 0.0826, 0.2837, 0.9366]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.6389, 0.8135, 0.6286, ..., 0.0387, 0.4513, 0.2151]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 9.872223138809204 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1316', '-ss', '500000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 93, ..., 24999907, + 24999956, 25000000]), + col_indices=tensor([ 1315, 9605, 36829, ..., 467295, 483577, + 490282]), + values=tensor([0.4504, 0.6377, 0.8962, ..., 0.8735, 0.2973, 0.1824]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.1673, 0.3950, 0.6065, ..., 0.2020, 0.8580, 0.9739]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 10.480877876281738 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 93, ..., 24999907, + 24999956, 25000000]), + col_indices=tensor([ 1315, 9605, 36829, ..., 467295, 483577, + 490282]), + values=tensor([0.4504, 0.6377, 0.8962, ..., 0.8735, 0.2973, 0.1824]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.1673, 0.3950, 0.6065, ..., 0.2020, 0.8580, 0.9739]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 10.480877876281738 seconds + +[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68] +[119.72] +19.623883724212646 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72} +[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68, 41.19, 39.75, 40.31, 41.27, 39.67, 39.76, 39.72, 39.58, 39.67, 40.17] +721.3699999999999 +36.06849999999999 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72, 'J_1KI': 1785.2365953364272, 'W_1KI': 90.9726443768997, 'W_D': 83.6515, 'J_D': 1641.5673093559742, 'W_D_1KI': 63.564969604863215, 'J_D_1KI': 48.3016486359143} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..032b229 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1728, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495, "TIME_S_1KI": 6.277456741642069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2262.675802116394, "W": 116.47, "J_1KI": 1309.418866965506, "W_1KI": 67.40162037037037, "W_D": 80.83224999999999, "J_D": 1570.3372207918164, "W_D_1KI": 46.7779224537037, "J_D_1KI": 27.070556975522976} diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..44057a0 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.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', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.073575019836426} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 511, 999, ..., 24998996, + 24999515, 25000000]), + col_indices=tensor([ 50, 140, 163, ..., 49849, 49891, 49909]), + values=tensor([0.6896, 0.4241, 0.6835, ..., 0.3809, 0.0330, 0.1679]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.8743, 0.1159, 0.4633, ..., 0.1043, 0.2471, 0.3798]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 6.073575019836426 seconds + +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1728', '-ss', '50000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 553, 1047, ..., 24998955, + 24999507, 25000000]), + col_indices=tensor([ 47, 133, 262, ..., 49731, 49773, 49776]), + values=tensor([0.8665, 0.6889, 0.7366, ..., 0.8541, 0.3572, 0.3739]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.0392, 0.1826, 0.6698, ..., 0.4937, 0.5808, 0.8286]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 10.847445249557495 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, 553, 1047, ..., 24998955, + 24999507, 25000000]), + col_indices=tensor([ 47, 133, 262, ..., 49731, 49773, 49776]), + values=tensor([0.8665, 0.6889, 0.7366, ..., 0.8541, 0.3572, 0.3739]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.0392, 0.1826, 0.6698, ..., 0.4937, 0.5808, 0.8286]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 10.847445249557495 seconds + +[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51] +[116.47] +19.427112579345703 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47} +[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51, 40.76, 39.62, 39.66, 39.43, 39.52, 39.33, 39.97, 39.4, 39.29, 39.41] +712.7550000000001 +35.637750000000004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47, 'J_1KI': 1309.418866965506, 'W_1KI': 67.40162037037037, 'W_D': 80.83224999999999, 'J_D': 1570.3372207918164, 'W_D_1KI': 46.7779224537037, 'J_D_1KI': 27.070556975522976} 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 new file mode 100644 index 0000000..5fa7d17 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json @@ -0,0 +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} 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 new file mode 100644 index 0000000..6a5e106 --- /dev/null +++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output @@ -0,0 +1,85 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.3033, 0.5024, 0.5149, ..., 0.3250, 0.9742, 0.7218]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 0.6186139583587646 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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.6092, 0.5985, 0.2131, ..., 0.5175, 0.3653, 0.9642]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 9.225086212158203 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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 10.466439723968506 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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.1022, 0.6049, 0.3113, ..., 0.6147, 0.8376, 0.5666]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 10.466439723968506 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} 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 new file mode 100644 index 0000000..5caf9cb --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json @@ -0,0 +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} 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 new file mode 100644 index 0000000..18d1cd6 --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output @@ -0,0 +1,45 @@ +['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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 12.322984457015991 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]), + size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr) +tensor([0.3885, 0.1371, 0.6444, ..., 0.5368, 0.0739, 0.9250]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([10000, 10000]) +Rows: 10000 +Size: 100000000 +NNZ: 30000000 +Density: 0.3 +Time: 12.322984457015991 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} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json new file mode 100644 index 0000000..250b22c --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186, "TIME_S_1KI": 15.963828563690184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3837.0465870952603, "W": 57.08, "J_1KI": 3837.0465870952603, "W_1KI": 57.08, "W_D": 41.032999999999994, "J_D": 2758.3309847280975, "W_D_1KI": 41.032999999999994, "J_D_1KI": 41.032999999999994} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output new file mode 100644 index 0000000..8f6ef0a --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output @@ -0,0 +1,47 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 110, ..., 24999910, + 24999948, 25000000]), + col_indices=tensor([ 6869, 7642, 11671, ..., 455502, 470939, + 478512]), + values=tensor([0.8757, 0.6946, 0.8023, ..., 0.8472, 0.7183, 0.5606]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4126, 0.9616, 0.6093, ..., 0.3863, 0.8636, 0.0433]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 15.963828563690186 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 49, 110, ..., 24999910, + 24999948, 25000000]), + col_indices=tensor([ 6869, 7642, 11671, ..., 455502, 470939, + 478512]), + values=tensor([0.8757, 0.6946, 0.8023, ..., 0.8472, 0.7183, 0.5606]), + size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4126, 0.9616, 0.6093, ..., 0.3863, 0.8636, 0.0433]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([500000, 500000]) +Rows: 500000 +Size: 250000000000 +NNZ: 25000000 +Density: 0.0001 +Time: 15.963828563690186 seconds + +[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59] +[57.08] +67.22225975990295 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08} +[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59, 18.37, 17.79, 17.89, 17.88, 18.0, 17.73, 17.87, 17.96, 17.67, 17.76] +320.94000000000005 +16.047000000000004 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08, 'J_1KI': 3837.0465870952603, 'W_1KI': 57.08, 'W_D': 41.032999999999994, 'J_D': 2758.3309847280975, 'W_D_1KI': 41.032999999999994, 'J_D_1KI': 41.032999999999994} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json new file mode 100644 index 0000000..aff89af --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389, "TIME_S_1KI": 10.122996807098389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3257.7272432255745, "W": 53.79, "J_1KI": 3257.7272432255745, "W_1KI": 53.79, "W_D": 37.451750000000004, "J_D": 2268.2206038571003, "W_D_1KI": 37.451750000000004, "J_D_1KI": 37.451750000000004} diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output new file mode 100644 index 0000000..bbe283a --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output @@ -0,0 +1,45 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16'] +{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 509, 1001, ..., 24999037, + 24999516, 25000000]), + col_indices=tensor([ 11, 136, 275, ..., 49665, 49739, 49958]), + values=tensor([0.3161, 0.2173, 0.0956, ..., 0.2198, 0.0588, 0.5951]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4562, 0.2414, 0.7128, ..., 0.4854, 0.8312, 0.1880]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 10.122996807098389 seconds + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 509, 1001, ..., 24999037, + 24999516, 25000000]), + col_indices=tensor([ 11, 136, 275, ..., 49665, 49739, 49958]), + values=tensor([0.3161, 0.2173, 0.0956, ..., 0.2198, 0.0588, 0.5951]), + size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr) +tensor([0.4562, 0.2414, 0.7128, ..., 0.4854, 0.8312, 0.1880]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([50000, 50000]) +Rows: 50000 +Size: 2500000000 +NNZ: 25000000 +Density: 0.01 +Time: 10.122996807098389 seconds + +[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81] +[53.79] +60.56380820274353 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79} +[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81, 18.18, 18.11, 17.85, 17.68, 17.63, 17.92, 17.78, 17.67, 18.05, 18.04] +326.765 +16.33825 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79, 'J_1KI': 3257.7272432255745, 'W_1KI': 53.79, 'W_D': 37.451750000000004, 'J_D': 2268.2206038571003, 'W_D_1KI': 37.451750000000004, 'J_D_1KI': 37.451750000000004} 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 new file mode 100644 index 0000000..457cdda --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json @@ -0,0 +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} 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 new file mode 100644 index 0000000..0e365cf --- /dev/null +++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output @@ -0,0 +1,65 @@ +['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.8071, 0.3432, 0.7096, ..., 0.6319, 0.9579, 0.4287]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 1.8840982913970947 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} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 10.464208841323853 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]), + size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr) +tensor([0.6230, 0.9791, 0.5460, ..., 0.3523, 0.6316, 0.0066]) +Matrix Type: synthetic +Matrix Format: csr +Shape: torch.Size([5000, 5000]) +Rows: 5000 +Size: 25000000 +NNZ: 7500000 +Density: 0.3 +Time: 10.464208841323853 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}