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cephi 2024-12-18 21:26:39 -05:00
parent a8eff4c683
commit 7569920be0
1863 changed files with 91816 additions and 24138 deletions
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2
pytorch/batch.py
View File

@ -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 <= 50000000])
if size ** 2 * density <= 100000000])
#for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
for i, parameter in parameter_list:

1
pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json
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@ -0,0 +1 @@
{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 4950, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.512001991271973, "TIME_S_1KI": 2.12363676591353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 850.9319224691392, "W": 64.26, "J_1KI": 171.90543888265438, "W_1KI": 12.981818181818182, "W_D": 29.119500000000002, "J_D": 385.600873270154, "W_D_1KI": 5.882727272727273, "J_D_1KI": 1.1884297520661158}

48
pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
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@ -1,11 +1,11 @@
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2207937240600586}
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.23576855659484863}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.7193, 0.7070, 0.1341, ..., 0.0458, 0.8165, 0.6087])
tensor([0.0720, 0.8900, 0.2156, ..., 0.8187, 0.1034, 0.8459])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
@ -14,16 +14,16 @@ Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 0.2207937240600586 seconds
Time: 0.23576855659484863 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4755', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.089951038360596}
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4453', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 9.444213628768921}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.1581, 0.9236, 0.8715, ..., 0.0128, 0.7061, 0.4474])
tensor([0.5426, 0.3622, 0.1360, ..., 0.9710, 0.4622, 0.4763])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
@ -32,13 +32,16 @@ Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.089951038360596 seconds
Time: 9.444213628768921 seconds
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4950', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.512001991271973}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.1581, 0.9236, 0.8715, ..., 0.0128, 0.7061, 0.4474])
tensor([0.9719, 0.1948, 0.3482, ..., 0.1448, 0.3015, 0.3034])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
@ -47,9 +50,28 @@ Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.089951038360596 seconds
Time: 10.512001991271973 seconds
[44.57, 39.27, 38.78, 39.37, 39.08, 39.0, 39.29, 40.29, 38.94, 38.6]
[64.64]
12.744337558746338
{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4755, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.089951038360596, 'TIME_S_1KI': 2.1219665695816183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 823.7939797973632, 'W': 64.64}
tensor(indices=tensor([[ 1040, 2020, 2054, ..., 160, 160, 12170],
[ 0, 0, 0, ..., 31353, 31360, 31378]]),
values=tensor([3., 1., 1., ..., 3., 3., 3.]),
size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
tensor([0.9719, 0.1948, 0.3482, ..., 0.1448, 0.3015, 0.3034])
Matrix Type: SuiteSparse
Matrix: as-caida_G_010
Matrix Format: coo
Shape: torch.Size([31379, 31379])
Rows: 31379
Size: 984641641
NNZ: 74994
Density: 7.616375021864427e-05
Time: 10.512001991271973 seconds
[39.44, 39.17, 38.57, 39.04, 39.83, 39.07, 38.63, 38.66, 40.52, 38.52]
[64.26]
13.242015600204468
{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4950, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.512001991271973, 'TIME_S_1KI': 2.12363676591353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 850.9319224691392, 'W': 64.26}
[39.44, 39.17, 38.57, 39.04, 39.83, 39.07, 38.63, 38.66, 40.52, 38.52, 39.2, 38.77, 38.71, 38.46, 38.74, 39.68, 39.05, 39.32, 38.59, 38.84]
702.8100000000001
35.1405
{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4950, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.512001991271973, 'TIME_S_1KI': 2.12363676591353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 850.9319224691392, 'W': 64.26, 'J_1KI': 171.90543888265438, 'W_1KI': 12.981818181818182, 'W_D': 29.119500000000002, 'J_D': 385.600873270154, 'W_D_1KI': 5.882727272727273, 'J_D_1KI': 1.1884297520661158}

1
pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.419839143753052, "TIME_S_1KI": 28.39193227180668, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 397.99056369781505, "W": 27.315883432832518, "J_1KI": 1084.4429528550818, "W_1KI": 74.43020008946189, "W_D": 12.377883432832519, "J_D": 180.34491972160353, "W_D_1KI": 33.727202814257545, "J_D_1KI": 91.89973518871265}

73
pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.output Normal file
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@ -0,0 +1,73 @@
['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 coo 10 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3841264247894287}
tensor(indices=tensor([[12468, 7286, 42070, ..., 41435, 93609, 87646],
[40435, 70110, 80235, ..., 72237, 42645, 8439]]),
values=tensor([0.4478, 0.9727, 0.1236, ..., 0.3635, 0.6500, 0.0042]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
tensor([0.3810, 0.0198, 0.2872, ..., 0.7346, 0.9919, 0.8495])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 0.3841264247894287 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 coo 273 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.802507400512695}
tensor(indices=tensor([[66870, 12119, 38015, ..., 43745, 48193, 42393],
[24231, 38254, 74788, ..., 95676, 54345, 18633]]),
values=tensor([0.5489, 0.7321, 0.1915, ..., 0.2369, 0.8050, 0.8821]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
tensor([0.9003, 0.7844, 0.6361, ..., 0.6011, 0.0782, 0.7656])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 7.802507400512695 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 coo 367 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.419839143753052}
tensor(indices=tensor([[49924, 69461, 63269, ..., 50062, 11998, 67211],
[44612, 71174, 43698, ..., 38509, 65750, 86040]]),
values=tensor([0.0322, 0.9610, 0.0897, ..., 0.9817, 0.7794, 0.1450]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
tensor([0.2779, 0.4359, 0.1613, ..., 0.8529, 0.4594, 0.0392])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 10.419839143753052 seconds
tensor(indices=tensor([[49924, 69461, 63269, ..., 50062, 11998, 67211],
[44612, 71174, 43698, ..., 38509, 65750, 86040]]),
values=tensor([0.0322, 0.9610, 0.0897, ..., 0.9817, 0.7794, 0.1450]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
tensor([0.2779, 0.4359, 0.1613, ..., 0.8529, 0.4594, 0.0392])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 10.419839143753052 seconds
[16.36, 16.36, 16.48, 16.6, 16.68, 16.72, 16.76, 16.64, 16.56, 16.68]
[16.88, 16.8, 17.76, 17.76, 19.72, 23.36, 28.64, 32.92, 36.04, 38.68, 38.72, 38.96, 38.72, 38.76]
14.569931983947754
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.419839143753052, 'TIME_S_1KI': 28.39193227180668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 397.99056369781505, 'W': 27.315883432832518}
[16.36, 16.36, 16.48, 16.6, 16.68, 16.72, 16.76, 16.64, 16.56, 16.68, 16.4, 16.64, 16.6, 16.88, 16.76, 16.64, 16.64, 16.56, 16.28, 16.48]
298.76
14.937999999999999
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.419839143753052, 'TIME_S_1KI': 28.39193227180668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 397.99056369781505, 'W': 27.315883432832518, 'J_1KI': 1084.4429528550818, 'W_1KI': 74.43020008946189, 'W_D': 12.377883432832519, 'J_D': 180.34491972160353, 'W_D_1KI': 33.727202814257545, 'J_D_1KI': 91.89973518871265}

1
pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 34, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.180678606033325, "TIME_S_1KI": 299.4317237068625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 422.515793838501, "W": 25.557128131432332, "J_1KI": 12426.935112897088, "W_1KI": 751.6802391597745, "W_D": 10.417128131432332, "J_D": 172.2181435775757, "W_D_1KI": 306.38612151271565, "J_D_1KI": 9011.356515079871}

56
pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.output Normal file
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@ -0,0 +1,56 @@
['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 coo 10 -ss 100000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0579380989074707}
tensor(indices=tensor([[40619, 25503, 5323, ..., 95949, 31390, 82914],
[17388, 8501, 29360, ..., 49103, 79345, 61915]]),
values=tensor([0.5711, 0.4325, 0.6219, ..., 0.4496, 0.6456, 0.2954]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
tensor([0.6293, 0.9496, 0.1593, ..., 0.6227, 0.5166, 0.2304])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 3.0579380989074707 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 coo 34 -ss 100000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.180678606033325}
tensor(indices=tensor([[22566, 11034, 23228, ..., 20288, 68913, 82958],
[93883, 53094, 89525, ..., 81254, 92542, 63317]]),
values=tensor([0.3468, 0.2008, 0.6088, ..., 0.6549, 0.5439, 0.6485]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
tensor([0.5288, 0.1483, 0.5754, ..., 0.9623, 0.5752, 0.3388])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 10.180678606033325 seconds
tensor(indices=tensor([[22566, 11034, 23228, ..., 20288, 68913, 82958],
[93883, 53094, 89525, ..., 81254, 92542, 63317]]),
values=tensor([0.3468, 0.2008, 0.6088, ..., 0.6549, 0.5439, 0.6485]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
tensor([0.5288, 0.1483, 0.5754, ..., 0.9623, 0.5752, 0.3388])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 10.180678606033325 seconds
[16.36, 16.68, 16.68, 16.96, 17.08, 17.24, 17.04, 16.76, 16.64, 16.48]
[16.32, 16.4, 19.4, 20.52, 22.24, 24.72, 24.72, 28.72, 29.0, 31.24, 33.04, 32.12, 32.0, 32.72, 33.04, 33.4]
16.532209396362305
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 34, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.180678606033325, 'TIME_S_1KI': 299.4317237068625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 422.515793838501, 'W': 25.557128131432332}
[16.36, 16.68, 16.68, 16.96, 17.08, 17.24, 17.04, 16.76, 16.64, 16.48, 16.36, 16.44, 16.6, 16.4, 16.84, 17.16, 17.12, 17.12, 17.0, 16.88]
302.8
15.14
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pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.output Normal file
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size=(10000, 10000), nnz=1000, layout=torch.sparse_coo)
tensor([0.2430, 0.4800, 0.1603, ..., 0.2671, 0.3314, 0.3631])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 0.006676435470581055 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 coo 15726 -ss 10000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5300266742706299}
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tensor([0.0173, 0.9884, 0.5080, ..., 0.4806, 0.4208, 0.0837])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([10000, 10000])
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Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 0.5300266742706299 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 coo 311537 -ss 10000 -sd 1e-05 -c 16']
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size=(10000, 10000), nnz=1000, layout=torch.sparse_coo)
tensor([0.9181, 0.5008, 0.3382, ..., 0.4200, 0.0196, 0.8754])
Matrix Type: synthetic
Matrix Format: coo
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 1000
Density: 1e-05
Time: 9.936841487884521 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 coo 329192 -ss 10000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.653273582458496}
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NNZ: 1000
Density: 1e-05
Time: 10.653273582458496 seconds
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pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.output Normal file
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@ -0,0 +1,90 @@
['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 coo 10 -ss 5000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0067138671875}
tensor(indices=tensor([[2960, 3352, 2272, ..., 4354, 3613, 3081],
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tensor([0.8861, 0.1443, 0.1411, ..., 0.6596, 0.1915, 0.7341])
Matrix Type: synthetic
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Time: 0.0067138671875 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 coo 15639 -ss 5000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.6672103404998779}
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tensor([0.8830, 0.3419, 0.0938, ..., 0.1921, 0.2616, 0.4458])
Matrix Type: synthetic
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['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 coo 246113 -ss 5000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.024578332901001}
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tensor([0.9981, 0.3203, 0.0360, ..., 0.8201, 0.6163, 0.8541])
Matrix Type: synthetic
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['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 coo 286349 -ss 5000 -sd 5e-05 -c 16']
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tensor(indices=tensor([[4546, 268, 2475, ..., 4578, 2693, 1114],
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tensor([0.3695, 0.7282, 0.1314, ..., 0.6685, 0.6494, 0.9188])
Matrix Type: synthetic
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[16.56, 16.44, 16.2, 16.36, 16.12, 16.08, 16.36, 16.4, 16.48, 16.4]
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292.32
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2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
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@ -1 +1 @@
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76
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
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@ -1,14 +1,34 @@
['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 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6005632877349854}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6985688209533691}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 17, ..., 999970,
tensor(crow_indices=tensor([ 0, 13, 22, ..., 999981,
999991, 1000000]),
col_indices=tensor([ 3841, 8582, 8659, ..., 46850, 51232, 80903]),
values=tensor([0.4815, 0.2359, 0.4039, ..., 0.8218, 0.1508, 0.2420]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.4121, 0.4277, 0.6441, ..., 0.2185, 0.3156, 0.3986])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 0.6985688209533691 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 1503 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.003351211547852}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 22, ..., 999979,
999989, 1000000]),
col_indices=tensor([27708, 32922, 35240, ..., 82805, 88487, 98517]),
values=tensor([0.0088, 0.7733, 0.0012, ..., 0.6420, 0.7382, 0.2177]),
col_indices=tensor([16506, 20710, 37506, ..., 59060, 78382, 91823]),
values=tensor([0.6315, 0.8392, 0.3677, ..., 0.4353, 0.5265, 0.8972]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.1129, 0.5965, 0.7496, ..., 0.0902, 0.9107, 0.7724])
tensor([0.3202, 0.2384, 0.4035, ..., 0.5180, 0.1753, 0.3933])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -16,19 +36,19 @@ Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 0.6005632877349854 seconds
Time: 9.003351211547852 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 1748 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308}
['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 1752 -ss 100000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.485095500946045}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 10, 23, ..., 999980,
999992, 1000000]),
col_indices=tensor([ 8017, 17251, 18992, ..., 72823, 91334, 91663]),
values=tensor([0.4596, 0.1797, 0.9797, ..., 0.0499, 0.7967, 0.0183]),
tensor(crow_indices=tensor([ 0, 10, 27, ..., 999981,
999986, 1000000]),
col_indices=tensor([27575, 38852, 42502, ..., 91134, 92148, 97111]),
values=tensor([0.1986, 0.8938, 0.8330, ..., 0.0983, 0.0891, 0.3928]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.8873, 0.5523, 0.3791, ..., 0.8812, 0.4027, 0.2259])
tensor([0.2333, 0.4383, 0.7667, ..., 0.5123, 0.4425, 0.6550])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -36,16 +56,16 @@ Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 10.143786191940308 seconds
Time: 10.485095500946045 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, 10, 23, ..., 999980,
999992, 1000000]),
col_indices=tensor([ 8017, 17251, 18992, ..., 72823, 91334, 91663]),
values=tensor([0.4596, 0.1797, 0.9797, ..., 0.0499, 0.7967, 0.0183]),
tensor(crow_indices=tensor([ 0, 10, 27, ..., 999981,
999986, 1000000]),
col_indices=tensor([27575, 38852, 42502, ..., 91134, 92148, 97111]),
values=tensor([0.1986, 0.8938, 0.8330, ..., 0.0983, 0.0891, 0.3928]),
size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.8873, 0.5523, 0.3791, ..., 0.8812, 0.4027, 0.2259])
tensor([0.2333, 0.4383, 0.7667, ..., 0.5123, 0.4425, 0.6550])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -53,13 +73,13 @@ Rows: 100000
Size: 10000000000
NNZ: 1000000
Density: 0.0001
Time: 10.143786191940308 seconds
Time: 10.485095500946045 seconds
[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04]
[20.76, 20.76, 21.04, 22.36, 24.68, 32.36, 38.56, 45.04, 50.12, 51.88, 51.52, 52.0, 52.0, 51.84]
14.611910104751587
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824}
[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04, 20.36, 20.64, 20.56, 20.72, 20.68, 20.52, 20.72, 20.76, 20.52, 20.48]
373.86
18.693
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824, 'J_1KI': 303.3989815962942, 'W_1KI': 20.763813862886625, 'W_D': 17.602146632325823, 'J_D': 257.20098424220083, 'W_D_1KI': 10.069877936113171, 'J_D_1KI': 5.760799734618519}
[21.12, 20.96, 20.8, 20.6, 20.8, 20.8, 20.8, 20.84, 20.72, 20.68]
[20.76, 20.8, 21.92, 21.92, 22.88, 27.32, 33.44, 40.64, 45.96, 51.28, 52.0, 51.52, 51.4, 51.84]
14.671415567398071
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1752, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.485095500946045, 'TIME_S_1KI': 5.984643550768291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 512.186183013916, 'W': 34.91048158652564}
[21.12, 20.96, 20.8, 20.6, 20.8, 20.8, 20.8, 20.84, 20.72, 20.68, 21.0, 21.04, 20.92, 20.76, 20.48, 20.48, 20.48, 20.64, 20.64, 21.0]
373.65999999999997
18.683
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1752, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.485095500946045, 'TIME_S_1KI': 5.984643550768291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 512.186183013916, 'W': 34.91048158652564, 'J_1KI': 292.3437117659338, 'W_1KI': 19.926073964911897, 'W_D': 16.22748158652564, 'J_D': 238.08012596821786, 'W_D_1KI': 9.262261179523769, 'J_D_1KI': 5.286678755435941}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
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@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321, "TIME_S_1KI": 63.90815871102469, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 628.162894821167, "W": 35.42748603907242, "J_1KI": 3589.5022561209544, "W_1KI": 202.44277736612813, "W_D": 16.71548603907242, "J_D": 296.38140530395515, "W_D_1KI": 95.51706308041383, "J_D_1KI": 545.8117890309362}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 183, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.392640113830566, "TIME_S_1KI": 56.790383135686156, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 621.8855728912355, "W": 35.106665438111044, "J_1KI": 3398.2818190777894, "W_1KI": 191.83970184760133, "W_D": 16.547665438111046, "J_D": 293.1282214522363, "W_D_1KI": 90.42440130115325, "J_D_1KI": 494.12241148171177}

58
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
View File

@ -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 100 -ss 100000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.9738054275512695}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.7204060554504395}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 100, 229, ..., 9999825,
9999913, 10000000]),
col_indices=tensor([ 2839, 3131, 5153, ..., 92533, 94576, 98932]),
values=tensor([0.4697, 0.9996, 0.7875, ..., 0.5192, 0.5202, 0.9540]),
tensor(crow_indices=tensor([ 0, 98, 204, ..., 9999786,
9999897, 10000000]),
col_indices=tensor([ 2168, 2221, 3670, ..., 97171, 97920, 99173]),
values=tensor([0.5868, 0.2768, 0.5813, ..., 0.2211, 0.2231, 0.3014]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.9598, 0.0952, 0.8851, ..., 0.3844, 0.8104, 0.5939])
tensor([0.6888, 0.8719, 0.0407, ..., 0.0271, 0.8141, 0.6850])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -16,19 +16,19 @@ Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 5.9738054275512695 seconds
Time: 5.7204060554504395 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 175 -ss 100000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321}
['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 183 -ss 100000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.392640113830566}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 95, 193, ..., 9999801,
9999901, 10000000]),
col_indices=tensor([ 2869, 4015, 6080, ..., 94953, 95635, 98117]),
values=tensor([0.0857, 0.9758, 0.7363, ..., 0.8151, 0.8595, 0.7723]),
tensor(crow_indices=tensor([ 0, 97, 203, ..., 9999791,
9999906, 10000000]),
col_indices=tensor([ 466, 1594, 2031, ..., 98883, 99435, 99456]),
values=tensor([0.5174, 0.4660, 0.4037, ..., 0.7630, 0.3193, 0.8740]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.7586, 0.5970, 0.0221, ..., 0.6721, 0.2659, 0.4588])
tensor([0.4869, 0.9209, 0.1121, ..., 0.4031, 0.7408, 0.7156])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -36,16 +36,16 @@ Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 11.183927774429321 seconds
Time: 10.392640113830566 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, 95, 193, ..., 9999801,
9999901, 10000000]),
col_indices=tensor([ 2869, 4015, 6080, ..., 94953, 95635, 98117]),
values=tensor([0.0857, 0.9758, 0.7363, ..., 0.8151, 0.8595, 0.7723]),
tensor(crow_indices=tensor([ 0, 97, 203, ..., 9999791,
9999906, 10000000]),
col_indices=tensor([ 466, 1594, 2031, ..., 98883, 99435, 99456]),
values=tensor([0.5174, 0.4660, 0.4037, ..., 0.7630, 0.3193, 0.8740]),
size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.7586, 0.5970, 0.0221, ..., 0.6721, 0.2659, 0.4588])
tensor([0.4869, 0.9209, 0.1121, ..., 0.4031, 0.7408, 0.7156])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -53,13 +53,13 @@ Rows: 100000
Size: 10000000000
NNZ: 10000000
Density: 0.001
Time: 11.183927774429321 seconds
Time: 10.392640113830566 seconds
[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64]
[20.64, 20.76, 20.84, 24.4, 27.0, 28.6, 31.96, 32.96, 34.0, 38.76, 44.2, 47.96, 51.6, 50.84, 50.88, 50.6, 50.76]
17.730947494506836
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242}
[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64, 20.64, 20.64, 20.64, 20.72, 20.76, 20.76, 21.0, 20.92, 21.36, 21.56]
374.24
18.712
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242, 'J_1KI': 3589.5022561209544, 'W_1KI': 202.44277736612813, 'W_D': 16.71548603907242, 'J_D': 296.38140530395515, 'W_D_1KI': 95.51706308041383, 'J_D_1KI': 545.8117890309362}
[20.4, 20.28, 20.32, 20.28, 20.48, 20.48, 20.88, 20.84, 20.76, 20.76]
[20.72, 20.64, 21.04, 22.32, 24.12, 25.84, 28.68, 31.24, 36.6, 42.2, 42.2, 45.8, 51.08, 52.16, 52.0, 52.08, 52.32]
17.714173793792725
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 183, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.392640113830566, 'TIME_S_1KI': 56.790383135686156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 621.8855728912355, 'W': 35.106665438111044}
[20.4, 20.28, 20.32, 20.28, 20.48, 20.48, 20.88, 20.84, 20.76, 20.76, 20.8, 20.68, 20.96, 21.0, 20.84, 20.72, 20.64, 20.56, 20.32, 20.32]
371.17999999999995
18.558999999999997
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 183, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.392640113830566, 'TIME_S_1KI': 56.790383135686156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 621.8855728912355, 'W': 35.106665438111044, 'J_1KI': 3398.2818190777894, 'W_1KI': 191.83970184760133, 'W_D': 16.547665438111046, 'J_D': 293.1282214522363, 'W_D_1KI': 90.42440130115325, 'J_D_1KI': 494.12241148171177}

1
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 55.23988056182861, "TIME_S_1KI": 552.3988056182861, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3392.229231271745, "W": 34.21525226903433, "J_1KI": 33922.29231271745, "W_1KI": 342.1525226903433, "W_D": 15.519252269034329, "J_D": 1538.6372364158642, "W_D_1KI": 155.19252269034328, "J_D_1KI": 1551.9252269034328}

45
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.output Normal file
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@ -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 100000 -sd 0.01 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 55.23988056182861}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 975, 2002, ...,
99998038, 99999032, 100000000]),
col_indices=tensor([ 193, 475, 708, ..., 99676, 99979, 99985]),
values=tensor([0.3846, 0.7039, 0.8140, ..., 0.8966, 0.5567, 0.1696]),
size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
tensor([0.9784, 0.7882, 0.1982, ..., 0.8110, 0.1934, 0.3487])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 100000000
Density: 0.01
Time: 55.23988056182861 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, 975, 2002, ...,
99998038, 99999032, 100000000]),
col_indices=tensor([ 193, 475, 708, ..., 99676, 99979, 99985]),
values=tensor([0.3846, 0.7039, 0.8140, ..., 0.8966, 0.5567, 0.1696]),
size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
tensor([0.9784, 0.7882, 0.1982, ..., 0.8110, 0.1934, 0.3487])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 100000000
Density: 0.01
Time: 55.23988056182861 seconds
[21.08, 21.04, 20.84, 21.08, 21.04, 21.04, 20.92, 20.88, 20.88, 20.72]
[20.8, 20.76, 20.92, 22.24, 24.44, 26.24, 27.2, 27.2, 26.72, 25.88, 27.64, 27.64, 29.6, 30.76, 30.64, 29.12, 27.36, 25.36, 26.56, 29.12, 32.64, 36.12, 36.04, 35.4, 34.08, 34.08, 28.52, 28.84, 28.6, 28.12, 27.72, 28.28, 28.12, 28.84, 29.32, 29.84, 29.72, 29.68, 29.68, 29.48, 29.92, 33.16, 35.88, 39.28, 42.08, 43.0, 42.28, 41.44, 40.48, 40.64, 40.88, 40.72, 40.84, 41.68, 41.32, 41.48, 41.32, 41.32, 41.4, 41.6, 41.04, 40.84, 40.52, 40.48, 41.32, 41.8, 41.56, 41.52, 40.16, 39.76, 39.12, 38.96, 39.96, 40.84, 40.92, 40.92, 41.64, 40.48, 41.04, 40.68, 40.48, 41.48, 42.2, 42.24, 42.56, 42.28, 41.8, 41.24, 40.48, 40.48, 40.96, 41.72, 41.92, 41.4, 41.24, 41.24]
99.1437737941742
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 55.23988056182861, 'TIME_S_1KI': 552.3988056182861, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3392.229231271745, 'W': 34.21525226903433}
[21.08, 21.04, 20.84, 21.08, 21.04, 21.04, 20.92, 20.88, 20.88, 20.72, 20.16, 20.16, 20.4, 20.36, 20.56, 20.68, 20.84, 20.88, 20.84, 21.0]
373.92
18.696
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 55.23988056182861, 'TIME_S_1KI': 552.3988056182861, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3392.229231271745, 'W': 34.21525226903433, 'J_1KI': 33922.29231271745, 'W_1KI': 342.1525226903433, 'W_D': 15.519252269034329, 'J_D': 1538.6372364158642, 'W_D_1KI': 155.19252269034328, 'J_D_1KI': 1551.9252269034328}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11597, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856, "TIME_S_1KI": 0.8998928454926151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 483.6025880336762, "W": 33.00177532885384, "J_1KI": 41.700662932971994, "W_1KI": 2.845716592985586, "W_D": 14.225775328853839, "J_D": 208.462172027588, "W_D_1KI": 1.2266771862424626, "J_D_1KI": 0.10577538900081596}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11845, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.452897548675537, "TIME_S_1KI": 0.8824734106100073, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 491.721949520111, "W": 33.55721565186675, "J_1KI": 41.51303921655644, "W_1KI": 2.833027914889552, "W_D": 15.062215651866751, "J_D": 220.71026754021645, "W_D_1KI": 1.2716095949233222, "J_D_1KI": 0.10735412367440457}

86
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
View File

@ -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 100 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12187767028808594}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.19909358024597168}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 99998, 99999,
tensor(crow_indices=tensor([ 0, 4, 5, ..., 99997, 99999,
100000]),
col_indices=tensor([99237, 81965, 52149, ..., 94819, 50598, 82628]),
values=tensor([0.3300, 0.8237, 0.5005, ..., 0.6469, 0.1010, 0.4687]),
col_indices=tensor([12266, 41353, 64119, ..., 57579, 58990, 6971]),
values=tensor([0.6227, 0.7944, 0.7450, ..., 0.9056, 0.8637, 0.0316]),
size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
tensor([0.0038, 0.2456, 0.3182, ..., 0.7163, 0.7510, 0.9775])
tensor([0.5310, 0.7756, 0.0968, ..., 0.3911, 0.0764, 0.5885])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -16,19 +16,19 @@ Rows: 100000
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 0.12187767028808594 seconds
Time: 0.19909358024597168 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 8615 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.799410104751587}
['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 5273 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.112189292907715}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 99998, 99999,
tensor(crow_indices=tensor([ 0, 0, 2, ..., 99998, 99999,
100000]),
col_indices=tensor([88588, 42232, 90125, ..., 27244, 80106, 39636]),
values=tensor([0.8018, 0.8315, 0.5597, ..., 0.5532, 0.0030, 0.5793]),
col_indices=tensor([18500, 89431, 21652, ..., 1449, 96967, 19441]),
values=tensor([0.9093, 0.3424, 0.7088, ..., 0.3078, 0.7479, 0.8254]),
size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
tensor([0.1929, 0.1411, 0.4568, ..., 0.6294, 0.2188, 0.4350])
tensor([0.7902, 0.1848, 0.8411, ..., 0.5530, 0.0625, 0.5516])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -36,19 +36,19 @@ Rows: 100000
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 7.799410104751587 seconds
Time: 5.112189292907715 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 11597 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856}
['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 10830 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.599655628204346}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 99998, 99998,
tensor(crow_indices=tensor([ 0, 0, 1, ..., 99999, 100000,
100000]),
col_indices=tensor([24172, 83350, 29274, ..., 76990, 53592, 71081]),
values=tensor([0.7302, 0.8346, 0.3553, ..., 0.4222, 0.8183, 0.0288]),
col_indices=tensor([ 4135, 30090, 75785, ..., 75263, 77636, 9635]),
values=tensor([0.5430, 0.9022, 0.0393, ..., 0.9343, 0.9359, 0.0313]),
size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
tensor([0.8537, 0.9431, 0.0277, ..., 0.1357, 0.7019, 0.9196])
tensor([0.4424, 0.3111, 0.3025, ..., 0.8284, 0.3246, 0.9597])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -56,16 +56,19 @@ Rows: 100000
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.436057329177856 seconds
Time: 9.599655628204346 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 11845 -ss 100000 -sd 1e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.452897548675537}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 1, ..., 99998, 99998,
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99997, 100000,
100000]),
col_indices=tensor([24172, 83350, 29274, ..., 76990, 53592, 71081]),
values=tensor([0.7302, 0.8346, 0.3553, ..., 0.4222, 0.8183, 0.0288]),
col_indices=tensor([26237, 4052, 39558, ..., 16301, 35459, 98674]),
values=tensor([0.0699, 0.2116, 0.3702, ..., 0.5467, 0.2088, 0.3545]),
size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
tensor([0.8537, 0.9431, 0.0277, ..., 0.1357, 0.7019, 0.9196])
tensor([0.6982, 0.4327, 0.4762, ..., 0.0773, 0.8958, 0.0557])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -73,13 +76,30 @@ Rows: 100000
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.436057329177856 seconds
Time: 10.452897548675537 seconds
[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8]
[20.64, 20.36, 20.72, 23.08, 24.72, 29.44, 35.88, 40.08, 43.56, 45.4, 45.96, 45.6, 45.36, 46.04]
14.653835535049438
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384}
[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8, 20.36, 20.32, 20.32, 20.44, 20.64, 20.8, 21.0, 21.16, 21.12, 21.04]
375.52
18.776
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384, 'J_1KI': 41.700662932971994, 'W_1KI': 2.845716592985586, 'W_D': 14.225775328853839, 'J_D': 208.462172027588, 'W_D_1KI': 1.2266771862424626, 'J_D_1KI': 0.10577538900081596}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 99997, 100000,
100000]),
col_indices=tensor([26237, 4052, 39558, ..., 16301, 35459, 98674]),
values=tensor([0.0699, 0.2116, 0.3702, ..., 0.5467, 0.2088, 0.3545]),
size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
tensor([0.6982, 0.4327, 0.4762, ..., 0.0773, 0.8958, 0.0557])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 100000
Density: 1e-05
Time: 10.452897548675537 seconds
[20.44, 20.4, 20.64, 20.88, 20.76, 20.76, 20.88, 20.8, 20.96, 21.04]
[20.84, 20.88, 21.72, 26.12, 28.0, 32.48, 37.88, 39.16, 42.96, 44.8, 45.0, 45.32, 45.04, 45.04]
14.653240442276001
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11845, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.452897548675537, 'TIME_S_1KI': 0.8824734106100073, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 491.721949520111, 'W': 33.55721565186675}
[20.44, 20.4, 20.64, 20.88, 20.76, 20.76, 20.88, 20.8, 20.96, 21.04, 20.68, 20.68, 20.52, 20.24, 20.2, 20.04, 20.28, 20.24, 20.36, 20.36]
369.9
18.494999999999997
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11845, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.452897548675537, 'TIME_S_1KI': 0.8824734106100073, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 491.721949520111, 'W': 33.55721565186675, 'J_1KI': 41.51303921655644, 'W_1KI': 2.833027914889552, 'W_D': 15.062215651866751, 'J_D': 220.71026754021645, 'W_D_1KI': 1.2716095949233222, 'J_D_1KI': 0.10735412367440457}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524, "TIME_S_1KI": 3.3034382352403777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 528.672490940094, "W": 36.05478479295784, "J_1KI": 160.34955745832394, "W_1KI": 10.935633846817664, "W_D": 17.45578479295784, "J_D": 255.95474444794658, "W_D_1KI": 5.2944448871573675, "J_D_1KI": 1.605837090432929}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3160, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.255688428878784, "TIME_S_1KI": 3.2454710217970835, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 463.90646287918094, "W": 34.111621125184975, "J_1KI": 146.8058426832851, "W_1KI": 10.794816811767397, "W_D": 15.559621125184975, "J_D": 211.6055632019043, "W_D_1KI": 4.92393073581803, "J_D_1KI": 1.5582059290563386}

72
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
View File

@ -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 100 -ss 100000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4573814868927002}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.332211971282959}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 10, 16, ..., 499990, 499995,
tensor(crow_indices=tensor([ 0, 10, 13, ..., 499990, 499997,
500000]),
col_indices=tensor([ 5164, 6869, 8448, ..., 29154, 68140, 97893]),
values=tensor([0.8386, 0.0921, 0.7067, ..., 0.9232, 0.1449, 0.6848]),
col_indices=tensor([16831, 31700, 33476, ..., 20126, 37524, 56641]),
values=tensor([0.4034, 0.0732, 0.2390, ..., 0.1660, 0.9005, 0.3603]),
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
tensor([0.0246, 0.8160, 0.3295, ..., 0.0588, 0.6998, 0.9868])
tensor([0.3728, 0.2661, 0.0172, ..., 0.8787, 0.3705, 0.6094])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -16,19 +16,19 @@ Rows: 100000
Size: 10000000000
NNZ: 500000
Density: 5e-05
Time: 0.4573814868927002 seconds
Time: 0.332211971282959 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 2295 -ss 100000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.306779146194458}
['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 3160 -ss 100000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.255688428878784}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 4, 8, ..., 499989, 499995,
tensor(crow_indices=tensor([ 0, 1, 7, ..., 499992, 499996,
500000]),
col_indices=tensor([ 2059, 19971, 54406, ..., 65065, 65922, 83323]),
values=tensor([0.5530, 0.6181, 0.7781, ..., 0.5380, 0.6243, 0.8378]),
col_indices=tensor([64602, 478, 27899, ..., 42044, 53218, 73264]),
values=tensor([0.8097, 0.5983, 0.2516, ..., 0.0269, 0.0458, 0.5960]),
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
tensor([0.4055, 0.5945, 0.9428, ..., 0.6446, 0.1456, 0.3700])
tensor([0.5290, 0.0200, 0.8761, ..., 0.9070, 0.8739, 0.1739])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -36,19 +36,16 @@ Rows: 100000
Size: 10000000000
NNZ: 500000
Density: 5e-05
Time: 7.306779146194458 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 3297 -ss 100000 -sd 5e-05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524}
Time: 10.255688428878784 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 11, ..., 499995, 499995,
tensor(crow_indices=tensor([ 0, 1, 7, ..., 499992, 499996,
500000]),
col_indices=tensor([ 8913, 22689, 49331, ..., 65321, 72756, 72788]),
values=tensor([0.7511, 0.0782, 0.7533, ..., 0.6341, 0.1803, 0.2288]),
col_indices=tensor([64602, 478, 27899, ..., 42044, 53218, 73264]),
values=tensor([0.8097, 0.5983, 0.2516, ..., 0.0269, 0.0458, 0.5960]),
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
tensor([0.5601, 0.4293, 0.2285, ..., 0.5137, 0.5400, 0.5797])
tensor([0.5290, 0.0200, 0.8761, ..., 0.9070, 0.8739, 0.1739])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
@ -56,30 +53,13 @@ Rows: 100000
Size: 10000000000
NNZ: 500000
Density: 5e-05
Time: 10.891435861587524 seconds
Time: 10.255688428878784 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 5, 11, ..., 499995, 499995,
500000]),
col_indices=tensor([ 8913, 22689, 49331, ..., 65321, 72756, 72788]),
values=tensor([0.7511, 0.0782, 0.7533, ..., 0.6341, 0.1803, 0.2288]),
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
tensor([0.5601, 0.4293, 0.2285, ..., 0.5137, 0.5400, 0.5797])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([100000, 100000])
Rows: 100000
Size: 10000000000
NNZ: 500000
Density: 5e-05
Time: 10.891435861587524 seconds
[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92]
[20.6, 20.48, 20.72, 25.12, 26.88, 32.4, 38.84, 42.28, 46.96, 50.12, 51.2, 51.8, 51.56, 51.6]
14.66303277015686
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784}
[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92, 20.84, 21.04, 20.84, 20.84, 20.76, 20.8, 20.68, 20.44, 20.28, 20.24]
371.98
18.599
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784, 'J_1KI': 160.34955745832394, 'W_1KI': 10.935633846817664, 'W_D': 17.45578479295784, 'J_D': 255.95474444794658, 'W_D_1KI': 5.2944448871573675, 'J_D_1KI': 1.605837090432929}
[20.4, 20.4, 20.36, 20.48, 20.48, 20.56, 20.72, 20.68, 20.88, 20.92]
[20.8, 20.96, 22.0, 23.08, 26.64, 32.64, 32.64, 39.84, 44.88, 50.64, 51.68, 51.72, 51.64]
13.599660396575928
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.255688428878784, 'TIME_S_1KI': 3.2454710217970835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.90646287918094, 'W': 34.111621125184975}
[20.4, 20.4, 20.36, 20.48, 20.48, 20.56, 20.72, 20.68, 20.88, 20.92, 20.52, 20.4, 20.36, 20.68, 20.72, 20.8, 20.88, 20.68, 20.56, 20.96]
371.03999999999996
18.552
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.255688428878784, 'TIME_S_1KI': 3.2454710217970835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.90646287918094, 'W': 34.111621125184975, 'J_1KI': 146.8058426832851, 'W_1KI': 10.794816811767397, 'W_D': 15.559621125184975, 'J_D': 211.6055632019043, 'W_D_1KI': 4.92393073581803, 'J_D_1KI': 1.5582059290563386}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372, "TIME_S_1KI": 0.32081469877018387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.63640854835506, "W": 22.48260624860275, "J_1KI": 9.793982367580435, "W_1KI": 0.6888897612637195, "W_D": 3.984606248602752, "J_D": 56.64935891771315, "W_D_1KI": 0.12209235962136145, "J_D_1KI": 0.003741033203252894}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.722160577774048, "TIME_S_1KI": 0.32498289266735514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.13037155151375, "W": 22.887281127943623, "J_1KI": 9.854525855530376, "W_1KI": 0.6937011222969607, "W_D": 4.493281127943625, "J_D": 63.8303062057496, "W_D_1KI": 0.13618892273947883, "J_D_1KI": 0.0041278126493340655}

66
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
View File

@ -1,13 +1,13 @@
['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 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.050879478454589844}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04047083854675293}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 9998, 9999, 10000]),
col_indices=tensor([5382, 2827, 5658, ..., 9195, 8647, 1137]),
values=tensor([0.6423, 0.5656, 0.8194, ..., 0.3825, 0.7281, 0.0248]),
tensor(crow_indices=tensor([ 0, 3, 4, ..., 10000, 10000, 10000]),
col_indices=tensor([2549, 9361, 9970, ..., 704, 4011, 7891]),
values=tensor([0.5892, 0.1476, 0.4892, ..., 0.9338, 0.1639, 0.4664]),
size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
tensor([0.6609, 0.7541, 0.4159, ..., 0.2180, 0.3481, 0.0053])
tensor([0.8574, 0.7762, 0.9018, ..., 0.6074, 0.6936, 0.9938])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -15,18 +15,18 @@ Rows: 10000
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 0.050879478454589844 seconds
Time: 0.04047083854675293 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 20637 -ss 10000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.639445781707764}
['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 25944 -ss 10000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.256449699401855}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 0, 1, ..., 9998, 10000, 10000]),
col_indices=tensor([1538, 6690, 5733, ..., 9607, 7438, 7782]),
values=tensor([0.7222, 0.1089, 0.5631, ..., 0.3116, 0.0243, 0.6999]),
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9998, 10000, 10000]),
col_indices=tensor([2764, 9413, 5263, ..., 1959, 4242, 4549]),
values=tensor([0.9684, 0.2656, 0.2250, ..., 0.3440, 0.8382, 0.0353]),
size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
tensor([0.2878, 0.8940, 0.0961, ..., 0.0631, 0.2895, 0.2219])
tensor([0.2298, 0.9239, 0.9999, ..., 0.9160, 0.4053, 0.1195])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -34,18 +34,18 @@ Rows: 10000
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 6.639445781707764 seconds
Time: 8.256449699401855 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 32636 -ss 10000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372}
['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 32993 -ss 10000 -sd 0.0001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.722160577774048}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9999, 10000, 10000]),
col_indices=tensor([7014, 8766, 3433, ..., 9466, 1431, 7728]),
values=tensor([0.0370, 0.3747, 0.2051, ..., 0.2901, 0.3737, 0.7201]),
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9998, 9998, 10000]),
col_indices=tensor([5965, 6204, 1451, ..., 9267, 1058, 9600]),
values=tensor([0.3937, 0.8327, 0.3110, ..., 0.9645, 0.7301, 0.2055]),
size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
tensor([0.8451, 0.4833, 0.4298, ..., 0.9015, 0.0937, 0.6764])
tensor([0.5302, 0.5425, 0.3526, ..., 0.3577, 0.4587, 0.7959])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,15 +53,15 @@ Rows: 10000
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 10.47010850906372 seconds
Time: 10.722160577774048 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9999, 10000, 10000]),
col_indices=tensor([7014, 8766, 3433, ..., 9466, 1431, 7728]),
values=tensor([0.0370, 0.3747, 0.2051, ..., 0.2901, 0.3737, 0.7201]),
tensor(crow_indices=tensor([ 0, 1, 2, ..., 9998, 9998, 10000]),
col_indices=tensor([5965, 6204, 1451, ..., 9267, 1058, 9600]),
values=tensor([0.3937, 0.8327, 0.3110, ..., 0.9645, 0.7301, 0.2055]),
size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
tensor([0.8451, 0.4833, 0.4298, ..., 0.9015, 0.0937, 0.6764])
tensor([0.5302, 0.5425, 0.3526, ..., 0.3577, 0.4587, 0.7959])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -69,13 +69,13 @@ Rows: 10000
Size: 100000000
NNZ: 10000
Density: 0.0001
Time: 10.47010850906372 seconds
Time: 10.722160577774048 seconds
[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4]
[20.72, 20.64, 21.24, 23.32, 25.32, 26.04, 26.72, 26.72, 26.48, 25.08, 23.72, 23.6, 23.56, 23.48]
14.217053174972534
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275}
[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4, 20.64, 20.56, 20.48, 20.52, 20.68, 20.84, 20.88, 20.88, 20.84, 20.48]
369.96
18.497999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275, 'J_1KI': 9.793982367580435, 'W_1KI': 0.6888897612637195, 'W_D': 3.984606248602752, 'J_D': 56.64935891771315, 'W_D_1KI': 0.12209235962136145, 'J_D_1KI': 0.003741033203252894}
[20.24, 20.2, 20.16, 20.24, 20.32, 20.88, 20.84, 20.52, 20.88, 20.36]
[20.32, 20.56, 20.56, 23.56, 25.76, 28.24, 29.12, 29.28, 25.72, 24.52, 23.72, 23.72, 23.64, 23.48]
14.20572280883789
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.722160577774048, 'TIME_S_1KI': 0.32498289266735514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.13037155151375, 'W': 22.887281127943623}
[20.24, 20.2, 20.16, 20.24, 20.32, 20.88, 20.84, 20.52, 20.88, 20.36, 20.28, 20.32, 20.32, 20.44, 20.4, 20.4, 20.4, 20.32, 20.56, 20.48]
367.88
18.394
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.722160577774048, 'TIME_S_1KI': 0.32498289266735514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.13037155151375, 'W': 22.887281127943623, 'J_1KI': 9.854525855530376, 'W_1KI': 0.6937011222969607, 'W_D': 4.493281127943625, 'J_D': 63.8303062057496, 'W_D_1KI': 0.13618892273947883, 'J_D_1KI': 0.0041278126493340655}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648, "TIME_S_1KI": 2.279479990598506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2094431686402, "W": 23.392743273719635, "J_1KI": 73.51392856132777, "W_1KI": 5.176530930232272, "W_D": 4.8837432737196345, "J_D": 69.35593720483783, "W_D_1KI": 1.0807132714582062, "J_D_1KI": 0.2391487655362262}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4704, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.513959884643555, "TIME_S_1KI": 2.235110519694633, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.1826779842377, "W": 23.013408423661655, "J_1KI": 69.9793107959689, "W_1KI": 4.892306212513107, "W_D": 4.506408423661657, "J_D": 64.45944753956805, "W_D_1KI": 0.9579949880233115, "J_D_1KI": 0.2036553971137992}

66
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output
View File

@ -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 100 -ss 10000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2727935314178467}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.28493499755859375}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 6, 18, ..., 99975, 99993,
tensor(crow_indices=tensor([ 0, 6, 15, ..., 99974, 99987,
100000]),
col_indices=tensor([2872, 4034, 5620, ..., 6357, 6556, 9590]),
values=tensor([0.7995, 0.0045, 0.2448, ..., 0.5761, 0.7842, 0.1546]),
col_indices=tensor([1008, 1745, 2458, ..., 8180, 8309, 8725]),
values=tensor([0.4541, 0.1167, 0.6157, ..., 0.2339, 0.7280, 0.6670]),
size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
tensor([0.8077, 0.7130, 0.7281, ..., 0.3829, 0.9486, 0.9162])
tensor([0.9535, 0.6938, 0.6793, ..., 0.3504, 0.5915, 0.1345])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 100000
Density: 0.001
Time: 0.2727935314178467 seconds
Time: 0.28493499755859375 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 3849 -ss 10000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.942286252975464}
['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 3685 -ss 10000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.224288940429688}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 12, 21, ..., 99991, 99998,
tensor(crow_indices=tensor([ 0, 8, 23, ..., 99975, 99993,
100000]),
col_indices=tensor([ 425, 574, 695, ..., 9570, 6024, 9715]),
values=tensor([0.7410, 0.8879, 0.5840, ..., 0.6995, 0.9280, 0.9465]),
col_indices=tensor([2342, 2426, 3411, ..., 3261, 4460, 9472]),
values=tensor([0.8447, 0.2534, 0.2074, ..., 0.5724, 0.1389, 0.7449]),
size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
tensor([0.2929, 0.5164, 0.5482, ..., 0.5103, 0.5008, 0.9557])
tensor([0.2993, 0.9441, 0.0750, ..., 0.0171, 0.8286, 0.1160])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,19 +36,19 @@ Rows: 10000
Size: 100000000
NNZ: 100000
Density: 0.001
Time: 8.942286252975464 seconds
Time: 8.224288940429688 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 4519 -ss 10000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648}
['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 4704 -ss 10000 -sd 0.001 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.513959884643555}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 16, ..., 99974, 99990,
tensor(crow_indices=tensor([ 0, 7, 18, ..., 99977, 99987,
100000]),
col_indices=tensor([ 582, 1691, 2515, ..., 7345, 7996, 8295]),
values=tensor([0.8177, 0.9283, 0.6030, ..., 0.2647, 0.3717, 0.8633]),
col_indices=tensor([ 68, 2486, 2822, ..., 8793, 8847, 9684]),
values=tensor([0.4423, 0.5768, 0.9908, ..., 0.4103, 0.7568, 0.2801]),
size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
tensor([0.2209, 0.7260, 0.0429, ..., 0.4887, 0.7834, 0.0043])
tensor([0.3663, 0.2060, 0.8473, ..., 0.8925, 0.9991, 0.3035])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -56,16 +56,16 @@ Rows: 10000
Size: 100000000
NNZ: 100000
Density: 0.001
Time: 10.300970077514648 seconds
Time: 10.513959884643555 seconds
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
matrix = matrix.to_sparse_csr().type(torch.float32)
tensor(crow_indices=tensor([ 0, 7, 16, ..., 99974, 99990,
tensor(crow_indices=tensor([ 0, 7, 18, ..., 99977, 99987,
100000]),
col_indices=tensor([ 582, 1691, 2515, ..., 7345, 7996, 8295]),
values=tensor([0.8177, 0.9283, 0.6030, ..., 0.2647, 0.3717, 0.8633]),
col_indices=tensor([ 68, 2486, 2822, ..., 8793, 8847, 9684]),
values=tensor([0.4423, 0.5768, 0.9908, ..., 0.4103, 0.7568, 0.2801]),
size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
tensor([0.2209, 0.7260, 0.0429, ..., 0.4887, 0.7834, 0.0043])
tensor([0.3663, 0.2060, 0.8473, ..., 0.8925, 0.9991, 0.3035])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -73,13 +73,13 @@ Rows: 10000
Size: 100000000
NNZ: 100000
Density: 0.001
Time: 10.300970077514648 seconds
Time: 10.513959884643555 seconds
[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48]
[20.4, 20.36, 23.16, 24.08, 27.0, 27.6, 28.72, 28.72, 26.24, 26.28, 24.44, 24.28, 24.24, 24.08]
14.201388835906982
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635}
[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48, 20.52, 20.6, 20.68, 20.64, 20.64, 20.52, 20.52, 20.48, 20.68, 20.72]
370.18
18.509
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635, 'J_1KI': 73.51392856132777, 'W_1KI': 5.176530930232272, 'W_D': 4.8837432737196345, 'J_D': 69.35593720483783, 'W_D_1KI': 1.0807132714582062, 'J_D_1KI': 0.2391487655362262}
[20.44, 20.44, 20.44, 20.4, 20.24, 20.32, 20.48, 20.48, 20.52, 20.68]
[20.64, 20.56, 20.48, 24.2, 26.08, 28.48, 29.28, 27.2, 26.72, 24.2, 24.2, 24.12, 24.08, 23.96]
14.303951501846313
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.513959884643555, 'TIME_S_1KI': 2.235110519694633, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.1826779842377, 'W': 23.013408423661655}
[20.44, 20.44, 20.44, 20.4, 20.24, 20.32, 20.48, 20.48, 20.52, 20.68, 20.24, 20.44, 20.28, 20.72, 20.96, 21.08, 20.96, 21.0, 20.48, 20.44]
370.14
18.506999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.513959884643555, 'TIME_S_1KI': 2.235110519694633, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.1826779842377, 'W': 23.013408423661655, 'J_1KI': 69.9793107959689, 'W_1KI': 4.892306212513107, 'W_D': 4.506408423661657, 'J_D': 64.45944753956805, 'W_D_1KI': 0.9579949880233115, 'J_D_1KI': 0.2036553971137992}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 490, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521, "TIME_S_1KI": 21.416943900439204, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.6961988353729, "W": 22.678695903983957, "J_1KI": 658.5636710925977, "W_1KI": 46.283052865273376, "W_D": 4.3196959039839555, "J_D": 61.46515012335775, "W_D_1KI": 8.815705926497868, "J_D_1KI": 17.991236584689528}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.031994819641113, "TIME_S_1KI": 21.527885879058182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.8022685432434, "W": 22.86777901563946, "J_1KI": 699.1464990198356, "W_1KI": 49.07248715802459, "W_D": 4.6127790156394575, "J_D": 65.71927542924874, "W_D_1KI": 9.898667415535316, "J_D_1KI": 21.241775569818273}

58
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output
View File

@ -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 100 -ss 10000 -sd 0.01 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.140977382659912}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.2486960887908936}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 103, 212, ..., 999798,
999901, 1000000]),
col_indices=tensor([ 63, 140, 146, ..., 9691, 9771, 9918]),
values=tensor([0.8748, 0.2571, 0.8906, ..., 0.1504, 0.2890, 0.7825]),
tensor(crow_indices=tensor([ 0, 77, 175, ..., 999811,
999910, 1000000]),
col_indices=tensor([ 35, 141, 347, ..., 9617, 9713, 9775]),
values=tensor([0.5684, 0.4118, 0.8956, ..., 0.8300, 0.5668, 0.8186]),
size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.5882, 0.3416, 0.1892, ..., 0.3016, 0.5220, 0.0626])
tensor([0.7862, 0.9671, 0.1334, ..., 0.0132, 0.8938, 0.7920])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,19 +16,19 @@ Rows: 10000
Size: 100000000
NNZ: 1000000
Density: 0.01
Time: 2.140977382659912 seconds
Time: 2.2486960887908936 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 490 -ss 10000 -sd 0.01 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521}
['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 466 -ss 10000 -sd 0.01 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.031994819641113}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 113, 202, ..., 999820,
999916, 1000000]),
col_indices=tensor([ 2, 35, 39, ..., 9519, 9605, 9656]),
values=tensor([0.0992, 0.7724, 0.9238, ..., 0.3639, 0.9758, 0.0697]),
tensor(crow_indices=tensor([ 0, 101, 225, ..., 999830,
999902, 1000000]),
col_indices=tensor([ 154, 165, 205, ..., 9812, 9815, 9915]),
values=tensor([0.8739, 0.8341, 0.5427, ..., 0.3042, 0.6360, 0.3675]),
size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.2199, 0.1288, 0.7757, ..., 0.1449, 0.2950, 0.2928])
tensor([0.1667, 0.6012, 0.2305, ..., 0.2181, 0.2842, 0.9004])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -36,16 +36,16 @@ Rows: 10000
Size: 100000000
NNZ: 1000000
Density: 0.01
Time: 10.49430251121521 seconds
Time: 10.031994819641113 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, 113, 202, ..., 999820,
999916, 1000000]),
col_indices=tensor([ 2, 35, 39, ..., 9519, 9605, 9656]),
values=tensor([0.0992, 0.7724, 0.9238, ..., 0.3639, 0.9758, 0.0697]),
tensor(crow_indices=tensor([ 0, 101, 225, ..., 999830,
999902, 1000000]),
col_indices=tensor([ 154, 165, 205, ..., 9812, 9815, 9915]),
values=tensor([0.8739, 0.8341, 0.5427, ..., 0.3042, 0.6360, 0.3675]),
size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
tensor([0.2199, 0.1288, 0.7757, ..., 0.1449, 0.2950, 0.2928])
tensor([0.1667, 0.6012, 0.2305, ..., 0.2181, 0.2842, 0.9004])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -53,13 +53,13 @@ Rows: 10000
Size: 100000000
NNZ: 1000000
Density: 0.01
Time: 10.49430251121521 seconds
Time: 10.031994819641113 seconds
[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92]
[19.96, 20.36, 20.52, 22.04, 24.08, 26.92, 27.96, 27.96, 26.64, 25.04, 24.52, 24.4, 24.4, 24.52]
14.229045629501343
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957}
[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92, 20.44, 20.36, 20.4, 20.52, 20.4, 20.64, 20.76, 20.52, 20.52, 20.52]
367.18
18.359
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957, 'J_1KI': 658.5636710925977, 'W_1KI': 46.283052865273376, 'W_D': 4.3196959039839555, 'J_D': 61.46515012335775, 'W_D_1KI': 8.815705926497868, 'J_D_1KI': 17.991236584689528}
[20.0, 20.04, 20.24, 20.28, 20.28, 20.28, 20.44, 20.2, 20.12, 20.16]
[20.16, 20.4, 20.28, 22.2, 24.0, 27.88, 28.52, 28.36, 27.2, 25.84, 24.16, 24.32, 24.36, 24.36]
14.247219562530518
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.031994819641113, 'TIME_S_1KI': 21.527885879058182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.8022685432434, 'W': 22.86777901563946}
[20.0, 20.04, 20.24, 20.28, 20.28, 20.28, 20.44, 20.2, 20.12, 20.16, 20.4, 20.44, 20.32, 20.44, 20.56, 20.32, 20.4, 20.36, 20.08, 20.04]
365.1
18.255000000000003
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.031994819641113, 'TIME_S_1KI': 21.527885879058182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.8022685432434, 'W': 22.86777901563946, 'J_1KI': 699.1464990198356, 'W_1KI': 49.07248715802459, 'W_D': 4.6127790156394575, 'J_D': 65.71927542924874, 'W_D_1KI': 9.898667415535316, 'J_D_1KI': 21.241775569818273}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
View File

@ -1 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151, "TIME_S_1KI": 105.75754880905151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.48722974777223, "W": 23.357767084816274, "J_1KI": 3574.8722974777224, "W_1KI": 233.57767084816274, "W_D": 4.891767084816273, "J_D": 74.86778412389756, "W_D_1KI": 48.91767084816273, "J_D_1KI": 489.1767084816273}
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.582204103469849, "TIME_S_1KI": 105.82204103469849, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 394.3855400466919, "W": 24.027936546671576, "J_1KI": 3943.8554004669195, "W_1KI": 240.27936546671577, "W_D": 5.732936546671578, "J_D": 94.09827063679698, "W_D_1KI": 57.32936546671578, "J_D_1KI": 573.2936546671577}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
View File

@ -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 100 -ss 10000 -sd 0.05 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151}
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.582204103469849}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 505, 1011, ..., 4998999,
4999505, 5000000]),
col_indices=tensor([ 17, 34, 93, ..., 9927, 9945, 9977]),
values=tensor([0.3942, 0.9668, 0.2842, ..., 0.3748, 0.5474, 0.6270]),
tensor(crow_indices=tensor([ 0, 506, 1036, ..., 4999012,
4999522, 5000000]),
col_indices=tensor([ 2, 6, 14, ..., 9953, 9962, 9983]),
values=tensor([0.7970, 0.3700, 0.8324, ..., 0.2223, 0.8075, 0.1339]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.3102, 0.7326, 0.1847, ..., 0.2267, 0.2009, 0.0941])
tensor([0.3881, 0.8882, 0.8978, ..., 0.9188, 0.6267, 0.4542])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -16,16 +16,16 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.575754880905151 seconds
Time: 10.582204103469849 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, 505, 1011, ..., 4998999,
4999505, 5000000]),
col_indices=tensor([ 17, 34, 93, ..., 9927, 9945, 9977]),
values=tensor([0.3942, 0.9668, 0.2842, ..., 0.3748, 0.5474, 0.6270]),
tensor(crow_indices=tensor([ 0, 506, 1036, ..., 4999012,
4999522, 5000000]),
col_indices=tensor([ 2, 6, 14, ..., 9953, 9962, 9983]),
values=tensor([0.7970, 0.3700, 0.8324, ..., 0.2223, 0.8075, 0.1339]),
size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
tensor([0.3102, 0.7326, 0.1847, ..., 0.2267, 0.2009, 0.0941])
tensor([0.3881, 0.8882, 0.8978, ..., 0.9188, 0.6267, 0.4542])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
@ -33,13 +33,13 @@ Rows: 10000
Size: 100000000
NNZ: 5000000
Density: 0.05
Time: 10.575754880905151 seconds
Time: 10.582204103469849 seconds
[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32]
[20.32, 20.16, 21.08, 22.56, 24.44, 27.6, 27.6, 29.28, 28.88, 28.12, 25.16, 24.16, 24.12, 24.4, 24.56]
15.30485463142395
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274}
[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32, 20.52, 20.56, 20.76, 20.76, 20.84, 20.84, 20.72, 20.56, 20.56, 20.48]
369.32
18.466
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274, 'J_1KI': 3574.8722974777224, 'W_1KI': 233.57767084816274, 'W_D': 4.891767084816273, 'J_D': 74.86778412389756, 'W_D_1KI': 48.91767084816273, 'J_D_1KI': 489.1767084816273}
[20.0, 20.2, 20.08, 20.2, 20.2, 20.12, 20.4, 20.28, 20.44, 20.72]
[20.6, 20.72, 21.08, 24.84, 27.04, 29.48, 31.2, 28.84, 27.88, 27.88, 26.0, 23.84, 24.0, 24.32, 24.6, 24.48]
16.41362500190735
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.582204103469849, 'TIME_S_1KI': 105.82204103469849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 394.3855400466919, 'W': 24.027936546671576}
[20.0, 20.2, 20.08, 20.2, 20.2, 20.12, 20.4, 20.28, 20.44, 20.72, 20.56, 20.44, 20.2, 20.24, 20.08, 20.28, 20.68, 20.68, 20.44, 20.6]
365.9
18.294999999999998
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.582204103469849, 'TIME_S_1KI': 105.82204103469849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 394.3855400466919, 'W': 24.027936546671576, 'J_1KI': 3943.8554004669195, 'W_1KI': 240.27936546671577, 'W_D': 5.732936546671578, 'J_D': 94.09827063679698, 'W_D_1KI': 57.32936546671578, 'J_D_1KI': 573.2936546671577}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
View File

@ -1 +1 @@
{"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": 21.58586049079895, "TIME_S_1KI": 215.8586049079895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 695.2596657943726, "W": 24.42843676698333, "J_1KI": 6952.596657943726, "W_1KI": 244.2843676698333, "W_D": 6.1854367669833294, "J_D": 176.04420374608034, "W_D_1KI": 61.854367669833294, "J_D_1KI": 618.543676698333}
{"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": 21.590675115585327, "TIME_S_1KI": 215.90675115585327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 698.2714468479157, "W": 24.533351073497297, "J_1KI": 6982.714468479157, "W_1KI": 245.33351073497298, "W_D": 6.067351073497296, "J_D": 172.68973977231983, "W_D_1KI": 60.67351073497296, "J_D_1KI": 606.7351073497296}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
View File

@ -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 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": 21.58586049079895}
{"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": 21.590675115585327}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 983, 1945, ..., 9997995,
9998975, 10000000]),
col_indices=tensor([ 7, 29, 32, ..., 9986, 9994, 9999]),
values=tensor([0.5805, 0.0545, 0.7779, ..., 0.8799, 0.6314, 0.5149]),
tensor(crow_indices=tensor([ 0, 953, 1990, ..., 9998028,
9999002, 10000000]),
col_indices=tensor([ 3, 5, 31, ..., 9963, 9981, 9996]),
values=tensor([0.0177, 0.1736, 0.9045, ..., 0.8807, 0.7948, 0.3225]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1246, 0.2739, 0.0084, ..., 0.7975, 0.3318, 0.0977])
tensor([0.5695, 0.4055, 0.5858, ..., 0.6808, 0.9483, 0.6795])
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: 21.58586049079895 seconds
Time: 21.590675115585327 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, 983, 1945, ..., 9997995,
9998975, 10000000]),
col_indices=tensor([ 7, 29, 32, ..., 9986, 9994, 9999]),
values=tensor([0.5805, 0.0545, 0.7779, ..., 0.8799, 0.6314, 0.5149]),
tensor(crow_indices=tensor([ 0, 953, 1990, ..., 9998028,
9999002, 10000000]),
col_indices=tensor([ 3, 5, 31, ..., 9963, 9981, 9996]),
values=tensor([0.0177, 0.1736, 0.9045, ..., 0.8807, 0.7948, 0.3225]),
size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
tensor([0.1246, 0.2739, 0.0084, ..., 0.7975, 0.3318, 0.0977])
tensor([0.5695, 0.4055, 0.5858, ..., 0.6808, 0.9483, 0.6795])
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: 21.58586049079895 seconds
Time: 21.590675115585327 seconds
[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04]
[20.8, 20.6, 23.72, 23.72, 26.04, 27.56, 30.88, 32.68, 29.8, 29.08, 27.52, 26.28, 24.44, 24.48, 24.48, 24.2, 24.2, 24.2, 24.12, 24.12, 24.28, 24.28, 24.4, 24.32, 24.16, 24.0, 24.08, 24.16]
28.461078882217407
{'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': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333}
[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04, 19.8, 19.8, 19.8, 19.96, 20.0, 20.08, 20.12, 20.36, 20.32, 20.36]
364.86
18.243000000000002
{'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': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333, 'J_1KI': 6952.596657943726, 'W_1KI': 244.2843676698333, 'W_D': 6.1854367669833294, 'J_D': 176.04420374608034, 'W_D_1KI': 61.854367669833294, 'J_D_1KI': 618.543676698333}
[20.76, 20.76, 20.76, 20.76, 20.48, 20.8, 20.84, 20.8, 20.76, 20.6]
[20.24, 20.28, 23.72, 25.04, 26.96, 30.6, 31.88, 31.88, 29.52, 29.44, 27.04, 25.04, 23.8, 23.76, 24.04, 24.2, 24.36, 24.36, 24.28, 24.28, 24.28, 24.2, 24.12, 24.32, 24.28, 24.52, 24.36, 24.52]
28.462130784988403
{'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': 21.590675115585327, 'TIME_S_1KI': 215.90675115585327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 698.2714468479157, 'W': 24.533351073497297}
[20.76, 20.76, 20.76, 20.76, 20.48, 20.8, 20.84, 20.8, 20.76, 20.6, 20.52, 20.48, 20.64, 20.56, 20.36, 20.36, 20.0, 20.12, 19.88, 20.04]
369.32000000000005
18.466
{'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': 21.590675115585327, 'TIME_S_1KI': 215.90675115585327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 698.2714468479157, 'W': 24.533351073497297, 'J_1KI': 6982.714468479157, 'W_1KI': 245.33351073497298, 'W_D': 6.067351073497296, 'J_D': 172.68973977231983, 'W_D_1KI': 60.67351073497296, 'J_D_1KI': 606.7351073497296}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
View File

@ -1 +1 @@
{"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": 42.006776571273804, "TIME_S_1KI": 420.06776571273804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.3857918548583, "W": 24.65839535462019, "J_1KI": 13233.857918548583, "W_1KI": 246.58395354620188, "W_D": 6.256395354620192, "J_D": 335.77305422592167, "W_D_1KI": 62.56395354620192, "J_D_1KI": 625.6395354620192}
{"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": 42.53456234931946, "TIME_S_1KI": 425.3456234931946, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.7017677497863, "W": 24.331627673949086, "J_1KI": 12587.017677497863, "W_1KI": 243.31627673949086, "W_D": 5.892627673949086, "J_D": 304.83208806586254, "W_D_1KI": 58.92627673949086, "J_D_1KI": 589.2627673949086}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
View File

@ -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 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": 42.006776571273804}
{"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": 42.53456234931946}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1950, 3929, ..., 19995954,
19997973, 20000000]),
col_indices=tensor([ 0, 10, 17, ..., 9977, 9980, 9990]),
values=tensor([0.1470, 0.8510, 0.9446, ..., 0.3735, 0.6466, 0.3885]),
tensor(crow_indices=tensor([ 0, 1981, 3961, ..., 19996043,
19998038, 20000000]),
col_indices=tensor([ 1, 3, 6, ..., 9979, 9991, 9993]),
values=tensor([0.0058, 0.7086, 0.6623, ..., 0.9502, 0.1257, 0.5097]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1499, 0.7404, 0.4886, ..., 0.1182, 0.4158, 0.3615])
tensor([0.3594, 0.8224, 0.5071, ..., 0.7554, 0.0445, 0.2812])
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: 42.006776571273804 seconds
Time: 42.53456234931946 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, 1950, 3929, ..., 19995954,
19997973, 20000000]),
col_indices=tensor([ 0, 10, 17, ..., 9977, 9980, 9990]),
values=tensor([0.1470, 0.8510, 0.9446, ..., 0.3735, 0.6466, 0.3885]),
tensor(crow_indices=tensor([ 0, 1981, 3961, ..., 19996043,
19998038, 20000000]),
col_indices=tensor([ 1, 3, 6, ..., 9979, 9991, 9993]),
values=tensor([0.0058, 0.7086, 0.6623, ..., 0.9502, 0.1257, 0.5097]),
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
tensor([0.1499, 0.7404, 0.4886, ..., 0.1182, 0.4158, 0.3615])
tensor([0.3594, 0.8224, 0.5071, ..., 0.7554, 0.0445, 0.2812])
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: 42.006776571273804 seconds
Time: 42.53456234931946 seconds
[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56]
[20.6, 20.56, 20.76, 24.64, 26.0, 26.92, 30.76, 29.04, 28.68, 29.52, 30.24, 30.24, 27.76, 27.48, 26.4, 24.64, 24.48, 24.32, 24.28, 24.28, 24.28, 24.08, 24.28, 24.28, 24.36, 24.2, 24.24, 24.6, 24.64, 24.52, 24.72, 24.48, 24.64, 24.4, 24.52, 24.52, 24.36, 24.4, 24.4, 24.4, 24.48, 24.68, 24.76, 24.56, 24.36, 24.16, 24.24, 24.4, 24.4, 24.76, 24.88, 24.96, 24.92]
53.668771743774414
{'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': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019}
[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56, 20.12, 19.92, 20.0, 20.28, 20.44, 20.72, 20.96, 21.04, 21.04, 21.04]
368.03999999999996
18.401999999999997
{'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': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019, 'J_1KI': 13233.857918548583, 'W_1KI': 246.58395354620188, 'W_D': 6.256395354620192, 'J_D': 335.77305422592167, 'W_D_1KI': 62.56395354620192, 'J_D_1KI': 625.6395354620192}
[20.48, 20.48, 20.28, 20.32, 20.32, 20.64, 20.72, 20.8, 20.8, 20.6]
[20.68, 20.52, 20.64, 22.6, 23.52, 25.2, 28.36, 29.8, 29.56, 30.0, 31.0, 27.24, 27.24, 27.16, 25.84, 24.08, 24.08, 24.32, 24.32, 24.2, 24.04, 24.08, 24.12, 24.16, 24.16, 24.28, 24.44, 24.36, 24.12, 23.96, 23.8, 24.08, 24.2, 24.32, 24.56, 24.56, 24.32, 24.32, 24.4, 24.28, 24.48, 24.4, 24.4, 24.36, 24.6, 24.52, 24.4, 24.52, 24.36, 24.24, 24.24]
51.731096029281616
{'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': 42.53456234931946, 'TIME_S_1KI': 425.3456234931946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.7017677497863, 'W': 24.331627673949086}
[20.48, 20.48, 20.28, 20.32, 20.32, 20.64, 20.72, 20.8, 20.8, 20.6, 19.76, 19.96, 20.32, 20.4, 20.48, 20.2, 20.28, 20.84, 20.88, 21.28]
368.78
18.439
{'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': 42.53456234931946, 'TIME_S_1KI': 425.3456234931946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.7017677497863, 'W': 24.331627673949086, 'J_1KI': 12587.017677497863, 'W_1KI': 243.31627673949086, 'W_D': 5.892627673949086, 'J_D': 304.83208806586254, 'W_D_1KI': 58.92627673949086, 'J_D_1KI': 589.2627673949086}

2
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
View File

@ -1 +1 @@
{"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": 67.20304369926453, "TIME_S_1KI": 672.0304369926453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1846.4787199783327, "W": 24.29422238106021, "J_1KI": 18464.787199783328, "W_1KI": 242.9422238106021, "W_D": 5.818222381060206, "J_D": 442.2131174325942, "W_D_1KI": 58.18222381060206, "J_D_1KI": 581.8222381060207}
{"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": 63.5993595123291, "TIME_S_1KI": 635.993595123291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1830.1276798915862, "W": 24.37217906347355, "J_1KI": 18301.27679891586, "W_1KI": 243.72179063473547, "W_D": 6.110179063473549, "J_D": 458.81854897069934, "W_D_1KI": 61.10179063473549, "J_D_1KI": 611.0179063473548}

42
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
View File

@ -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 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": 67.20304369926453}
{"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": 63.5993595123291}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 2926, 5920, ..., 29993999,
29997022, 30000000]),
col_indices=tensor([ 1, 4, 6, ..., 9978, 9982, 9992]),
values=tensor([0.3929, 0.6592, 0.7367, ..., 0.3321, 0.3012, 0.1502]),
tensor(crow_indices=tensor([ 0, 3020, 6019, ..., 29993935,
29996918, 30000000]),
col_indices=tensor([ 3, 10, 12, ..., 9996, 9998, 9999]),
values=tensor([0.4272, 0.9443, 0.2889, ..., 0.0892, 0.8844, 0.9121]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.6782, 0.5388, 0.0901, ..., 0.7339, 0.4235, 0.1483])
tensor([0.7267, 0.4804, 0.8266, ..., 0.7945, 0.0876, 0.7736])
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: 67.20304369926453 seconds
Time: 63.5993595123291 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, 2926, 5920, ..., 29993999,
29997022, 30000000]),
col_indices=tensor([ 1, 4, 6, ..., 9978, 9982, 9992]),
values=tensor([0.3929, 0.6592, 0.7367, ..., 0.3321, 0.3012, 0.1502]),
tensor(crow_indices=tensor([ 0, 3020, 6019, ..., 29993935,
29996918, 30000000]),
col_indices=tensor([ 3, 10, 12, ..., 9996, 9998, 9999]),
values=tensor([0.4272, 0.9443, 0.2889, ..., 0.0892, 0.8844, 0.9121]),
size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
tensor([0.6782, 0.5388, 0.0901, ..., 0.7339, 0.4235, 0.1483])
tensor([0.7267, 0.4804, 0.8266, ..., 0.7945, 0.0876, 0.7736])
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: 67.20304369926453 seconds
Time: 63.5993595123291 seconds
[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6]
[20.64, 20.6, 20.6, 21.32, 22.6, 24.24, 25.68, 28.84, 30.4, 30.64, 31.4, 31.32, 28.4, 28.16, 27.72, 26.8, 26.8, 25.96, 24.6, 24.4, 24.12, 24.12, 24.12, 24.08, 24.36, 24.56, 24.8, 24.88, 24.88, 24.92, 24.92, 24.72, 24.64, 24.44, 24.28, 24.52, 24.68, 24.64, 24.68, 24.64, 24.64, 24.52, 24.76, 24.84, 24.68, 24.72, 24.68, 24.68, 24.76, 24.68, 24.52, 24.2, 24.12, 24.12, 24.24, 24.48, 24.64, 24.76, 24.76, 24.52, 24.28, 24.32, 24.12, 24.04, 24.32, 24.32, 24.6, 24.52, 24.76, 24.72, 24.36, 24.12, 24.16, 24.36, 24.48]
76.0048496723175
{'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': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021}
[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6, 20.04, 20.16, 20.28, 20.16, 20.16, 20.64, 20.72, 20.72, 20.92, 20.68]
369.52000000000004
18.476000000000003
{'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': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021, 'J_1KI': 18464.787199783328, 'W_1KI': 242.9422238106021, 'W_D': 5.818222381060206, 'J_D': 442.2131174325942, 'W_D_1KI': 58.18222381060206, 'J_D_1KI': 581.8222381060207}
[20.12, 20.36, 20.28, 20.24, 20.4, 20.44, 20.32, 20.4, 20.48, 20.56]
[20.56, 20.48, 20.76, 22.32, 24.68, 26.0, 27.64, 30.32, 30.48, 30.52, 31.08, 31.48, 29.28, 29.36, 29.36, 28.84, 27.6, 26.24, 24.72, 24.8, 24.56, 24.56, 24.52, 24.24, 24.12, 24.36, 24.52, 24.52, 24.36, 24.48, 24.12, 23.72, 23.84, 24.24, 24.16, 24.24, 24.16, 24.2, 24.0, 24.0, 24.16, 24.32, 24.36, 24.48, 24.4, 24.32, 24.28, 24.16, 24.2, 24.08, 24.28, 24.36, 24.36, 24.4, 24.2, 24.28, 24.16, 24.28, 24.4, 24.48, 24.68, 24.72, 24.72, 24.56, 24.56, 24.48, 24.64, 24.44, 24.4, 24.44, 24.28, 24.24, 24.36, 24.52]
75.09085154533386
{'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': 63.5993595123291, 'TIME_S_1KI': 635.993595123291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1830.1276798915862, 'W': 24.37217906347355}
[20.12, 20.36, 20.28, 20.24, 20.4, 20.44, 20.32, 20.4, 20.48, 20.56, 20.2, 20.2, 20.36, 20.16, 20.08, 20.08, 20.12, 20.24, 20.4, 20.48]
365.24
18.262
{'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': 63.5993595123291, 'TIME_S_1KI': 635.993595123291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1830.1276798915862, 'W': 24.37217906347355, 'J_1KI': 18301.27679891586, 'W_1KI': 243.72179063473547, 'W_D': 6.110179063473549, 'J_D': 458.81854897069934, 'W_D_1KI': 61.10179063473549, 'J_D_1KI': 611.0179063473548}

1
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 83.99711680412292, "TIME_S_1KI": 839.9711680412292, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2478.5038306999204, "W": 24.954187412594408, "J_1KI": 24785.038306999206, "W_1KI": 249.54187412594408, "W_D": 6.705187412594405, "J_D": 665.9737066528793, "W_D_1KI": 67.05187412594405, "J_D_1KI": 670.5187412594405}

45
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.output Normal file
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@ -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 10000 -sd 0.4 -c 16']
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 83.99711680412292}
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 3989, 7950, ..., 39991913,
39995922, 40000000]),
col_indices=tensor([ 0, 1, 6, ..., 9996, 9997, 9999]),
values=tensor([0.4556, 0.9367, 0.5980, ..., 0.0177, 0.8725, 0.2828]),
size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
tensor([0.5334, 0.8652, 0.9709, ..., 0.8156, 0.3004, 0.2949])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 40000000
Density: 0.4
Time: 83.99711680412292 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, 3989, 7950, ..., 39991913,
39995922, 40000000]),
col_indices=tensor([ 0, 1, 6, ..., 9996, 9997, 9999]),
values=tensor([0.4556, 0.9367, 0.5980, ..., 0.0177, 0.8725, 0.2828]),
size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
tensor([0.5334, 0.8652, 0.9709, ..., 0.8156, 0.3004, 0.2949])
Matrix Type: synthetic
Matrix Format: csr
Shape: torch.Size([10000, 10000])
Rows: 10000
Size: 100000000
NNZ: 40000000
Density: 0.4
Time: 83.99711680412292 seconds
[20.2, 20.04, 20.04, 20.04, 20.28, 20.2, 20.28, 20.64, 20.48, 20.64]
[20.92, 20.76, 21.2, 24.8, 26.64, 27.52, 29.04, 30.4, 30.4, 31.44, 31.2, 31.8, 30.56, 27.28, 27.84, 27.44, 28.2, 27.6, 26.92, 25.92, 25.92, 24.76, 24.68, 24.72, 24.84, 24.84, 24.92, 25.08, 25.16, 25.12, 25.12, 24.8, 24.68, 24.68, 24.68, 24.76, 24.92, 24.84, 24.8, 25.0, 25.08, 25.08, 25.48, 25.56, 25.56, 25.56, 25.32, 25.2, 24.96, 24.6, 24.72, 24.72, 24.76, 25.04, 25.04, 24.96, 24.8, 24.72, 24.72, 24.6, 24.64, 24.8, 25.04, 24.96, 25.04, 25.08, 24.88, 24.68, 24.76, 24.8, 24.8, 24.88, 24.92, 25.12, 25.08, 25.16, 25.36, 25.28, 25.32, 25.4, 25.2, 24.96, 24.96, 24.96, 24.8, 25.0, 25.04, 25.0, 24.92, 24.88, 24.84, 24.8, 24.6, 24.44, 24.48, 24.48, 24.6, 24.72]
99.32216143608093
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 83.99711680412292, 'TIME_S_1KI': 839.9711680412292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2478.5038306999204, 'W': 24.954187412594408}
[20.2, 20.04, 20.04, 20.04, 20.28, 20.2, 20.28, 20.64, 20.48, 20.64, 20.4, 20.44, 20.36, 20.32, 20.24, 20.24, 20.24, 20.16, 20.16, 20.4]
364.98
18.249000000000002
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 83.99711680412292, 'TIME_S_1KI': 839.9711680412292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2478.5038306999204, 'W': 24.954187412594408, 'J_1KI': 24785.038306999206, 'W_1KI': 249.54187412594408, 'W_D': 6.705187412594405, 'J_D': 665.9737066528793, 'W_D_1KI': 67.05187412594405, 'J_D_1KI': 670.5187412594405}

1
pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.json Normal file
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@ -0,0 +1 @@
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 122.3178551197052, "TIME_S_1KI": 1223.178551197052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3064.272414226532, "W": 24.76047197717133, "J_1KI": 30642.72414226532, "W_1KI": 247.6047197717133, "W_D": 6.469471977171327, "J_D": 800.6400093073842, "W_D_1KI": 64.69471977171328, "J_D_1KI": 646.9471977171328}

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