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cephi 2024-12-09 15:06:46 -05:00
parent 496c515c9c
commit d4ef9f4346
98 changed files with 1086 additions and 19 deletions
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19
analysis/data_stat.py
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@ -108,25 +108,6 @@ names = {
}
}
def parse_output_old(filename: str, data: dict[str, str]) -> dict:
result: dict[str, int | float] = dict()
cpu: CPU = CPU[data['cpu'].upper()]
with open(filename, 'r') as file:
for line in file:
for stat in [x for x in Stat if x in cpu.value]:
regex = r'^\W*([\d+(,|\.)?]+)\W*.*' + cpu.value[stat]
value = re.search(regex, line)
if value is None:
continue
elif stat == Stat.TASK_CLK:
result[stat.value] = float(value.group(1).replace(',', ''))
else:
result[stat.value] = int(value.group(1).replace(',', ''))
return result | parse_power(filename, cpu)
def parse_output(output: str, cpu: Cpu) -> dict:
result = dict()

1
pytorch/output_test2/altra_10_10_ASIC_680k_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "ASIC_680k", "MATRIX_SHAPE": [682862, 682862], "MATRIX_SIZE": 466300511044, "MATRIX_NNZ": 3871773, "MATRIX_DENSITY": 8.303171256088674e-06, "TIME_S": 143.85276532173157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [22.6, 22.52, 22.76, 22.68, 22.88, 22.84, 22.72, 22.48, 22.36, 22.64], "POWER": [101.6, 97.76, 86.32, 69.24, 56.32, 52.24, 57.16, 57.16, 75.88, 89.6, 100.72, 101.6, 103.6, 103.0, 104.08, 109.28, 107.8, 106.44, 104.68, 104.68, 100.04, 98.72, 98.28, 101.4, 98.96, 97.04, 94.12, 92.84, 88.4, 88.8, 93.36, 93.36, 94.12, 94.92, 95.6, 92.16, 91.6, 94.88, 95.88, 97.28, 98.36, 98.64, 96.52, 96.52, 97.24, 98.36, 95.12, 94.92, 98.72, 97.52, 94.56, 96.2, 98.04, 98.52, 102.44, 106.08, 106.08, 108.4, 107.52, 104.56, 103.16, 101.8, 103.24, 107.64, 105.52, 103.64, 104.84, 101.64, 101.64, 98.92, 95.64, 96.16, 100.24, 104.36, 105.52, 105.64, 102.0, 97.16, 95.4, 98.28, 98.28, 100.16, 102.76, 101.96, 103.16, 101.8, 105.32, 100.96, 98.44, 97.68, 97.6, 97.16, 97.16, 100.4, 101.48, 100.6, 98.96, 95.88, 93.68, 93.72, 94.44, 98.64, 100.44, 99.52, 101.52, 101.52, 98.16, 97.68, 98.28, 101.56, 100.2, 102.72, 103.8, 100.68, 103.12, 102.24, 101.28, 101.28, 100.04, 97.48, 95.08, 95.8, 94.92, 96.12, 95.16, 100.08, 104.08, 104.48, 107.4, 107.4, 109.68, 102.6, 100.44, 102.16, 99.48, 97.88, 95.96, 98.92, 102.84, 101.36, 102.48, 102.48, 100.92, 100.68, 96.48, 100.0, 102.04], "JOULES": 14125.656173429492, "POWER_AFTER": [23.04, 23.28, 23.0, 22.88, 22.84, 22.84, 22.76, 22.8, 22.96, 22.8]}

26
pytorch/output_test2/altra_10_10_ASIC_680k_100000.output Normal file
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@ -0,0 +1,26 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471856 queued and waiting for resources
srun: job 3471856 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 4, ..., 3871767,
3871770, 3871773]),
col_indices=tensor([ 0, 11698, 11699, ..., 169456, 645874,
682861]),
values=tensor([ 3.8333e-04, -3.3333e-04, -5.0000e-05, ...,
0.0000e+00, 0.0000e+00, 7.9289e-02]),
size=(682862, 682862), nnz=3871773, layout=torch.sparse_csr)
tensor([0.6052, 0.7917, 0.7066, ..., 0.3876, 0.8366, 0.5267])
Matrix: ASIC_680k
Shape: torch.Size([682862, 682862])
Size: 466300511044
NNZ: 3871773
Density: 8.303171256088674e-06
Time: 143.85276532173157 seconds

1
pytorch/output_test2/altra_10_10_Oregon-2_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "Oregon-2", "MATRIX_SHAPE": [11806, 11806], "MATRIX_SIZE": 139381636, "MATRIX_NNZ": 65460, "MATRIX_DENSITY": 0.0004696458003979807, "TIME_S": 8.373449563980103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [22.8, 22.64, 22.52, 22.56, 22.68, 22.44, 22.44, 22.36, 22.24, 22.36], "POWER": [97.2, 97.84, 89.08, 72.32, 58.36, 58.4, 58.84, 74.52, 74.52, 88.96, 99.68], "JOULES": 612.2254525375366, "POWER_AFTER": [22.16, 21.92, 21.92, 21.96, 21.96, 22.36, 22.24, 22.24, 22.04, 21.88]}

23
pytorch/output_test2/altra_10_10_Oregon-2_100000.output Normal file
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@ -0,0 +1,23 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471857 queued and waiting for resources
srun: job 3471857 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 583, 584, ..., 65459, 65460, 65460]),
col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
nnz=65460, layout=torch.sparse_csr)
tensor([0.8374, 0.0143, 0.3251, ..., 0.2693, 0.4062, 0.8940])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
Size: 139381636
NNZ: 65460
Density: 0.0004696458003979807
Time: 8.373449563980103 seconds

1
pytorch/output_test2/altra_10_10_as-caida_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "as-caida", "MATRIX_SHAPE": [31379, 31379], "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106762, "MATRIX_DENSITY": 0.00010842726485909405, "TIME_S": 7.69922399520874, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.24, 21.28, 21.28, 21.12, 21.28, 21.0, 21.0, 21.16, 21.16, 21.08], "POWER": [101.04, 100.48, 90.04, 74.8, 62.16, 59.8, 62.6, 62.6, 77.84, 92.96, 103.24, 101.24, 101.84], "JOULES": 631.4889716720581, "POWER_AFTER": [21.24, 21.44, 21.36, 21.4, 21.4, 21.36, 21.44, 21.36, 21.28, 21.36]}

24
pytorch/output_test2/altra_10_10_as-caida_100000.output Normal file
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@ -0,0 +1,24 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471796 queued and waiting for resources
srun: job 3471796 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.3242, 0.9198, 0.8266, ..., 0.4648, 0.8946, 0.6351])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
Size: 984641641
NNZ: 106762
Density: 0.00010842726485909405
Time: 7.69922399520874 seconds

1
pytorch/output_test2/altra_10_10_dc2_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "dc2", "MATRIX_SHAPE": [116835, 116835], "MATRIX_SIZE": 13650417225, "MATRIX_NNZ": 766396, "MATRIX_DENSITY": 5.614451099680581e-05, "TIME_S": 37.14217662811279, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.24, 20.92, 21.08, 21.16, 21.36, 21.6, 21.6, 21.28, 21.04, 21.0], "POWER": [102.16, 103.44, 92.84, 76.08, 59.2, 59.48, 59.48, 66.88, 79.48, 96.96, 106.32, 105.92, 102.72, 102.68, 100.88, 99.92, 99.32, 98.04, 97.4, 97.4, 97.08, 94.72, 94.08, 96.16, 94.52, 95.32, 94.76, 92.16, 92.76, 95.88, 96.48, 96.48, 97.4, 98.08, 97.92, 97.56, 98.44, 97.36, 97.88, 99.72, 99.52, 99.0, 97.76, 96.36, 96.36], "JOULES": 3585.0201398849495, "POWER_AFTER": [23.36, 23.08, 22.72, 22.52, 22.52, 22.12, 22.0, 21.96, 21.72, 21.72]}

26
pytorch/output_test2/altra_10_10_dc2_100000.output Normal file
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@ -0,0 +1,26 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471782 queued and waiting for resources
srun: job 3471782 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 766390, 766394,
766396]),
col_indices=tensor([ 0, 1, 2, ..., 116833, 89,
116834]),
values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ...,
1.0331e+01, -1.0000e-03, 1.0000e-03]),
size=(116835, 116835), nnz=766396, layout=torch.sparse_csr)
tensor([0.1528, 0.7657, 0.8355, ..., 0.4682, 0.1999, 0.0103])
Matrix: dc2
Shape: torch.Size([116835, 116835])
Size: 13650417225
NNZ: 766396
Density: 5.614451099680581e-05
Time: 37.14217662811279 seconds

1
pytorch/output_test2/altra_10_10_de2010_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "de2010", "MATRIX_SHAPE": [24115, 24115], "MATRIX_SIZE": 581533225, "MATRIX_NNZ": 116056, "MATRIX_DENSITY": 0.0001995689928120616, "TIME_S": 8.169610738754272, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.48, 21.44, 21.32, 21.44, 21.6, 21.64, 21.44, 21.44, 21.4, 21.24], "POWER": [104.88, 103.2, 87.28, 72.12, 56.64, 56.64, 57.16, 62.64, 82.24, 97.56, 105.76, 102.64, 99.36], "JOULES": 638.1325230026245, "POWER_AFTER": [21.24, 21.32, 21.32, 21.24, 20.84, 20.84, 20.84, 20.84, 21.16, 21.56]}

25
pytorch/output_test2/altra_10_10_de2010_100000.output Normal file
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@ -0,0 +1,25 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471783 queued and waiting for resources
srun: job 3471783 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051,
116056]),
col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
16949.]), size=(24115, 24115), nnz=116056,
layout=torch.sparse_csr)
tensor([0.3069, 0.2208, 0.9592, ..., 0.2726, 0.0490, 0.9363])
Matrix: de2010
Shape: torch.Size([24115, 24115])
Size: 581533225
NNZ: 116056
Density: 0.0001995689928120616
Time: 8.169610738754272 seconds

1
pytorch/output_test2/altra_10_10_email-Enron_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "email-Enron", "MATRIX_SHAPE": [36692, 36692], "MATRIX_SIZE": 1346302864, "MATRIX_NNZ": 367662, "MATRIX_DENSITY": 0.0002730901120626302, "TIME_S": 12.88691234588623, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.76, 20.88, 20.84, 21.0, 21.16, 21.24, 21.44, 21.44, 21.48, 21.36], "POWER": [99.08, 99.68, 91.12, 77.0, 63.04, 57.0, 58.84, 75.68, 90.8, 90.8, 104.72, 103.32, 100.28, 97.32, 95.44, 93.56, 93.36], "JOULES": 1113.6021366119385, "POWER_AFTER": [21.72, 21.72, 21.8, 21.72, 21.92, 21.88, 21.88, 21.76, 21.44, 21.28]}

24
pytorch/output_test2/altra_10_10_email-Enron_100000.output Normal file
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@ -0,0 +1,24 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471786 queued and waiting for resources
srun: job 3471786 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
367662]),
col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
nnz=367662, layout=torch.sparse_csr)
tensor([0.6039, 0.3557, 0.6656, ..., 0.1586, 0.2866, 0.7610])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
Size: 1346302864
NNZ: 367662
Density: 0.0002730901120626302
Time: 12.88691234588623 seconds

1
pytorch/output_test2/altra_10_10_fl2010_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "fl2010", "MATRIX_SHAPE": [484481, 484481], "MATRIX_SIZE": 234721839361, "MATRIX_NNZ": 2346294, "MATRIX_DENSITY": 9.99606174861054e-06, "TIME_S": 31.069382905960083, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.64, 21.72, 21.64, 21.68, 21.4, 21.44, 21.24, 21.12, 21.12, 21.04], "POWER": [120.04, 120.92, 98.92, 79.32, 58.8, 57.84, 62.68, 62.68, 77.64, 100.44, 114.44, 116.72, 118.8, 121.36, 118.48, 116.4, 114.24, 113.88, 109.72, 109.72, 117.92, 119.64, 115.56, 112.28, 107.04, 104.52, 105.32, 109.56, 109.76, 110.6, 113.36, 113.36, 116.64], "JOULES": 3220.0128221511845, "POWER_AFTER": [22.36, 22.24, 22.52, 22.4, 22.44, 22.4, 22.28, 23.56, 23.56, 25.52]}

25
pytorch/output_test2/altra_10_10_fl2010_100000.output Normal file
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@ -0,0 +1,25 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471829 queued and waiting for resources
srun: job 3471829 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 2, 5, ..., 2346288,
2346292, 2346294]),
col_indices=tensor([ 1513, 5311, 947, ..., 484460, 482463,
484022]),
values=tensor([28364., 12497., 11567., ..., 8532., 22622., 35914.]),
size=(484481, 484481), nnz=2346294, layout=torch.sparse_csr)
tensor([0.5157, 0.5811, 0.2529, ..., 0.9249, 0.1469, 0.4136])
Matrix: fl2010
Shape: torch.Size([484481, 484481])
Size: 234721839361
NNZ: 2346294
Density: 9.99606174861054e-06
Time: 31.069382905960083 seconds

1
pytorch/output_test2/altra_10_10_ga2010_100000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "ga2010", "MATRIX_SHAPE": [291086, 291086], "MATRIX_SIZE": 84731059396, "MATRIX_NNZ": 1418056, "MATRIX_DENSITY": 1.6735964475229304e-05, "TIME_S": 17.813313722610474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.56, 21.72, 21.68, 21.56, 21.56, 21.76, 21.2, 21.16, 20.96, 20.96], "POWER": [115.36, 114.88, 106.84, 89.28, 71.36, 58.8, 57.92, 74.0, 91.48, 110.52, 113.56, 113.56, 116.72, 113.88, 117.16, 119.4, 113.4, 113.76, 111.48, 110.64, 115.04, 121.64], "JOULES": 1821.6114812183382, "POWER_AFTER": [21.56, 21.56, 21.4, 21.28, 21.36, 21.48, 21.64, 21.84, 21.68, 21.88]}

25
pytorch/output_test2/altra_10_10_ga2010_100000.output Normal file
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@ -0,0 +1,25 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471788 queued and waiting for resources
srun: job 3471788 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 10, ..., 1418047,
1418054, 1418056]),
col_indices=tensor([ 1566, 1871, 1997, ..., 291064, 289820,
290176]),
values=tensor([18760., 17851., 18847., ..., 65219., 56729., 77629.]),
size=(291086, 291086), nnz=1418056, layout=torch.sparse_csr)
tensor([0.6229, 0.6308, 0.8573, ..., 0.9191, 0.9418, 0.6011])
Matrix: ga2010
Shape: torch.Size([291086, 291086])
Size: 84731059396
NNZ: 1418056
Density: 1.6735964475229304e-05
Time: 17.813313722610474 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "mac_econ_fwd500", "MATRIX_SHAPE": [206500, 206500], "MATRIX_SIZE": 42642250000, "MATRIX_NNZ": 1273389, "MATRIX_DENSITY": 2.9862143765866013e-05, "TIME_S": 13.249896049499512, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.04, 21.08, 21.12, 21.24, 21.32, 21.32, 21.2, 21.04, 21.08, 21.08], "POWER": [109.92, 114.24, 106.48, 89.76, 70.72, 60.84, 56.4, 74.08, 74.08, 94.04, 110.84, 111.28, 109.44, 109.48, 110.12, 107.64, 114.36, 119.48, 121.56, 121.4], "JOULES": 1343.1373804092407, "POWER_AFTER": [21.44, 21.4, 21.24, 21.28, 21.92, 22.56, 23.52, 24.28, 24.28, 24.4]}

26
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471828 queued and waiting for resources
srun: job 3471828 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 1273376,
1273379, 1273389]),
col_indices=tensor([ 3, 30, 44, ..., 206363, 206408,
206459]),
values=tensor([-3.7877e-03, -1.5420e-01, 9.5305e-04, ...,
1.2290e-01, 2.2235e-01, -1.0000e+00]),
size=(206500, 206500), nnz=1273389, layout=torch.sparse_csr)
tensor([0.7751, 0.0281, 0.9910, ..., 0.3020, 0.8213, 0.1857])
Matrix: mac_econ_fwd500
Shape: torch.Size([206500, 206500])
Size: 42642250000
NNZ: 1273389
Density: 2.9862143765866013e-05
Time: 13.249896049499512 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "mc2depi", "MATRIX_SHAPE": [525825, 525825], "MATRIX_SIZE": 276491930625, "MATRIX_NNZ": 2100225, "MATRIX_DENSITY": 7.595972132902821e-06, "TIME_S": 19.404656887054443, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.72, 21.48, 21.32, 21.52, 21.52, 21.28, 21.4, 21.56, 21.56, 21.4], "POWER": [116.0, 110.56, 110.56, 91.76, 72.12, 61.36, 61.52, 74.2, 90.92, 117.8, 125.96, 128.68, 130.72, 124.72, 124.72, 115.08, 119.88, 117.36, 115.44, 110.44, 111.24, 110.4], "JOULES": 2049.1541203308107, "POWER_AFTER": [21.6, 21.64, 21.64, 21.84, 21.84, 21.76, 21.76, 21.72, 21.72, 21.4]}

25
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471790 queued and waiting for resources
srun: job 3471790 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 2, 5, ..., 2100220,
2100223, 2100225]),
col_indices=tensor([ 0, 1, 1, ..., 525824, 525821,
525824]),
values=tensor([-2025., 2025., -2026., ..., 2025., 1024., -1024.]),
size=(525825, 525825), nnz=2100225, layout=torch.sparse_csr)
tensor([0.4809, 0.5361, 0.4713, ..., 0.3506, 0.4153, 0.4817])
Matrix: mc2depi
Shape: torch.Size([525825, 525825])
Size: 276491930625
NNZ: 2100225
Density: 7.595972132902821e-06
Time: 19.404656887054443 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "p2p-Gnutella04", "MATRIX_SHAPE": [10879, 10879], "MATRIX_SIZE": 118352641, "MATRIX_NNZ": 39994, "MATRIX_DENSITY": 0.0003379223282393842, "TIME_S": 7.197759389877319, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.12, 21.2, 21.04, 20.92, 21.0, 21.04, 21.04, 20.72, 20.76, 21.12], "POWER": [100.84, 100.04, 85.4, 71.68, 71.68, 56.24, 57.76, 66.84, 79.88, 94.24, 101.2, 100.24, 98.4, 96.36, 95.08], "JOULES": 655.9629627895355, "POWER_AFTER": [21.68, 21.04, 20.88, 21.4, 21.28, 21.28, 21.32, 21.04, 21.04, 21.04]}

23
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471826 queued and waiting for resources
srun: job 3471826 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 10, 20, ..., 39994, 39994, 39994]),
col_indices=tensor([ 1, 2, 3, ..., 9711, 10875, 10876]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(10879, 10879),
nnz=39994, layout=torch.sparse_csr)
tensor([0.2810, 0.9768, 0.5232, ..., 0.2583, 0.8876, 0.2861])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
Size: 118352641
NNZ: 39994
Density: 0.0003379223282393842
Time: 7.197759389877319 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 100000, "MATRIX_FILE": "p2p-Gnutella24", "MATRIX_SHAPE": [26518, 26518], "MATRIX_SIZE": 703204324, "MATRIX_NNZ": 65369, "MATRIX_DENSITY": 9.295875717624285e-05, "TIME_S": 8.68448281288147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.84, 22.0, 21.92, 22.0, 21.92, 21.56, 21.52, 21.56, 21.48, 21.48], "POWER": [94.8, 95.36, 83.32, 69.76, 57.92, 59.48, 65.64, 65.64, 82.24, 99.92, 105.68, 103.0, 101.92, 99.32], "JOULES": 751.5028329753875, "POWER_AFTER": [24.12, 24.56, 24.56, 24.56, 24.28, 24.16, 24.24, 24.28, 24.28, 24.08]}

23
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471854 queued and waiting for resources
srun: job 3471854 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 9, 9, ..., 65369, 65369, 65369]),
col_indices=tensor([ 1, 2, 3, ..., 15065, 9401, 26517]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(26518, 26518),
nnz=65369, layout=torch.sparse_csr)
tensor([0.4761, 0.4887, 0.0195, ..., 0.5651, 0.2234, 0.2511])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
Size: 703204324
NNZ: 65369
Density: 9.295875717624285e-05
Time: 8.68448281288147 seconds

1
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471827 queued and waiting for resources
srun: job 3471827 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 9, 9, ..., 54704, 54704, 54705]),
col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687),
nnz=54705, layout=torch.sparse_csr)
tensor([0.4250, 0.5008, 0.7599, ..., 0.4696, 0.2842, 0.9247])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
Size: 514699969
NNZ: 54705
Density: 0.00010628522108964806
Time: 8.185347080230713 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471825 queued and waiting for resources
srun: job 3471825 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 10, 10, ..., 88328, 88328, 88328]),
col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682),
nnz=88328, layout=torch.sparse_csr)
tensor([0.5280, 0.0933, 0.8124, ..., 0.0433, 0.2447, 0.2625])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
Size: 1345569124
NNZ: 88328
Density: 6.564359899804003e-05
Time: 9.74808645248413 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471779 queued and waiting for resources
srun: job 3471779 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
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values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.2875, 0.2982, 0.0876, ..., 0.4058, 0.8442, 0.7364])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
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NNZ: 125750
Density: 0.00019831796057928155
Time: 7.650730133056641 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471832 queued and waiting for resources
srun: job 3471832 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 17, 34, ..., 2373939,
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col_indices=tensor([ 0, 1, 2, ..., 46831, 46833, 46834]),
values=tensor([ 1.2636e+05, -1.6615e+07, -8.2015e+04, ...,
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size=(46835, 46835), nnz=2374001, layout=torch.sparse_csr)
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Shape: torch.Size([46835, 46835])
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471855 queued and waiting for resources
srun: job 3471855 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 545669, 545669,
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col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871),
nnz=545671, layout=torch.sparse_csr)
tensor([0.7599, 0.3131, 0.1356, ..., 0.5599, 0.7303, 0.7084])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
Size: 6702860641
NNZ: 545671
Density: 8.140867447881048e-05
Time: 21.485024452209473 seconds

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24
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471791 queued and waiting for resources
srun: job 3471791 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.7291, 0.8277, 0.0975, ..., 0.0057, 0.6109, 0.6944])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
Size: 6747636736
NNZ: 549202
Density: 8.13917555860553e-05
Time: 9.906620264053345 seconds

1
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25
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471823 queued and waiting for resources
srun: job 3471823 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 1, 2, ..., 841371, 841371,
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.4186, 0.4768, 0.7650, ..., 0.7266, 0.5735, 0.6056])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
Size: 17378621584
NNZ: 841372
Density: 4.841419648464106e-05
Time: 31.47378420829773 seconds

1
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24
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471784 queued and waiting for resources
srun: job 3471784 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
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values=tensor([151., 17., 6., ..., 1., 1., 1.]),
size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.0721, 0.7772, 0.5440, ..., 0.2599, 0.9247, 0.3684])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
Size: 615933124
NNZ: 239978
Density: 0.00038961697406616504
Time: 9.512531042098999 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471794 queued and waiting for resources
srun: job 3471794 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 4, 20, ..., 1193961,
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tensor([0.7187, 0.4492, 0.0121, ..., 0.1002, 0.2839, 0.4108])
Matrix: tn2010
Shape: torch.Size([240116, 240116])
Size: 57655693456
NNZ: 1193966
Density: 2.070855328296721e-05
Time: 16.210495948791504 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471824 queued and waiting for resources
srun: job 3471824 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 9, ..., 572056, 572061,
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tensor([0.7125, 0.8600, 0.2723, ..., 0.9659, 0.9794, 0.8036])
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Shape: torch.Size([115406, 115406])
Size: 13318544836
NNZ: 572066
Density: 4.295259032005559e-05
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471831 queued and waiting for resources
srun: job 3471831 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
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tensor(crow_indices=tensor([ 0, 2, 8, ..., 1402119,
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tensor([0.4623, 0.7205, 0.5451, ..., 0.0101, 0.1478, 0.8275])
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Shape: torch.Size([285762, 285762])
Size: 81659920644
NNZ: 1402128
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3471830 queued and waiting for resources
srun: job 3471830 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If 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.)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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tensor([0.7636, 0.2831, 0.7866, ..., 0.4359, 0.2796, 0.7453])
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1
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3471836 queued and waiting for resources
srun: job 3471836 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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tensor(crow_indices=tensor([ 0, 3, 4, ..., 3871767,
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tensor([0.9586, 0.4554, 0.3276, ..., 0.2106, 0.5062, 0.3303])
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 583, 584, ..., 65459, 65460, 65460]),
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tensor([0.4829, 0.4475, 0.1256, ..., 0.6137, 0.5875, 0.8973])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
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Time: 4.933578252792358 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3471778 queued and waiting for resources
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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srun: job 3471780 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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tensor(crow_indices=tensor([ 0, 3, 10, ..., 1418047,
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 9, 9, ..., 54704, 54704, 54705]),
col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687),
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tensor([0.2183, 0.6576, 0.4780, ..., 0.0534, 0.0208, 0.5648])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
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NNZ: 54705
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Time: 5.152007102966309 seconds

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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 10, 10, ..., 88328, 88328, 88328]),
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tensor([0.5178, 0.0281, 0.3608, ..., 0.6911, 0.2357, 0.6596])
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 17, 34, ..., 2373939,
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 545669, 545669,
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
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).to_sparse_csr().type(torch.float)
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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tensor([3.0726e-01, 6.8295e-01, 5.4596e-01, ..., 3.6135e-01, 9.3459e-01,
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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tensor([0.9535, 0.8300, 0.1451, ..., 0.8613, 0.5153, 0.2159])
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/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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srun: job 3471811 queued and waiting for resources
srun: job 3471811 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:22: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
155598]),
col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.9036, 0.7985, 0.5047, ..., 0.6402, 0.1482, 0.0115])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
Size: 1061456400
NNZ: 155598
Density: 0.00014658915806621921
Time: 7.804270267486572 seconds

2
pytorch/spmv.py
View File

@ -29,6 +29,8 @@ print(vector, file=sys.stderr)
start = time.time()
for i in range(0, args.iterations):
torch.mv(matrix, vector)
#torch.sparse.mm(matrix, vector.unsqueeze(-1)).squeeze(-1)
#print(i)
end = time.time()
result = dict()