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Update with test data

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This commit is contained in:
cephi 2024-12-09 10:57:15 -05:00
parent 81b966f485
commit e452ad60fe
104 changed files with 1191 additions and 39 deletions
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16
pytorch/batch.py Executable file → Normal file
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@ -1,5 +1,3 @@
#! /bin/python3
from data_stat import Cpu from data_stat import Cpu
import argparse import argparse
@ -34,8 +32,20 @@ srun_args = {
#'--exclusive', #'--exclusive',
#'--output', '/dev/null', #'--output', '/dev/null',
#'--error', '/dev/null' #'--error', '/dev/null'
],
Cpu.EPYC_7313P: [
'--account', 'nexus',
'--partition', 'tron',
'--qos', 'high',
'--cpus-per-task', '16',
'--ntasks-per-node', '1',
'--prefer', 'EPYC-7313P'
] ]
} }
python = {
Cpu.ALTRA: 'python3',
Cpu.EPYC_7313P: 'python3.11'
}
def srun(srun_args_list: list, run_args, matrix_file: str) -> list: def srun(srun_args_list: list, run_args, matrix_file: str) -> list:
run_args_list = [ run_args_list = [
@ -48,7 +58,7 @@ def srun(srun_args_list: list, run_args, matrix_file: str) -> list:
run_args_list += [args.perf] run_args_list += [args.perf]
if args.power is not None: if args.power is not None:
run_args_list += [args.power] run_args_list += [args.power]
return ['srun'] + srun_args_list + ['./run.py'] + run_args_list return ['srun'] + srun_args_list + [python[args.cpu], 'run.py'] + run_args_list
processes = list() processes = list()

5
pytorch/build.sh
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@ -1,13 +1,14 @@
#! /bin/bash #! /bin/bash
image_name="$1" containerfile_name="$1"
image_name="${containerfile_name%.*}"
if [[ -z "$1" ]]; then if [[ -z "$1" ]]; then
echo "Missing image name argument!" echo "Missing image name argument!"
exit 1 exit 1
fi fi
podman build . -t "$image_name":latest -f "$image_name".Containerfile && \ podman build . -t "$image_name":latest -f "$containerfile_name" && \
podman save localhost/"$image_name":latest -o "$image_name".img && \ podman save localhost/"$image_name":latest -o "$image_name".img && \
rm -fv "$image_name".sif && \ rm -fv "$image_name".sif && \
apptainer pull "$image_name".sif docker-archive:"$image_name".img apptainer pull "$image_name".sif docker-archive:"$image_name".img

1
pytorch/output_cpu/altra_10_10_ASIC_680k_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "ASIC_680k", "MATRIX_SHAPE": [682862, 682862], "MATRIX_SIZE": 466300511044, "MATRIX_NNZ": 3871773, "MATRIX_DENSITY": 8.303171256088674e-06, "TIME_S": 11.77456283569336, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.36, 20.44, 20.48, 20.72, 20.8, 21.0, 21.32, 21.32, 21.28, 21.08], "POWER": [92.0, 91.8, 78.72, 66.68, 51.2, 46.6, 53.36, 53.36, 70.48, 90.16, 100.04, 103.68, 98.2, 95.64, 97.16, 101.4], "JOULES": 938.4206715393068, "POWER_AFTER": [20.96, 20.76, 20.76, 21.08, 21.24, 21.16, 21.28, 21.2, 21.0, 21.08]}

26
pytorch/output_cpu/altra_10_10_ASIC_680k_10000.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 3471013 queued and waiting for resources
srun: job 3471013 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([0.6902, 0.5218, 0.8924, ..., 0.0864, 0.5539, 0.5194])
Matrix: ASIC_680k
Shape: torch.Size([682862, 682862])
Size: 466300511044
NNZ: 3871773
Density: 8.303171256088674e-06
Time: 11.77456283569336 seconds

1
pytorch/output_cpu/altra_10_10_Oregon-2_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "Oregon-2", "MATRIX_SHAPE": [11806, 11806], "MATRIX_SIZE": 139381636, "MATRIX_NNZ": 65460, "MATRIX_DENSITY": 0.0004696458003979807, "TIME_S": 0.9880795478820801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.04, 21.12, 21.2, 21.12, 21.04, 20.96, 20.92, 20.88, 21.16, 21.08], "POWER": [25.92, 42.32, 42.32, 45.44, 45.4], "JOULES": 44.85881147384644, "POWER_AFTER": [20.72, 20.72, 20.84, 20.84, 20.84, 20.96, 20.92, 20.6, 20.68, 20.84]}

23
pytorch/output_cpu/altra_10_10_Oregon-2_10000.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 3471014 queued and waiting for resources
srun: job 3471014 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([0.2158, 0.5422, 0.9585, ..., 0.6377, 0.8158, 0.5743])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
Size: 139381636
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.9880795478820801 seconds

1
pytorch/output_cpu/altra_10_10_as-caida_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "as-caida", "MATRIX_SHAPE": [31379, 31379], "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106762, "MATRIX_DENSITY": 0.00010842726485909405, "TIME_S": 1.066300630569458, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.64, 20.48, 20.68, 20.64, 20.32, 20.32, 20.4, 20.2, 20.52, 20.52], "POWER": [26.32, 39.88, 50.16, 50.64, 50.24], "JOULES": 53.97094367980957, "POWER_AFTER": [20.28, 20.4, 20.2, 20.32, 20.32, 20.4, 20.48, 20.28, 20.28, 20.44]}

24
pytorch/output_cpu/altra_10_10_as-caida_10000.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 3470988 queued and waiting for resources
srun: job 3470988 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([0.8877, 0.6518, 0.0601, ..., 0.0372, 0.4806, 0.8853])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
Size: 984641641
NNZ: 106762
Density: 0.00010842726485909405
Time: 1.066300630569458 seconds

1
pytorch/output_cpu/altra_10_10_dc2_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "dc2", "MATRIX_SHAPE": [116835, 116835], "MATRIX_SIZE": 13650417225, "MATRIX_NNZ": 766396, "MATRIX_DENSITY": 5.614451099680581e-05, "TIME_S": 3.0164122581481934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.44, 20.72, 20.72, 21.0, 20.84, 21.08, 20.88, 20.8, 20.8, 20.88], "POWER": [64.4, 79.8, 83.24, 75.76, 58.2, 58.2, 56.64, 60.64, 75.88, 93.68], "JOULES": 194.69750034332276, "POWER_AFTER": [21.12, 21.0, 21.12, 20.88, 20.88, 20.84, 20.96, 20.92, 20.88, 20.8]}

26
pytorch/output_cpu/altra_10_10_dc2_10000.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 3470982 queued and waiting for resources
srun: job 3470982 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([0.3305, 0.9342, 0.6954, ..., 0.1999, 0.9064, 0.6304])
Matrix: dc2
Shape: torch.Size([116835, 116835])
Size: 13650417225
NNZ: 766396
Density: 5.614451099680581e-05
Time: 3.0164122581481934 seconds

1
pytorch/output_cpu/altra_10_10_de2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "de2010", "MATRIX_SHAPE": [24115, 24115], "MATRIX_SIZE": 581533225, "MATRIX_NNZ": 116056, "MATRIX_DENSITY": 0.0001995689928120616, "TIME_S": 1.1378686428070068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [21.0, 20.88, 21.04, 20.8, 20.8, 20.44, 20.64, 20.48, 20.28, 20.16], "POWER": [22.84, 39.8, 49.48, 50.32, 50.28], "JOULES": 57.25203536033631, "POWER_AFTER": [20.68, 20.44, 20.68, 20.68, 20.56, 20.88, 20.92, 20.88, 21.0, 20.96]}

25
pytorch/output_cpu/altra_10_10_de2010_10000.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 3470980 queued and waiting for resources
srun: job 3470980 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([0.3562, 0.7994, 0.9047, ..., 0.2891, 0.3611, 0.5704])
Matrix: de2010
Shape: torch.Size([24115, 24115])
Size: 581533225
NNZ: 116056
Density: 0.0001995689928120616
Time: 1.1378686428070068 seconds

1
pytorch/output_cpu/altra_10_10_email-Enron_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "email-Enron", "MATRIX_SHAPE": [36692, 36692], "MATRIX_SIZE": 1346302864, "MATRIX_NNZ": 367662, "MATRIX_DENSITY": 0.0002730901120626302, "TIME_S": 1.3314027786254883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.8, 20.64, 20.6, 20.6, 20.48, 20.8, 20.72, 20.72, 20.92, 20.92], "POWER": [28.4, 43.96, 54.4, 55.28, 55.08], "JOULES": 73.5336650466919, "POWER_AFTER": [20.88, 20.8, 20.8, 20.8, 20.64, 20.64, 20.64, 20.48, 20.52, 20.72]}

24
pytorch/output_cpu/altra_10_10_email-Enron_10000.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 3470985 queued and waiting for resources
srun: job 3470985 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([0.7107, 0.7540, 0.8321, ..., 0.9503, 0.7781, 0.9277])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
Size: 1346302864
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.3314027786254883 seconds

1
pytorch/output_cpu/altra_10_10_fl2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "fl2010", "MATRIX_SHAPE": [484481, 484481], "MATRIX_SIZE": 234721839361, "MATRIX_NNZ": 2346294, "MATRIX_DENSITY": 9.99606174861054e-06, "TIME_S": 2.924255609512329, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.8, 20.88, 20.72, 20.64, 20.56, 20.92, 20.92, 21.0, 20.96, 20.84], "POWER": [73.32, 93.24, 93.64, 82.2, 61.36, 61.36, 58.0], "JOULES": 176.3268253517151, "POWER_AFTER": [20.76, 20.56, 20.76, 20.72, 20.76, 20.76, 20.76, 20.88, 20.68, 20.68]}

25
pytorch/output_cpu/altra_10_10_fl2010_10000.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 3471002 queued and waiting for resources
srun: job 3471002 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([0.5561, 0.7849, 0.5628, ..., 0.5545, 0.2543, 0.1741])
Matrix: fl2010
Shape: torch.Size([484481, 484481])
Size: 234721839361
NNZ: 2346294
Density: 9.99606174861054e-06
Time: 2.924255609512329 seconds

1
pytorch/output_cpu/altra_10_10_ga2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "ga2010", "MATRIX_SHAPE": [291086, 291086], "MATRIX_SIZE": 84731059396, "MATRIX_NNZ": 1418056, "MATRIX_DENSITY": 1.6735964475229304e-05, "TIME_S": 2.341104745864868, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.32, 20.28, 20.24, 20.44, 20.52, 20.8, 20.64, 20.68, 20.6, 20.36], "POWER": [33.84, 53.08, 66.2, 66.52, 67.36, 59.0], "JOULES": 154.00518000602722, "POWER_AFTER": [20.28, 20.32, 20.52, 20.6, 20.6, 20.84, 21.12, 20.96, 20.76, 20.8]}

25
pytorch/output_cpu/altra_10_10_ga2010_10000.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 3470989 queued and waiting for resources
srun: job 3470989 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([0.0746, 0.8150, 0.2560, ..., 0.7929, 0.2552, 0.7733])
Matrix: ga2010
Shape: torch.Size([291086, 291086])
Size: 84731059396
NNZ: 1418056
Density: 1.6735964475229304e-05
Time: 2.341104745864868 seconds

1
pytorch/output_cpu/altra_10_10_mac_econ_fwd500_10000.json Normal file
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "mac_econ_fwd500", "MATRIX_SHAPE": [206500, 206500], "MATRIX_SIZE": 42642250000, "MATRIX_NNZ": 1273389, "MATRIX_DENSITY": 2.9862143765866013e-05, "TIME_S": 1.6093401908874512, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.76, 20.72, 20.28, 20.2, 20.24, 20.56, 20.72, 21.12, 21.24, 21.0], "POWER": [48.6, 65.2, 65.2, 61.84, 62.88, 59.36], "JOULES": 99.0504337310791, "POWER_AFTER": [20.76, 20.4, 20.64, 20.68, 20.68, 20.56, 20.48, 20.68, 20.64, 20.88]}

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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 3471003 queued and waiting for resources
srun: job 3471003 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([0.8982, 0.5128, 0.1053, ..., 0.5733, 0.7437, 0.9673])
Matrix: mac_econ_fwd500
Shape: torch.Size([206500, 206500])
Size: 42642250000
NNZ: 1273389
Density: 2.9862143765866013e-05
Time: 1.6093401908874512 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "mc2depi", "MATRIX_SHAPE": [525825, 525825], "MATRIX_SIZE": 276491930625, "MATRIX_NNZ": 2100225, "MATRIX_DENSITY": 7.595972132902821e-06, "TIME_S": 2.123237371444702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.68, 20.68, 20.68, 20.64, 20.72, 20.6, 20.84, 20.76, 20.92, 20.96], "POWER": [52.52, 76.2, 82.92, 85.4, 72.28, 58.76], "JOULES": 164.92142794609072, "POWER_AFTER": [20.68, 20.72, 20.84, 20.88, 20.84, 21.16, 21.04, 21.16, 20.88, 20.88]}

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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 3470981 queued and waiting for resources
srun: job 3470981 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([0.8254, 0.0543, 0.1764, ..., 0.7650, 0.8254, 0.6404])
Matrix: mc2depi
Shape: torch.Size([525825, 525825])
Size: 276491930625
NNZ: 2100225
Density: 7.595972132902821e-06
Time: 2.123237371444702 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "p2p-Gnutella04", "MATRIX_SHAPE": [10879, 10879], "MATRIX_SIZE": 118352641, "MATRIX_NNZ": 39994, "MATRIX_DENSITY": 0.0003379223282393842, "TIME_S": 0.9692902565002441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.6, 20.48, 20.64, 20.64, 20.64, 20.56, 20.52, 20.44, 20.24, 20.12], "POWER": [25.92, 43.16, 50.56, 48.4, 49.28], "JOULES": 47.76662384033203, "POWER_AFTER": [20.4, 20.52, 20.44, 20.64, 20.72, 20.64, 20.8, 20.6, 20.6, 20.64]}

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 3471000 queued and waiting for resources
srun: job 3471000 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([0.2688, 0.1431, 0.7891, ..., 0.0735, 0.7672, 0.4174])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
Size: 118352641
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.9692902565002441 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "p2p-Gnutella24", "MATRIX_SHAPE": [26518, 26518], "MATRIX_SIZE": 703204324, "MATRIX_NNZ": 65369, "MATRIX_DENSITY": 9.295875717624285e-05, "TIME_S": 0.9848971366882324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [16.32, 16.36, 16.36, 16.32, 16.56, 16.64, 16.72, 16.92, 16.76, 16.96], "POWER": [22.56, 40.8, 42.16, 42.16, 39.84], "JOULES": 39.23830192565919, "POWER_AFTER": [16.56, 16.44, 16.44, 16.68, 16.72, 16.72, 16.76, 16.68, 16.68, 16.92]}

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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 3471012 queued and waiting for resources
srun: job 3471012 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([0.6126, 0.7089, 0.2938, ..., 0.5143, 0.3903, 0.8766])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
Size: 703204324
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.9848971366882324 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "p2p-Gnutella25", "MATRIX_SHAPE": [22687, 22687], "MATRIX_SIZE": 514699969, "MATRIX_NNZ": 54705, "MATRIX_DENSITY": 0.00010628522108964806, "TIME_S": 1.064000129699707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.4, 20.68, 20.76, 20.6, 20.64, 20.48, 20.36, 20.48, 20.52, 20.52], "POWER": [33.4, 49.92, 52.44, 52.44, 51.68], "JOULES": 55.747526702880855, "POWER_AFTER": [20.96, 20.76, 20.96, 21.08, 20.64, 20.84, 20.84, 20.56, 20.28, 20.48]}

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 3470999 queued and waiting for resources
srun: job 3470999 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([0.1096, 0.4722, 0.2402, ..., 0.8482, 0.4609, 0.1028])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
Size: 514699969
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.064000129699707 seconds

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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "p2p-Gnutella30", "MATRIX_SHAPE": [36682, 36682], "MATRIX_SIZE": 1345569124, "MATRIX_NNZ": 88328, "MATRIX_DENSITY": 6.564359899804003e-05, "TIME_S": 1.022092580795288, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.44, 20.56, 20.76, 20.6, 20.64, 21.08, 20.76, 20.32, 20.32, 20.44], "POWER": [25.64, 36.88, 51.72, 49.6, 50.84], "JOULES": 50.723186807632445, "POWER_AFTER": [20.56, 20.68, 20.6, 20.88, 21.08, 20.76, 20.76, 20.92, 20.32, 20.24]}

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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 3471007 queued and waiting for resources
srun: job 3471007 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([0.4265, 0.5292, 0.2746, ..., 0.3064, 0.8544, 0.6969])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
Size: 1345569124
NNZ: 88328
Density: 6.564359899804003e-05
Time: 1.022092580795288 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 3470987 queued and waiting for resources
srun: job 3470987 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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col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.8235, 0.3045, 0.3176, ..., 0.8277, 0.2909, 0.5754])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
Size: 634082761
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.7675364017486572 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 3471006 queued and waiting for resources
srun: job 3471006 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)
tensor([0.3759, 0.1778, 0.4707, ..., 0.4812, 0.6721, 0.5216])
Matrix: rma10
Shape: torch.Size([46835, 46835])
Size: 2193517225
NNZ: 2374001
Density: 0.0010822805369125833
Time: 2.688584089279175 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 3471015 queued and waiting for resources
srun: job 3471015 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),
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tensor([0.7292, 0.5775, 0.7105, ..., 0.2374, 0.7415, 0.8438])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
Size: 6702860641
NNZ: 545671
Density: 8.140867447881048e-05
Time: 1.4809374809265137 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 3470983 queued and waiting for resources
srun: job 3470983 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,
549202]),
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([0.2718, 0.1909, 0.9904, ..., 0.8130, 0.5743, 0.4283])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
Size: 6747636736
NNZ: 549202
Density: 8.13917555860553e-05
Time: 1.608903408050537 seconds

1
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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "soc-sign-epinions", "MATRIX_SHAPE": [131828, 131828], "MATRIX_SIZE": 17378621584, "MATRIX_NNZ": 841372, "MATRIX_DENSITY": 4.841419648464106e-05, "TIME_S": 4.555854320526123, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [16.4, 16.36, 16.48, 16.68, 16.32, 16.32, 16.56, 16.56, 16.64, 16.64], "POWER": [51.6, 68.68, 77.56, 77.4, 61.4, 55.08, 54.44, 65.6], "JOULES": 284.7840434265137, "POWER_AFTER": [16.92, 16.88, 17.04, 16.92, 16.84, 16.92, 16.88, 16.8, 17.12, 17.12]}

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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 3470998 queued and waiting for resources
srun: job 3470998 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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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.6727, 0.2484, 0.1189, ..., 0.2578, 0.7441, 0.8799])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
Size: 17378621584
NNZ: 841372
Density: 4.841419648464106e-05
Time: 4.555854320526123 seconds

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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "sx-mathoverflow", "MATRIX_SHAPE": [24818, 24818], "MATRIX_SIZE": 615933124, "MATRIX_NNZ": 239978, "MATRIX_DENSITY": 0.00038961697406616504, "TIME_S": 1.0039293766021729, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.88, 21.0, 21.0, 20.92, 20.92, 20.8, 20.6, 20.6, 20.76, 20.92], "POWER": [29.76, 49.24, 50.6, 47.84, 47.84], "JOULES": 48.02798137664795, "POWER_AFTER": [20.96, 20.8, 20.92, 21.68, 22.4, 23.04, 23.76, 23.12, 22.6, 21.8]}

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 3470984 queued and waiting for resources
srun: job 3470984 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, 317, 416, ..., 239976, 239977,
239978]),
col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]),
values=tensor([151., 17., 6., ..., 1., 1., 1.]),
size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.8169, 0.9455, 0.2378, ..., 0.7183, 0.8285, 0.9774])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
Size: 615933124
NNZ: 239978
Density: 0.00038961697406616504
Time: 1.0039293766021729 seconds

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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "tn2010", "MATRIX_SHAPE": [240116, 240116], "MATRIX_SIZE": 57655693456, "MATRIX_NNZ": 1193966, "MATRIX_DENSITY": 2.070855328296721e-05, "TIME_S": 2.2318568229675293, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.52, 20.52, 20.68, 20.6, 20.76, 20.84, 20.52, 20.44, 20.48, 20.4], "POWER": [47.04, 68.12, 70.92, 71.88, 71.88, 61.28], "JOULES": 157.9681861114502, "POWER_AFTER": [21.04, 20.76, 20.8, 20.72, 20.76, 20.84, 20.92, 21.04, 20.8, 20.8]}

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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 3470986 queued and waiting for resources
srun: job 3470986 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,
1193963, 1193966]),
col_indices=tensor([ 1152, 1272, 1961, ..., 238254, 239142,
240113]),
values=tensor([ 5728., 2871., 418449., ..., 10058., 33324.,
34928.]), size=(240116, 240116), nnz=1193966,
layout=torch.sparse_csr)
tensor([0.2593, 0.6684, 0.1857, ..., 0.6282, 0.3314, 0.7454])
Matrix: tn2010
Shape: torch.Size([240116, 240116])
Size: 57655693456
NNZ: 1193966
Density: 2.070855328296721e-05
Time: 2.2318568229675293 seconds

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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "ut2010", "MATRIX_SHAPE": [115406, 115406], "MATRIX_SIZE": 13318544836, "MATRIX_NNZ": 572066, "MATRIX_DENSITY": 4.295259032005559e-05, "TIME_S": 1.5120632648468018, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [23.36, 22.84, 22.36, 21.92, 21.48, 21.48, 21.72, 22.08, 22.64, 23.28], "POWER": [43.48, 59.4, 65.28, 65.16, 62.16], "JOULES": 96.98985254287719, "POWER_AFTER": [22.56, 22.8, 22.24, 21.84, 21.4, 21.32, 20.96, 21.28, 21.36, 21.08]}

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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 3471001 queued and waiting for resources
srun: job 3471001 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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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
114602]),
values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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layout=torch.sparse_csr)
tensor([0.9240, 0.3751, 0.9849, ..., 0.9377, 0.9441, 0.6765])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
Size: 13318544836
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.5120632648468018 seconds

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{"CPU": "ALTRA", "ITERATIONS": 10000, "MATRIX_FILE": "va2010", "MATRIX_SHAPE": [285762, 285762], "MATRIX_SIZE": 81659920644, "MATRIX_NNZ": 1402128, "MATRIX_DENSITY": 1.717033263003816e-05, "TIME_S": 2.1484014987945557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [20.76, 20.72, 20.76, 20.88, 20.88, 20.96, 20.96, 20.96, 20.8, 20.6], "POWER": [65.16, 84.16, 87.88, 82.08, 64.16, 59.44], "JOULES": 155.0609850883484, "POWER_AFTER": [20.52, 20.52, 20.72, 20.56, 20.64, 20.64, 20.72, 20.92, 21.16, 21.32]}

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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 3471004 queued and waiting for resources
srun: job 3471004 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, 8, ..., 1402119,
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col_indices=tensor([ 2006, 2464, 1166, ..., 285581, 285634,
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values=tensor([125334., 3558., 1192., ..., 10148., 1763.,
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layout=torch.sparse_csr)
tensor([0.5972, 0.8492, 0.1772, ..., 0.7912, 0.0415, 0.8296])
Matrix: va2010
Shape: torch.Size([285762, 285762])
Size: 81659920644
NNZ: 1402128
Density: 1.717033263003816e-05
Time: 2.1484014987945557 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 3471005 queued and waiting for resources
srun: job 3471005 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, 7, ..., 155588, 155592,
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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([0.7980, 0.7955, 0.8301, ..., 0.2464, 0.9642, 0.0961])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
Size: 1061456400
NNZ: 155598
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Time: 0.8885588645935059 seconds

1
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470899 queued and waiting for resources
srun: job 3470899 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, 3, 4, ..., 3871767,
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col_indices=tensor([ 0, 11698, 11699, ..., 169456, 645874,
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tensor([0.6720, 0.5431, 0.4163, ..., 0.4625, 0.4662, 0.2085])
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Shape: torch.Size([682862, 682862])
Size: 466300511044
NNZ: 3871773
Density: 8.303171256088674e-06
Time: 7.5851967334747314 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470898 queued and waiting for resources
srun: job 3470898 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, 583, 584, ..., 65459, 65460, 65460]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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tensor([0.6755, 0.7426, 0.3350, ..., 0.5898, 0.3954, 0.3897])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
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Time: 0.4882948398590088 seconds

1
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470879 queued and waiting for resources
srun: job 3470879 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, 0, 0, ..., 106761, 106761,
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tensor([0.6412, 0.3070, 0.9642, ..., 0.0959, 0.1216, 0.7825])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
Size: 984641641
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Time: 0.6748511791229248 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470885 queued and waiting for resources
srun: job 3470885 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, 1, 2, ..., 766390, 766394,
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col_indices=tensor([ 0, 1, 2, ..., 116833, 89,
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size=(116835, 116835), nnz=766396, layout=torch.sparse_csr)
tensor([0.9489, 0.1111, 0.7586, ..., 0.1064, 0.9062, 0.5747])
Matrix: dc2
Shape: torch.Size([116835, 116835])
Size: 13650417225
NNZ: 766396
Density: 5.614451099680581e-05
Time: 2.0699713230133057 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470877 queued and waiting for resources
srun: job 3470877 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, 13, 21, ..., 116047, 116051,
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tensor([0.4656, 0.5143, 0.3514, ..., 0.7050, 0.9241, 0.6135])
Matrix: de2010
Shape: torch.Size([24115, 24115])
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NNZ: 116056
Density: 0.0001995689928120616
Time: 0.5970535278320312 seconds

1
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470880 queued and waiting for resources
srun: job 3470880 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, 1, 71, ..., 367660, 367661,
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tensor([0.0951, 0.9410, 0.9223, ..., 0.8038, 0.1166, 0.7786])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
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Density: 0.0002730901120626302
Time: 1.2558205127716064 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470889 queued and waiting for resources
srun: job 3470889 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, 2, 5, ..., 2346288,
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tensor([0.2616, 0.5904, 0.5539, ..., 0.1315, 0.7299, 0.8588])
Matrix: fl2010
Shape: torch.Size([484481, 484481])
Size: 234721839361
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Time: 3.8482837677001953 seconds

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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470878 queued and waiting for resources
srun: job 3470878 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, 3, 10, ..., 1418047,
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tensor([0.2127, 0.1840, 0.5883, ..., 0.9651, 0.0622, 0.2931])
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Shape: torch.Size([291086, 291086])
Size: 84731059396
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srun: job 3470890 queued and waiting for resources
srun: job 3470890 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, 3, 8, ..., 1273376,
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tensor([0.2225, 0.9184, 0.8891, ..., 0.8781, 0.9920, 0.8523])
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Shape: torch.Size([206500, 206500])
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NNZ: 1273389
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srun: job 3470883 queued and waiting for resources
srun: job 3470883 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, 2, 5, ..., 2100220,
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tensor([0.1112, 0.0723, 0.0629, ..., 0.0188, 0.2120, 0.5563])
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Shape: torch.Size([525825, 525825])
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Time: 1.4909443855285645 seconds

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srun: job 3470891 queued and waiting for resources
srun: job 3470891 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, 10, 20, ..., 39994, 39994, 39994]),
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tensor([0.7836, 0.6102, 0.9911, ..., 0.3070, 0.4164, 0.1677])
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Shape: torch.Size([10879, 10879])
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Time: 0.3917062282562256 seconds

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srun: job 3470896 queued and waiting for resources
srun: job 3470896 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, 9, 9, ..., 65369, 65369, 65369]),
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tensor([0.6457, 0.2082, 0.9929, ..., 0.5035, 0.5783, 0.4428])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
Size: 703204324
NNZ: 65369
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Time: 0.5904901027679443 seconds

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srun: job 3470893 queued and waiting for resources
srun: job 3470893 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, 9, 9, ..., 54704, 54704, 54705]),
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tensor([0.1041, 0.8814, 0.8390, ..., 0.9768, 0.6919, 0.6912])
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Shape: torch.Size([22687, 22687])
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srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470892 queued and waiting for resources
srun: job 3470892 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, 10, 10, ..., 88328, 88328, 88328]),
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tensor([0.8398, 0.4628, 0.1152, ..., 0.1515, 0.0358, 0.8190])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
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NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.5678565502166748 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_ri2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "ri2010", "MATRIX_SHAPE": [25181, 25181], "MATRIX_SIZE": 634082761, "MATRIX_NNZ": 125750, "MATRIX_DENSITY": 0.00019831796057928155, "TIME_S": 0.610253095626831, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [39.97, 38.69, 38.83, 38.7, 38.39, 38.32, 38.45, 38.47, 38.36, 38.28], "POWER": [80.37], "JOULES": 49.04604129552841, "POWER_AFTER": [40.01, 38.51, 38.35, 38.59, 39.52, 38.74, 38.53, 38.37, 38.81, 39.15]}

18
pytorch/output_cpu/epyc_7313p_10_10_ri2010_10000.output Normal file
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@ -0,0 +1,18 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470884 queued and waiting for resources
srun: job 3470884 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, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.8353, 0.9273, 0.0726, ..., 0.3513, 0.9132, 0.6466])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
Size: 634082761
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.610253095626831 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_rma10_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "rma10", "MATRIX_SHAPE": [46835, 46835], "MATRIX_SIZE": 2193517225, "MATRIX_NNZ": 2374001, "MATRIX_DENSITY": 0.0010822805369125833, "TIME_S": 3.9620909690856934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [40.62, 38.96, 38.47, 39.5, 38.54, 39.4, 38.42, 39.25, 39.24, 38.47], "POWER": [112.26], "JOULES": 444.78433218955996, "POWER_AFTER": [41.23, 39.08, 39.0, 39.24, 38.82, 39.26, 38.75, 40.54, 45.02, 39.59]}

19
pytorch/output_cpu/epyc_7313p_10_10_rma10_10000.output Normal file
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@ -0,0 +1,19 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470888 queued and waiting for resources
srun: job 3470888 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, 17, 34, ..., 2373939,
2373970, 2374001]),
col_indices=tensor([ 0, 1, 2, ..., 46831, 46833, 46834]),
values=tensor([ 1.2636e+05, -1.6615e+07, -8.2015e+04, ...,
8.3378e+01, 2.5138e+00, 1.2184e+03]),
size=(46835, 46835), nnz=2374001, layout=torch.sparse_csr)
tensor([0.0694, 0.3886, 0.4209, ..., 0.6373, 0.6766, 0.6929])
Matrix: rma10
Shape: torch.Size([46835, 46835])
Size: 2193517225
NNZ: 2374001
Density: 0.0010822805369125833
Time: 3.9620909690856934 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-Slashdot090216_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "soc-sign-Slashdot090216", "MATRIX_SHAPE": [81871, 81871], "MATRIX_SIZE": 6702860641, "MATRIX_NNZ": 545671, "MATRIX_DENSITY": 8.140867447881048e-05, "TIME_S": 1.620380163192749, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [41.09, 39.77, 38.98, 38.92, 39.2, 38.85, 38.8, 38.42, 38.67, 38.84], "POWER": [95.89], "JOULES": 155.3782538485527, "POWER_AFTER": [41.04, 38.44, 38.51, 39.43, 38.9, 38.66, 38.49, 38.69, 40.35, 38.43]}

18
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-Slashdot090216_10000.output Normal file
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@ -0,0 +1,18 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470897 queued and waiting for resources
srun: job 3470897 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, 29, 124, ..., 545669, 545669,
545671]),
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([0.6601, 0.6699, 0.4597, ..., 0.4006, 0.0724, 0.2095])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
Size: 6702860641
NNZ: 545671
Density: 8.140867447881048e-05
Time: 1.620380163192749 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-Slashdot090221_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "soc-sign-Slashdot090221", "MATRIX_SHAPE": [82144, 82144], "MATRIX_SIZE": 6747636736, "MATRIX_NNZ": 549202, "MATRIX_DENSITY": 8.13917555860553e-05, "TIME_S": 1.6988587379455566, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [39.74, 38.23, 38.66, 38.34, 38.69, 38.27, 38.61, 38.22, 38.36, 38.7], "POWER": [95.19], "JOULES": 161.71436326503752, "POWER_AFTER": [39.47, 38.44, 38.74, 38.72, 38.97, 38.52, 38.32, 38.61, 38.31, 38.32]}

18
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-Slashdot090221_10000.output Normal file
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@ -0,0 +1,18 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470876 queued and waiting for resources
srun: job 3470876 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, 29, 124, ..., 549200, 549200,
549202]),
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([0.5845, 0.4829, 0.3749, ..., 0.6026, 0.8058, 0.2362])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
Size: 6747636736
NNZ: 549202
Density: 8.13917555860553e-05
Time: 1.6988587379455566 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-epinions_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "soc-sign-epinions", "MATRIX_SHAPE": [131828, 131828], "MATRIX_SIZE": 17378621584, "MATRIX_NNZ": 841372, "MATRIX_DENSITY": 4.841419648464106e-05, "TIME_S": 2.818403720855713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [40.15, 38.33, 39.01, 38.43, 38.82, 38.58, 39.15, 39.79, 38.39, 38.52], "POWER": [101.75], "JOULES": 286.7725785970688, "POWER_AFTER": [40.36, 38.72, 38.87, 38.41, 38.45, 38.49, 39.37, 38.38, 38.82, 38.92]}

19
pytorch/output_cpu/epyc_7313p_10_10_soc-sign-epinions_10000.output Normal file
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@ -0,0 +1,19 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470886 queued and waiting for resources
srun: job 3470886 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, 1, 2, ..., 841371, 841371,
841372]),
col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
7714]),
values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.2911, 0.5946, 0.7956, ..., 0.3632, 0.5862, 0.9286])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
Size: 17378621584
NNZ: 841372
Density: 4.841419648464106e-05
Time: 2.818403720855713 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_sx-mathoverflow_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "sx-mathoverflow", "MATRIX_SHAPE": [24818, 24818], "MATRIX_SIZE": 615933124, "MATRIX_NNZ": 239978, "MATRIX_DENSITY": 0.00038961697406616504, "TIME_S": 0.9492478370666504, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [39.95, 38.76, 38.89, 38.34, 38.41, 38.56, 38.77, 38.39, 43.82, 38.34], "POWER": [85.3], "JOULES": 80.97084050178528, "POWER_AFTER": [40.98, 38.86, 38.38, 38.62, 38.93, 38.52, 39.17, 38.59, 38.99, 38.46]}

18
pytorch/output_cpu/epyc_7313p_10_10_sx-mathoverflow_10000.output Normal file
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@ -0,0 +1,18 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470882 queued and waiting for resources
srun: job 3470882 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, 317, 416, ..., 239976, 239977,
239978]),
col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]),
values=tensor([151., 17., 6., ..., 1., 1., 1.]),
size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.5148, 0.6679, 0.4736, ..., 0.5604, 0.1954, 0.1745])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
Size: 615933124
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.9492478370666504 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_tn2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "tn2010", "MATRIX_SHAPE": [240116, 240116], "MATRIX_SIZE": 57655693456, "MATRIX_NNZ": 1193966, "MATRIX_DENSITY": 2.070855328296721e-05, "TIME_S": 1.7836709022521973, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [40.32, 39.68, 39.86, 39.48, 38.52, 38.29, 40.37, 38.34, 39.45, 38.27], "POWER": [106.23], "JOULES": 189.47935994625092, "POWER_AFTER": [41.93, 39.25, 38.74, 39.37, 39.14, 39.52, 38.5, 38.58, 38.4, 39.3]}

20
pytorch/output_cpu/epyc_7313p_10_10_tn2010_10000.output Normal file
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@ -0,0 +1,20 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470881 queued and waiting for resources
srun: job 3470881 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, 20, ..., 1193961,
1193963, 1193966]),
col_indices=tensor([ 1152, 1272, 1961, ..., 238254, 239142,
240113]),
values=tensor([ 5728., 2871., 418449., ..., 10058., 33324.,
34928.]), size=(240116, 240116), nnz=1193966,
layout=torch.sparse_csr)
tensor([0.0655, 0.4633, 0.1355, ..., 0.7193, 0.0926, 0.7299])
Matrix: tn2010
Shape: torch.Size([240116, 240116])
Size: 57655693456
NNZ: 1193966
Density: 2.070855328296721e-05
Time: 1.7836709022521973 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_ut2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "ut2010", "MATRIX_SHAPE": [115406, 115406], "MATRIX_SIZE": 13318544836, "MATRIX_NNZ": 572066, "MATRIX_DENSITY": 4.295259032005559e-05, "TIME_S": 0.7757325172424316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [39.73, 39.13, 38.38, 39.43, 38.36, 39.7, 43.3, 38.81, 38.54, 39.24], "POWER": [90.41], "JOULES": 70.13397688388824, "POWER_AFTER": [40.58, 38.4, 39.23, 38.86, 38.48, 38.28, 39.25, 38.5, 40.62, 44.86]}

20
pytorch/output_cpu/epyc_7313p_10_10_ut2010_10000.output Normal file
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@ -0,0 +1,20 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470887 queued and waiting for resources
srun: job 3470887 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, 3, 9, ..., 572056, 572061,
572066]),
col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
114602]),
values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
18651.]), size=(115406, 115406), nnz=572066,
layout=torch.sparse_csr)
tensor([0.5921, 0.3895, 0.8812, ..., 0.2001, 0.1496, 0.7049])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
Size: 13318544836
NNZ: 572066
Density: 4.295259032005559e-05
Time: 0.7757325172424316 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_va2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "va2010", "MATRIX_SHAPE": [285762, 285762], "MATRIX_SIZE": 81659920644, "MATRIX_NNZ": 1402128, "MATRIX_DENSITY": 1.717033263003816e-05, "TIME_S": 2.389526844024658, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [42.21, 38.46, 39.09, 39.12, 38.6, 38.8, 38.74, 38.74, 38.51, 38.8], "POWER": [112.07], "JOULES": 267.7942734098434, "POWER_AFTER": [41.44, 38.92, 38.62, 39.01, 38.95, 38.72, 39.78, 38.59, 38.49, 38.56]}

20
pytorch/output_cpu/epyc_7313p_10_10_va2010_10000.output Normal file
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@ -0,0 +1,20 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470895 queued and waiting for resources
srun: job 3470895 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, 2, 8, ..., 1402119,
1402123, 1402128]),
col_indices=tensor([ 2006, 2464, 1166, ..., 285581, 285634,
285760]),
values=tensor([125334., 3558., 1192., ..., 10148., 1763.,
9832.]), size=(285762, 285762), nnz=1402128,
layout=torch.sparse_csr)
tensor([0.7826, 0.8027, 0.4606, ..., 0.4410, 0.5591, 0.5693])
Matrix: va2010
Shape: torch.Size([285762, 285762])
Size: 81659920644
NNZ: 1402128
Density: 1.717033263003816e-05
Time: 2.389526844024658 seconds

1
pytorch/output_cpu/epyc_7313p_10_10_vt2010_10000.json Normal file
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@ -0,0 +1 @@
{"CPU": "EPYC_7313P", "ITERATIONS": 10000, "MATRIX_FILE": "vt2010", "MATRIX_SHAPE": [32580, 32580], "MATRIX_SIZE": 1061456400, "MATRIX_NNZ": 155598, "MATRIX_DENSITY": 0.00014658915806621921, "TIME_S": 0.8104038238525391, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "POWER_BEFORE": [41.86, 43.27, 40.2, 39.3, 38.73, 39.46, 39.11, 39.26, 38.8, 38.85], "POWER": [81.98], "JOULES": 66.43690547943116, "POWER_AFTER": [41.6, 38.57, 39.45, 38.33, 39.52, 44.29, 38.68, 38.83, 39.46, 38.86]}

18
pytorch/output_cpu/epyc_7313p_10_10_vt2010_10000.output Normal file
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@ -0,0 +1,18 @@
srun: Job time limit was unset; set to partition default of 60 minutes
srun: job 3470894 queued and waiting for resources
srun: job 3470894 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([0.7387, 0.6923, 0.8988, ..., 0.9956, 0.0628, 0.8750])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
Size: 1061456400
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.8104038238525391 seconds

29
pytorch/power.py Executable file → Normal file
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@ -9,6 +9,7 @@
import argparse, subprocess import argparse, subprocess
import sys import sys
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('program', nargs='*')
parser.add_argument('-s', '--seconds', type=int) parser.add_argument('-s', '--seconds', type=int)
args = parser.parse_args() args = parser.parse_args()
@ -19,11 +20,35 @@ if arch == "aarch64":
i = 0 i = 0
while i != upper_bound: while i != upper_bound:
proc = subprocess.Popen(['sensors'], stdout=subprocess.PIPE) proc = subprocess.Popen(['sensors'], stdout=subprocess.PIPE)
proc = subprocess.Popen(['awk', '/CPU power:/ {print $3}'], stdin=proc.stdout, stdout=subprocess.PIPE, text=True) proc = subprocess.Popen(
['awk', '/CPU power:/ {print $3; exit}'],
stdin=proc.stdout,
stdout=subprocess.PIPE,
text=True)
power = proc.communicate()[0].strip().split('\n')[0] power = proc.communicate()[0].strip().split('\n')[0]
print(power) print(power)
i += 1 i += 1
time.sleep(1) time.sleep(1)
elif arch == "x86_64": elif arch == "x86_64":
pass if args.seconds is None:
proc = subprocess.Popen(
['turbostat', '-s', 'PkgWatt'] + args.program,
stdout=subprocess.DEVNULL,
stderr=subprocess.PIPE)
proc = subprocess.Popen(
['sed', '-n', '/PkgWatt/{n;p}'],
stdin=proc.stderr,
stdout=subprocess.PIPE,
text=True)
else:
proc = subprocess.Popen(
['turbostat', '-s', 'PkgWatt', '-n', str(args.seconds), '-i', '1'],
stdout=subprocess.PIPE)
proc = subprocess.Popen(
['sed', '-n', '/PkgWatt/{n;p}'],
stdin=proc.stdout,
stdout=subprocess.PIPE,
text=True)
power = proc.communicate()[0].strip().split('\n')
print('\n'.join(power))

23
pytorch/power.sh.bak
View File

@ -6,23 +6,26 @@ arch=$(uname -m)
if [[ $arch = aarch64 ]]; then if [[ $arch = aarch64 ]]; then
iter=0 iter=0
function aarch64_power { function aarch64_power {
sensors | awk '/CPU power:/ {printf "Socket"++count[$1] " "; print $3}' sensors | awk '/CPU power:/ {print $3; exit}'
((iter++)) ((iter++))
sleep 1s sleep 1s
} }
if [[ -z "$baseline_time_s" ]]; then if [[ -z "$baseline_time_s" ]]; then
while true; do baseline_time_s=-1
aarch64_power
done
else
while [[ "$iter" -ne "$baseline_time_s" ]]; do
aarch64_power
done
fi fi
while [[ "$iter" -ne "$baseline_time_s" ]]; do
aarch64_power
done
elif [[ $arch = x86_64 ]]; then elif [[ $arch = x86_64 ]]; then
if [[ -z "$baseline_time_s" ]]; then if [[ -z "$baseline_time_s" ]]; then
turbostat -s PkgWatt -i 1 2>/dev/null #turbostat -s PkgWatt -i 1 2>/dev/null | awk -F: '/PkgWatt/ {getline; print $0}'
#turbostat -s PkgWatt -i 1 | awk '/PkgWatt/ {getline; print $0}'
turbostat -s PkgWatt -i 1 | sed -n "/PkgWatt/{n;p}"
else else
turbostat -s PkgWatt -n "$baseline_time_s" -i 1 2>/dev/null #turbostat -s PkgWatt -n "$baseline_time_s" -i 1 2>/dev/null | awk -F: '/PkgWatt/ {getline; print $0}'
turbostat -s PkgWatt -n "$baseline_time_s" -i 1 | sed -n "/PkgWatt/{n;p}"
fi fi
else
echo "Unrecognized arch!"
exit 1
fi fi

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