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cephi 2024-12-03 00:20:09 -05:00
parent c9ed7980b8
commit 01b1b0fc0c
33 changed files with 2445 additions and 55 deletions
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pytorch/output/altra_100_soc-sign-Slashdot090221_2_2.json
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pytorch/output/altra_2_2_Oregon-2_100.json Normal file
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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 3394148 queued and waiting for resources
srun: job 3394148 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3190, 0.2829, 0.6210, ..., 0.9278, 0.7514, 0.5737])
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.22389841079711914 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
42.01 msec task-clock:u # 0.012 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,263 page-faults:u # 77.672 K/sec
47,084,933 cycles:u # 1.121 GHz (65.90%)
77,895,119 instructions:u # 1.65 insn per cycle (85.49%)
<not supported> branches:u
352,740 branch-misses:u
30,958,922 L1-dcache-loads:u # 736.946 M/sec
442,351 L1-dcache-load-misses:u # 1.43% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
29,506,648 L1-icache-loads:u # 702.376 M/sec
272,063 L1-icache-load-misses:u # 0.92% of all L1-icache accesses
51,646,382 dTLB-loads:u # 1.229 G/sec (15.87%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.513156571 seconds time elapsed
15.150380000 seconds user
32.922923000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.0741, 0.5476, 0.1060, ..., 0.8459, 0.8270, 0.8313])
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.20610284805297852 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
330,923 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,740,519 BR_RETIRED:u
3.639725976 seconds time elapsed
15.493122000 seconds user
27.617441000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9699, 0.9368, 0.7284, ..., 0.7182, 0.5308, 0.9833])
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.15960955619812012 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
27,761,239 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,471 ITLB_WALK:u
17,268 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,993,265 L1D_TLB:u
3.455602215 seconds time elapsed
15.015027000 seconds user
27.930709000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.5851, 0.3425, 0.8120, ..., 0.0829, 0.5823, 0.2256])
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.15697884559631348 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
31,834,980 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
298,333 L1I_CACHE_REFILL:u
466,901 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,528,976 L1D_CACHE:u
3.452279902 seconds time elapsed
14.635240000 seconds user
28.262858000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.0772, 0.9112, 0.0293, ..., 0.4016, 0.4357, 0.5368])
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 0.20962285995483398 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
525,505 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
546,521 LL_CACHE_RD:u
184,884 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
22,933 L2D_TLB_REFILL:u
292,367 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,706,226 L2D_CACHE:u
3.566096255 seconds time elapsed
15.763579000 seconds user
28.620423000 seconds sys

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pytorch/output/altra_2_2_as-caida_100.json Normal file
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pytorch/output/altra_2_2_as-caida_100.output Normal file
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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 3394150 queued and waiting for resources
srun: job 3394150 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.7672, 0.5818, 0.6775, ..., 0.1052, 0.2539, 0.4347])
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 0.28373050689697266 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 100':
60.78 msec task-clock:u # 0.017 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,300 page-faults:u # 54.293 K/sec
66,733,059 cycles:u # 1.098 GHz (58.34%)
87,889,334 instructions:u # 1.32 insn per cycle (93.45%)
<not supported> branches:u
369,909 branch-misses:u
31,872,708 L1-dcache-loads:u # 524.386 M/sec
465,719 L1-dcache-load-misses:u # 1.46% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
30,443,353 L1-icache-loads:u # 500.870 M/sec
292,371 L1-icache-load-misses:u # 0.96% of all L1-icache accesses
34,702,735 dTLB-loads:u # 570.947 M/sec (6.96%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.683429807 seconds time elapsed
15.161162000 seconds user
31.335288000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2708, 0.2455, 0.7615, ..., 0.1172, 0.4072, 0.8970])
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 0.32511067390441895 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 100':
326,300 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,832,700 BR_RETIRED:u
3.755497210 seconds time elapsed
14.681699000 seconds user
29.413955000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9417, 0.0965, 0.8551, ..., 0.6665, 0.0164, 0.5102])
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 0.33124780654907227 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 100':
27,233,629 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
5,868 ITLB_WALK:u
16,893 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,409,508 L1D_TLB:u
3.751203540 seconds time elapsed
14.849342000 seconds user
27.706396000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9215, 0.4139, 0.1789, ..., 0.0245, 0.0029, 0.2129])
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 0.3386805057525635 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 100':
30,924,532 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
288,199 L1I_CACHE_REFILL:u
462,816 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
32,428,375 L1D_CACHE:u
3.628443937 seconds time elapsed
15.430937000 seconds user
30.878583000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.4983, 0.0268, 0.1695, ..., 0.6987, 0.7224, 0.8577])
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 0.3289623260498047 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 100':
551,997 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
568,528 LL_CACHE_RD:u
193,991 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
24,353 L2D_TLB_REFILL:u
312,207 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,821,196 L2D_CACHE:u
3.698790384 seconds time elapsed
15.745189000 seconds user
31.063512000 seconds sys

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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 3394149 queued and waiting for resources
srun: job 3394149 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.4749, 0.3788, 0.8812, ..., 0.8281, 0.8889, 0.4945])
Shape: torch.Size([116835, 116835])
NNZ: 766396
Density: 5.614451099680581e-05
Time: 2.2480316162109375 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 100':
50.43 msec task-clock:u # 0.009 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,285 page-faults:u # 65.135 K/sec
54,118,679 cycles:u # 1.073 GHz (60.92%)
77,692,421 instructions:u # 1.44 insn per cycle (82.73%)
<not supported> branches:u
367,999 branch-misses:u
32,182,371 L1-dcache-loads:u # 638.112 M/sec
491,960 L1-dcache-load-misses:u # 1.53% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
30,682,258 L1-icache-loads:u # 608.367 M/sec
300,874 L1-icache-load-misses:u # 0.98% of all L1-icache accesses
55,244,523 dTLB-loads:u # 1.095 G/sec (19.09%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
5.813837947 seconds time elapsed
28.815118000 seconds user
213.749674000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9715, 0.3920, 0.0297, ..., 0.1819, 0.5744, 0.8105])
Shape: torch.Size([116835, 116835])
NNZ: 766396
Density: 5.614451099680581e-05
Time: 2.2333595752716064 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 100':
325,039 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,383,216 BR_RETIRED:u
5.973132269 seconds time elapsed
29.719778000 seconds user
213.706315000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3371, 0.4985, 0.9905, ..., 0.6075, 0.1568, 0.3782])
Shape: torch.Size([116835, 116835])
NNZ: 766396
Density: 5.614451099680581e-05
Time: 1.9790923595428467 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 100':
26,060,519 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
4,749 ITLB_WALK:u
16,865 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
34,819,729 L1D_TLB:u
5.575020445 seconds time elapsed
26.769391000 seconds user
188.138935000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6806, 0.8858, 0.7035, ..., 0.6007, 0.0880, 0.4550])
Shape: torch.Size([116835, 116835])
NNZ: 766396
Density: 5.614451099680581e-05
Time: 1.5306556224822998 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 100':
30,777,115 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
293,980 L1I_CACHE_REFILL:u
461,522 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
32,216,597 L1D_CACHE:u
4.961298684 seconds time elapsed
23.946357000 seconds user
156.598674000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3029, 0.1908, 0.9816, ..., 0.0418, 0.8182, 0.5474])
Shape: torch.Size([116835, 116835])
NNZ: 766396
Density: 5.614451099680581e-05
Time: 2.28926944732666 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 100':
567,700 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
588,689 LL_CACHE_RD:u
189,417 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
22,360 L2D_TLB_REFILL:u
328,306 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,908,607 L2D_CACHE:u
5.710829283 seconds time elapsed
28.671301000 seconds user
213.960421000 seconds sys

1
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@ -0,0 +1 @@
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163
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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 3394139 queued and waiting for resources
srun: job 3394139 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.4207, 0.3943, 0.6543, ..., 0.2191, 0.5415, 0.1575])
Shape: torch.Size([24115, 24115])
NNZ: 116056
Density: 0.0001995689928120616
Time: 0.36042284965515137 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
59.88 msec task-clock:u # 0.016 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,313 page-faults:u # 55.328 K/sec
58,169,777 cycles:u # 0.971 GHz (61.49%)
57,993,431 instructions:u # 1.00 insn per cycle (81.67%)
<not supported> branches:u
341,266 branch-misses:u
31,858,781 L1-dcache-loads:u # 532.049 M/sec
467,486 L1-dcache-load-misses:u # 1.47% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
30,461,310 L1-icache-loads:u # 508.711 M/sec
294,156 L1-icache-load-misses:u # 0.97% of all L1-icache accesses
43,828,130 dTLB-loads:u # 731.940 M/sec (40.26%)
47,836 dTLB-load-misses:u # 0.11% of all dTLB cache accesses (25.52%)
0 iTLB-loads:u # 0.000 /sec (2.73%)
<not counted> iTLB-load-misses:u (0.00%)
3.824054028 seconds time elapsed
15.099361000 seconds user
28.830417000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.0456, 0.2095, 0.0276, ..., 0.4209, 0.6824, 0.5475])
Shape: torch.Size([24115, 24115])
NNZ: 116056
Density: 0.0001995689928120616
Time: 0.3598823547363281 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
330,494 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,578,427 BR_RETIRED:u
3.781234836 seconds time elapsed
14.965545000 seconds user
29.444131000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9882, 0.5477, 0.6307, ..., 0.1179, 0.6903, 0.1235])
Shape: torch.Size([24115, 24115])
NNZ: 116056
Density: 0.0001995689928120616
Time: 0.29088521003723145 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
27,982,097 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,614 ITLB_WALK:u
17,270 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
37,728,899 L1D_TLB:u
3.576632300 seconds time elapsed
14.864601000 seconds user
29.274547000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3952, 0.0475, 0.1125, ..., 0.3481, 0.1290, 0.3495])
Shape: torch.Size([24115, 24115])
NNZ: 116056
Density: 0.0001995689928120616
Time: 0.30365920066833496 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
29,754,926 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
278,786 L1I_CACHE_REFILL:u
454,742 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
31,173,246 L1D_CACHE:u
3.730995381 seconds time elapsed
15.213930000 seconds user
30.995070000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.7266, 0.7537, 0.9729, ..., 0.3349, 0.3523, 0.6532])
Shape: torch.Size([24115, 24115])
NNZ: 116056
Density: 0.0001995689928120616
Time: 0.2798902988433838 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
543,243 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
560,716 LL_CACHE_RD:u
162,281 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
19,847 L2D_TLB_REFILL:u
300,577 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,696,278 L2D_CACHE:u
3.819959836 seconds time elapsed
15.346035000 seconds user
29.199873000 seconds sys

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 3394152 queued and waiting for resources
srun: job 3394152 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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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([0.3626, 0.7532, 0.0782, ..., 0.6679, 0.4308, 0.6586])
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.3745801448822021 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
60.43 msec task-clock:u # 0.012 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,319 page-faults:u # 54.926 K/sec
66,114,448 cycles:u # 1.094 GHz (58.10%)
90,786,829 instructions:u # 1.37 insn per cycle (92.25%)
<not supported> branches:u
372,381 branch-misses:u
32,997,410 L1-dcache-loads:u # 546.070 M/sec
470,216 L1-dcache-load-misses:u # 1.43% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,485,339 L1-icache-loads:u # 521.047 M/sec
294,395 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
31,376,646 dTLB-loads:u # 519.248 M/sec (10.03%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
4.904488673 seconds time elapsed
22.874521000 seconds user
139.276239000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2040, 0.8252, 0.0215, ..., 0.2921, 0.9143, 0.8728])
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.3087654113769531 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
341,625 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,129,354 BR_RETIRED:u
4.644873434 seconds time elapsed
22.729927000 seconds user
132.278582000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6154, 0.6641, 0.3794, ..., 0.9736, 0.0619, 0.4790])
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.2701547145843506 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
27,441,303 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,807 ITLB_WALK:u
20,551 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,867,114 L1D_TLB:u
4.861510767 seconds time elapsed
22.111354000 seconds user
132.431608000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.4201, 0.4134, 0.8169, ..., 0.6631, 0.0087, 0.8439])
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.1176586151123047 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
31,744,243 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
271,027 L1I_CACHE_REFILL:u
464,135 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,441,141 L1D_CACHE:u
4.693803969 seconds time elapsed
21.724904000 seconds user
119.873018000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.1285, 0.3989, 0.3903, ..., 0.7892, 0.2737, 0.2659])
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 1.196892261505127 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
539,935 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
552,519 LL_CACHE_RD:u
188,291 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
24,177 L2D_TLB_REFILL:u
301,281 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,737,575 L2D_CACHE:u
4.741030347 seconds time elapsed
23.793930000 seconds user
125.634838000 seconds sys

1
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@ -0,0 +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 3394153 queued and waiting for resources
srun: job 3394153 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6982, 0.7263, 0.0064, ..., 0.9256, 0.7249, 0.5065])
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.18009519577026367 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 100':
67.56 msec task-clock:u # 0.019 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,829 page-faults:u # 56.674 K/sec
47,862,000 cycles:u # 0.708 GHz (59.24%)
84,392,375 instructions:u # 1.76 insn per cycle (87.61%)
<not supported> branches:u
368,432 branch-misses:u
32,507,448 L1-dcache-loads:u # 481.147 M/sec
481,389 L1-dcache-load-misses:u # 1.48% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,030,656 L1-icache-loads:u # 459.289 M/sec
308,582 L1-icache-load-misses:u # 0.99% of all L1-icache accesses
34,988,046 dTLB-loads:u # 517.863 M/sec (20.00%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.538329547 seconds time elapsed
14.667604000 seconds user
29.534487000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.4946, 0.3509, 0.5239, ..., 0.4520, 0.4206, 0.8181])
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.18875432014465332 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 100':
331,622 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,800,140 BR_RETIRED:u
3.556031790 seconds time elapsed
14.799719000 seconds user
27.876987000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2184, 0.4999, 0.9567, ..., 0.8794, 0.8213, 0.8713])
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.1066896915435791 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 100':
25,905,045 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,746 ITLB_WALK:u
17,547 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
35,220,079 L1D_TLB:u
3.505367779 seconds time elapsed
14.557493000 seconds user
29.642958000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2180, 0.0881, 0.5532, ..., 0.4961, 0.0093, 0.4929])
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.12433028221130371 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 100':
30,359,576 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
283,204 L1I_CACHE_REFILL:u
465,520 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
31,843,274 L1D_CACHE:u
3.565310130 seconds time elapsed
14.913239000 seconds user
28.125605000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6394, 0.6808, 0.7957, ..., 0.1529, 0.0561, 0.7834])
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.13401126861572266 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 100':
560,542 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
575,610 LL_CACHE_RD:u
173,643 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
21,499 L2D_TLB_REFILL:u
313,335 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,741,621 L2D_CACHE:u
3.503362704 seconds time elapsed
15.287949000 seconds user
28.752303000 seconds sys

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 3394141 queued and waiting for resources
srun: job 3394141 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6616, 0.1149, 0.0110, ..., 0.2481, 0.7877, 0.5589])
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.16974925994873047 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 100':
61.92 msec task-clock:u # 0.017 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,281 page-faults:u # 52.988 K/sec
66,250,810 cycles:u # 1.070 GHz (62.94%)
75,178,179 instructions:u # 1.13 insn per cycle (83.47%)
<not supported> branches:u
367,749 branch-misses:u
33,064,095 L1-dcache-loads:u # 533.986 M/sec
465,542 L1-dcache-load-misses:u # 1.41% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,552,264 L1-icache-loads:u # 509.570 M/sec
296,060 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
73,155,896 dTLB-loads:u # 1.181 G/sec (17.31%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.675971385 seconds time elapsed
14.857293000 seconds user
29.791187000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.1683, 0.8999, 0.0578, ..., 0.5893, 0.0628, 0.8262])
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.2227163314819336 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 100':
332,366 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,076,182 BR_RETIRED:u
3.532329673 seconds time elapsed
14.883993000 seconds user
28.516661000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.8389, 0.5614, 0.9033, ..., 0.2231, 0.0349, 0.5167])
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.17095375061035156 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 100':
27,005,133 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
4,791 ITLB_WALK:u
13,403 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,457,054 L1D_TLB:u
3.579041343 seconds time elapsed
14.885159000 seconds user
29.562650000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.8849, 0.5982, 0.0578, ..., 0.9975, 0.2204, 0.0718])
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.18003463745117188 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 100':
32,367,686 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
287,524 L1I_CACHE_REFILL:u
467,557 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
34,022,862 L1D_CACHE:u
3.405321132 seconds time elapsed
15.291636000 seconds user
28.005015000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2790, 0.1291, 0.6053, ..., 0.1651, 0.4973, 0.6821])
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 0.22036528587341309 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 100':
535,707 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
556,316 LL_CACHE_RD:u
150,149 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
18,418 L2D_TLB_REFILL:u
297,042 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,687,364 L2D_CACHE:u
3.505209576 seconds time elapsed
15.297738000 seconds user
29.848441000 seconds sys

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 3394140 queued and waiting for resources
srun: job 3394140 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.8199, 0.9849, 0.4642, ..., 0.7594, 0.3568, 0.4020])
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 0.19272208213806152 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100':
64.71 msec task-clock:u # 0.018 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,319 page-faults:u # 51.288 K/sec
57,611,295 cycles:u # 0.890 GHz (39.00%)
83,148,228 instructions:u # 1.44 insn per cycle (82.73%)
<not supported> branches:u
375,111 branch-misses:u
32,759,228 L1-dcache-loads:u # 506.221 M/sec
475,086 L1-dcache-load-misses:u # 1.45% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,366,158 L1-icache-loads:u # 484.694 M/sec
297,293 L1-icache-load-misses:u # 0.95% of all L1-icache accesses
35,611,781 dTLB-loads:u # 550.301 M/sec (25.73%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.578384817 seconds time elapsed
14.435258000 seconds user
27.700836000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.0069, 0.9904, 0.5316, ..., 0.2082, 0.4858, 0.4936])
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 0.1423017978668213 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100':
318,386 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,233,431 BR_RETIRED:u
3.555753224 seconds time elapsed
14.642518000 seconds user
30.112207000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2250, 0.5676, 0.3018, ..., 0.5431, 0.7314, 0.5593])
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 0.14638042449951172 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100':
27,039,805 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,375 ITLB_WALK:u
17,290 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,688,544 L1D_TLB:u
3.566915241 seconds time elapsed
16.116565000 seconds user
28.752519000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.0220, 0.7494, 0.7913, ..., 0.8924, 0.8542, 0.5491])
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 0.17815685272216797 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100':
32,508,072 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
297,568 L1I_CACHE_REFILL:u
477,654 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
34,044,579 L1D_CACHE:u
3.435706033 seconds time elapsed
14.690285000 seconds user
28.763423000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.6277, 0.4955, 0.9335, ..., 0.1476, 0.2079, 0.0931])
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 0.14432048797607422 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100':
549,474 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
561,939 LL_CACHE_RD:u
185,622 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
23,295 L2D_TLB_REFILL:u
305,878 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,763,089 L2D_CACHE:u
3.538826979 seconds time elapsed
15.006109000 seconds user
29.644298000 seconds sys

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 3394142 queued and waiting for resources
srun: job 3394142 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.5867, 0.3729, 0.0718, ..., 0.5551, 0.6046, 0.6005])
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.3765556812286377 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 100':
65.91 msec task-clock:u # 0.017 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,247 page-faults:u # 49.267 K/sec
92,293,071 cycles:u # 1.400 GHz (58.72%)
76,208,632 instructions:u # 0.83 insn per cycle (75.47%)
<not supported> branches:u
336,620 branch-misses:u (89.96%)
33,256,017 L1-dcache-loads:u # 504.599 M/sec
479,188 L1-dcache-load-misses:u # 1.44% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,686,331 L1-icache-loads:u # 480.782 M/sec
297,521 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
55,295,804 dTLB-loads:u # 839.012 M/sec (27.47%)
103,616 dTLB-load-misses:u # 0.19% of all dTLB cache accesses (20.17%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.803094533 seconds time elapsed
16.585763000 seconds user
62.703127000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.2027, 0.2128, 0.5093, ..., 0.8069, 0.6413, 0.1136])
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.2942969799041748 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 100':
320,083 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,285,106 BR_RETIRED:u
3.763535833 seconds time elapsed
16.476022000 seconds user
55.208213000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.5930, 0.8044, 0.8115, ..., 0.6366, 0.1026, 0.6914])
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.2431955337524414 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 100':
26,853,940 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,728 ITLB_WALK:u
13,955 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
37,111,059 L1D_TLB:u
3.752433570 seconds time elapsed
16.433982000 seconds user
53.207908000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9666, 0.8206, 0.6252, ..., 0.5180, 0.8170, 0.7406])
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.15313339233398438 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 100':
32,554,796 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
298,729 L1I_CACHE_REFILL:u
473,779 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
34,117,102 L1D_CACHE:u
3.595579651 seconds time elapsed
15.817851000 seconds user
44.491315000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9800, 0.9021, 0.5677, ..., 0.3869, 0.2468, 0.3286])
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 0.2539215087890625 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 100':
535,040 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
547,502 LL_CACHE_RD:u
179,876 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
21,809 L2D_TLB_REFILL:u
298,620 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,722,959 L2D_CACHE:u
3.549060962 seconds time elapsed
16.570077000 seconds user
52.238012000 seconds sys

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 3394145 queued and waiting for resources
srun: job 3394145 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.1402, 0.0708, 0.4576, ..., 0.4700, 0.5629, 0.9120])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3585643768310547 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
60.77 msec task-clock:u # 0.016 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,361 page-faults:u # 55.311 K/sec
63,493,475 cycles:u # 1.045 GHz (49.59%)
91,578,911 instructions:u # 1.44 insn per cycle (92.22%)
<not supported> branches:u
374,941 branch-misses:u
33,905,978 L1-dcache-loads:u # 557.979 M/sec
470,553 L1-dcache-load-misses:u # 1.39% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
32,247,376 L1-icache-loads:u # 530.684 M/sec
299,037 L1-icache-load-misses:u # 0.93% of all L1-icache accesses
27,428,635 dTLB-loads:u # 451.384 M/sec (13.50%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.818532962 seconds time elapsed
15.563570000 seconds user
30.194882000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.1841, 0.4436, 0.8281, ..., 0.0546, 0.5967, 0.9496])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3050577640533447 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
329,084 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,406,595 BR_RETIRED:u
3.673527837 seconds time elapsed
15.520198000 seconds user
29.068211000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.1849, 0.5991, 0.5040, ..., 0.4916, 0.4789, 0.8887])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3605458736419678 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
26,859,919 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,237 ITLB_WALK:u
16,689 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,348,977 L1D_TLB:u
3.769690988 seconds time elapsed
15.173839000 seconds user
29.963392000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.0513, 0.4498, 0.6748, ..., 0.2114, 0.6847, 0.2188])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3485410213470459 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
30,979,764 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
292,038 L1I_CACHE_REFILL:u
469,219 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
32,411,890 L1D_CACHE:u
3.598754329 seconds time elapsed
16.139631000 seconds user
29.287026000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.7270, 0.7858, 0.3165, ..., 0.7139, 0.8270, 0.9478])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3687746524810791 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
571,870 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
598,306 LL_CACHE_RD:u
205,488 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
26,392 L2D_TLB_REFILL:u
342,141 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,857,697 L2D_CACHE:u
3.726794738 seconds time elapsed
15.231331000 seconds user
32.108693000 seconds sys

1
pytorch/output/altra_2_2_soc-sign-Slashdot090216_100.json Normal file
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@ -0,0 +1 @@
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158
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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 3394151 queued and waiting for resources
srun: job 3394151 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3831, 0.6714, 0.8380, ..., 0.7892, 0.5274, 0.9035])
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 2.044952392578125 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100':
59.01 msec task-clock:u # 0.010 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,448 page-faults:u # 58.432 K/sec
73,062,796 cycles:u # 1.238 GHz (59.95%)
88,329,175 instructions:u # 1.21 insn per cycle (93.89%)
<not supported> branches:u
365,177 branch-misses:u
31,850,867 L1-dcache-loads:u # 539.766 M/sec
473,835 L1-dcache-load-misses:u # 1.49% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
30,385,913 L1-icache-loads:u # 514.940 M/sec
299,969 L1-icache-load-misses:u # 0.99% of all L1-icache accesses
24,365,554 dTLB-loads:u # 412.915 M/sec (8.42%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
5.680365622 seconds time elapsed
27.656957000 seconds user
194.823873000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.6906, 0.4067, 0.7042, ..., 0.8333, 0.7120, 0.3519])
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 1.3788115978240967 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100':
331,091 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,013,316 BR_RETIRED:u
4.886021169 seconds time elapsed
23.105025000 seconds user
141.491451000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.8755, 0.6165, 0.4104, ..., 0.6974, 0.9453, 0.9872])
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 2.8570749759674072 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100':
26,330,936 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
5,193 ITLB_WALK:u
16,837 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
35,930,477 L1D_TLB:u
6.371573603 seconds time elapsed
30.986329000 seconds user
254.347216000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.3573, 0.9331, 0.0611, ..., 0.9133, 0.6057, 0.2374])
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 2.311248540878296 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100':
31,853,890 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
306,147 L1I_CACHE_REFILL:u
479,933 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,426,019 L1D_CACHE:u
5.718741260 seconds time elapsed
28.451593000 seconds user
214.350594000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.6021, 0.5679, 0.4538, ..., 0.9086, 0.9552, 0.5329])
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 1.8193013668060303 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100':
540,302 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
553,181 LL_CACHE_RD:u
173,206 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
21,390 L2D_TLB_REFILL:u
300,032 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,739,931 L2D_CACHE:u
5.546861941 seconds time elapsed
28.194596000 seconds user
181.004698000 seconds sys

1
pytorch/output/altra_2_2_soc-sign-Slashdot090221_100.json Normal file
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@ -0,0 +1 @@
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108
pytorch/output/altra_100_soc-sign-Slashdot090221_2_2.output → pytorch/output/altra_2_2_soc-sign-Slashdot090221_100.output
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@ -5,8 +5,8 @@ srun: # All submission nodes and all other compute nodes have x86_64 architectur
srun: # CPUs. Programs, environments, or other software that was built on x86_64 # 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: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################ srun: ################################################################################
srun: job 3393718 queued and waiting for resources srun: job 3394147 queued and waiting for resources
srun: job 3393718 has been allocated resources srun: job 3394147 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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.) /nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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) ).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200, tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
@ -14,37 +14,37 @@ tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr) nnz=549202, layout=torch.sparse_csr)
tensor([0.8320, 0.8961, 0.3119, ..., 0.2600, 0.3720, 0.6950]) tensor([0.2696, 0.6106, 0.1626, ..., 0.2215, 0.5107, 0.8609])
Shape: torch.Size([82144, 82144]) Shape: torch.Size([82144, 82144])
NNZ: 549202 NNZ: 549202
Density: 8.13917555860553e-05 Density: 8.13917555860553e-05
Time: 3.012270212173462 seconds Time: 1.4500706195831299 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
61.63 msec task-clock:u # 0.008 CPUs utilized 61.26 msec task-clock:u # 0.012 CPUs utilized
0 context-switches:u # 0.000 /sec 0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec
3,293 page-faults:u # 53.433 K/sec 3,303 page-faults:u # 53.917 K/sec
41,677,750 cycles:u # 0.676 GHz (43.47%) 44,515,786 cycles:u # 0.727 GHz (40.46%)
91,767,205 instructions:u # 2.20 insn per cycle (93.66%) 81,513,738 instructions:u # 1.83 insn per cycle (73.51%)
<not supported> branches:u <not supported> branches:u
369,577 branch-misses:u 344,479 branch-misses:u (89.42%)
33,184,885 L1-dcache-loads:u # 538.465 M/sec 34,411,073 L1-dcache-loads:u # 561.710 M/sec
489,650 L1-dcache-load-misses:u # 1.48% of all L1-dcache accesses 484,811 L1-dcache-load-misses:u # 1.41% of all L1-dcache accesses
<not supported> LLC-loads:u <not supported> LLC-loads:u
<not supported> LLC-load-misses:u <not supported> LLC-load-misses:u
31,518,657 L1-icache-loads:u # 511.428 M/sec 32,789,672 L1-icache-loads:u # 535.243 M/sec
300,352 L1-icache-load-misses:u # 0.95% of all L1-icache accesses 293,487 L1-icache-load-misses:u # 0.90% of all L1-icache accesses
21,439,232 dTLB-loads:u # 347.878 M/sec (11.35%) 47,065,740 dTLB-loads:u # 768.279 M/sec (32.81%)
<not counted> dTLB-load-misses:u (0.00%) 146,215 dTLB-load-misses:u # 0.31% of all dTLB cache accesses (13.39%)
<not counted> iTLB-loads:u (0.00%) <not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%) <not counted> iTLB-load-misses:u (0.00%)
7.285558270 seconds time elapsed 4.966101053 seconds time elapsed
30.820742000 seconds user 23.375418000 seconds user
271.093513000 seconds sys 148.052989000 seconds sys
@ -55,21 +55,21 @@ tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr) nnz=549202, layout=torch.sparse_csr)
tensor([0.2625, 0.3727, 0.7700, ..., 0.9213, 0.0373, 0.4236]) tensor([0.1999, 0.3932, 0.8035, ..., 0.5079, 0.5903, 0.7606])
Shape: torch.Size([82144, 82144]) Shape: torch.Size([82144, 82144])
NNZ: 549202 NNZ: 549202
Density: 8.13917555860553e-05 Density: 8.13917555860553e-05
Time: 3.8292958736419678 seconds Time: 1.9677543640136719 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
329,386 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 328,019 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,813,961 BR_RETIRED:u 19,893,662 BR_RETIRED:u
7.818393438 seconds time elapsed 5.529871590 seconds time elapsed
35.952830000 seconds user 26.844356000 seconds user
333.700971000 seconds sys 190.429440000 seconds sys
@ -80,23 +80,23 @@ tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr) nnz=549202, layout=torch.sparse_csr)
tensor([0.0340, 0.2650, 0.1324, ..., 0.0868, 0.2162, 0.5618]) tensor([0.2933, 0.6999, 0.0078, ..., 0.6213, 0.9377, 0.6359])
Shape: torch.Size([82144, 82144]) Shape: torch.Size([82144, 82144])
NNZ: 549202 NNZ: 549202
Density: 8.13917555860553e-05 Density: 8.13917555860553e-05
Time: 3.464143753051758 seconds Time: 1.4976201057434082 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
27,944,146 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 27,248,112 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,811 ITLB_WALK:u 5,792 ITLB_WALK:u
18,962 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 16,632 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
37,689,058 L1D_TLB:u 36,929,042 L1D_TLB:u
7.541903779 seconds time elapsed 4.971341163 seconds time elapsed
32.666428000 seconds user 24.247480000 seconds user
309.938101000 seconds sys 151.276717000 seconds sys
@ -107,23 +107,23 @@ tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr) nnz=549202, layout=torch.sparse_csr)
tensor([0.6118, 0.9275, 0.9072, ..., 0.7025, 0.2788, 0.7796]) tensor([0.1310, 0.6695, 0.9479, ..., 0.3141, 0.9327, 0.2117])
Shape: torch.Size([82144, 82144]) Shape: torch.Size([82144, 82144])
NNZ: 549202 NNZ: 549202
Density: 8.13917555860553e-05 Density: 8.13917555860553e-05
Time: 1.4259674549102783 seconds Time: 1.0877256393432617 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
31,746,573 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 31,702,830 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
290,044 L1I_CACHE_REFILL:u 295,778 L1I_CACHE_REFILL:u
471,100 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 470,423 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,271,575 L1D_CACHE:u 33,155,119 L1D_CACHE:u
5.333100815 seconds time elapsed 4.675682406 seconds time elapsed
24.606404000 seconds user 23.098007000 seconds user
142.184021000 seconds sys 119.827712000 seconds sys
@ -134,25 +134,25 @@ tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr) nnz=549202, layout=torch.sparse_csr)
tensor([0.1819, 0.6831, 0.7926, ..., 0.2272, 0.8215, 0.3765]) tensor([0.0860, 0.5402, 0.6738, ..., 0.3856, 0.5968, 0.4203])
Shape: torch.Size([82144, 82144]) Shape: torch.Size([82144, 82144])
NNZ: 549202 NNZ: 549202
Density: 8.13917555860553e-05 Density: 8.13917555860553e-05
Time: 2.8267815113067627 seconds Time: 1.2302696704864502 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
550,308 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 545,220 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
564,981 LL_CACHE_RD:u 562,139 LL_CACHE_RD:u
168,456 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 192,206 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
20,450 L2D_TLB_REFILL:u 24,891 L2D_TLB_REFILL:u
306,309 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 307,033 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,745,776 L2D_CACHE:u 1,782,260 L2D_CACHE:u
7.032343494 seconds time elapsed 4.781838296 seconds time elapsed
31.547129000 seconds user 23.716896000 seconds user
251.812633000 seconds sys 130.971947000 seconds sys

1
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@ -0,0 +1 @@
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163
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@ -0,0 +1,163 @@
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 3394154 queued and waiting for resources
srun: job 3394154 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.5842, 0.3042, 0.7358, ..., 0.7882, 0.7596, 0.5895])
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 2.4407293796539307 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 100':
49.87 msec task-clock:u # 0.008 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,300 page-faults:u # 66.174 K/sec
51,935,476 cycles:u # 1.041 GHz (65.00%)
83,731,856 instructions:u # 1.61 insn per cycle (84.25%)
<not supported> branches:u
375,900 branch-misses:u
34,169,837 L1-dcache-loads:u # 685.197 M/sec
474,410 L1-dcache-load-misses:u # 1.39% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
32,443,215 L1-icache-loads:u # 650.574 M/sec
294,146 L1-icache-load-misses:u # 0.91% of all L1-icache accesses
63,709,518 dTLB-loads:u # 1.278 G/sec (16.44%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
6.058862056 seconds time elapsed
29.101578000 seconds user
224.790489000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.9696, 0.8139, 0.4858, ..., 0.2374, 0.1716, 0.9756])
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 2.0945546627044678 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 100':
326,464 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,341,367 BR_RETIRED:u
5.525378890 seconds time elapsed
28.841740000 seconds user
199.678982000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.3478, 0.0057, 0.8574, ..., 0.6409, 0.1876, 0.8429])
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 2.8504912853240967 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 100':
27,590,154 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,210 ITLB_WALK:u
17,536 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,763,243 L1D_TLB:u
6.425887143 seconds time elapsed
33.069094000 seconds user
256.667850000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.5381, 0.6651, 0.4689, ..., 0.7251, 0.3759, 0.8516])
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 1.6941111087799072 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 100':
31,663,300 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
289,727 L1I_CACHE_REFILL:u
462,864 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,262,254 L1D_CACHE:u
5.304170809 seconds time elapsed
25.992245000 seconds user
173.752913000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.4145, 0.8515, 0.7222, ..., 0.1386, 0.6641, 0.6662])
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 3.0850296020507812 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 100':
530,272 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
551,373 LL_CACHE_RD:u
196,152 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
23,542 L2D_TLB_REFILL:u
301,998 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,732,662 L2D_CACHE:u
6.733517838 seconds time elapsed
34.030476000 seconds user
271.397968000 seconds sys

1
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@ -0,0 +1 @@
{"power_before": [20.44, 20.2], "shape": [24818, 24818], "nnz": 239978, "% density": 0.00038961697406616504, "time_s": 0.556269645690918, "power": [25.24, 32.16, 33.0, 32.52], "power_after": [34.24, 30.28], "task clock (msec)": 62.49, "page faults": 3312, "cycles": 76783170, "instructions": 77095702, "branch mispredictions": 323514, "branches": 19769937, "ITLB accesses": 26809325, "ITLB misses": 6925, "DTLB misses": 19003, "DTLB accesses": 36516965, "L1I cache accesses": 31104231, "L1I cache misses": 285499, "L1D cache misses": 468498, "L1D cache accesses": 32677465, "LL cache misses": 559358, "LL cache accesses": 571935, "L2D TLB accesses": 194840, "L2D TLB misses": 23481, "L2D cache misses": 313487, "L2D cache accesses": 1779730, "instructions per cycle": 1.004070319055595, "branch miss rate": 0.016363936819829016, "ITLB miss rate": 0.00025830564551699827, "DTLB miss rate": 0.0005203882633729282, "L2D TLB miss rate": 0.12051426811742968, "L1I cache miss rate": 0.009178783426601994, "L1D cache miss rate": 0.01433703624194839, "L2D cache miss rate": 0.1761430104566423, "LL cache miss rate": 0.9780097388689274}

158
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@ -0,0 +1,158 @@
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 3394144 queued and waiting for resources
srun: job 3394144 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.7658, 0.2874, 0.7506, ..., 0.3335, 0.5056, 0.9767])
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.5561239719390869 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 100':
62.49 msec task-clock:u # 0.015 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,312 page-faults:u # 53.003 K/sec
76,783,170 cycles:u # 1.229 GHz (62.65%)
77,095,702 instructions:u # 1.00 insn per cycle (80.20%)
<not supported> branches:u
370,891 branch-misses:u (94.99%)
32,730,448 L1-dcache-loads:u # 523.800 M/sec
467,718 L1-dcache-load-misses:u # 1.43% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,548,469 L1-icache-loads:u # 504.885 M/sec
298,966 L1-icache-load-misses:u # 0.95% of all L1-icache accesses
61,098,419 dTLB-loads:u # 977.786 M/sec (20.67%)
64,747 dTLB-load-misses:u # 0.11% of all dTLB cache accesses (10.91%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
4.062782709 seconds time elapsed
16.106338000 seconds user
32.399716000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.7531, 0.4727, 0.4126, ..., 0.1574, 0.5247, 0.8875])
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.6003477573394775 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 100':
323,514 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,769,937 BR_RETIRED:u
4.061021393 seconds time elapsed
16.155442000 seconds user
31.047278000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.3067, 0.4335, 0.8814, ..., 0.2370, 0.1210, 0.7695])
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.5404119491577148 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 100':
26,809,325 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,925 ITLB_WALK:u
19,003 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,516,965 L1D_TLB:u
4.031175418 seconds time elapsed
15.607232000 seconds user
30.562258000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.5013, 0.5961, 0.5565, ..., 0.3779, 0.1835, 0.6722])
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.6185996532440186 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 100':
31,104,231 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
285,499 L1I_CACHE_REFILL:u
468,498 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
32,677,465 L1D_CACHE:u
4.083129305 seconds time elapsed
16.243642000 seconds user
36.578375000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.9075, 0.2788, 0.1365, ..., 0.4240, 0.8832, 0.1064])
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 0.54673171043396 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 100':
559,358 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
571,935 LL_CACHE_RD:u
194,840 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
23,481 L2D_TLB_REFILL:u
313,487 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,779,730 L2D_CACHE:u
3.961843929 seconds time elapsed
15.425912000 seconds user
28.864046000 seconds sys

1
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@ -0,0 +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 3394146 queued and waiting for resources
srun: job 3394146 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.4608, 0.1516, 0.8492, ..., 0.8920, 0.4275, 0.8070])
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.3751039505004883 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
60.55 msec task-clock:u # 0.012 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,490 page-faults:u # 57.638 K/sec
49,977,496 cycles:u # 0.825 GHz (40.93%)
78,622,993 instructions:u # 1.57 insn per cycle (85.37%)
<not supported> branches:u
358,029 branch-misses:u
31,478,500 L1-dcache-loads:u # 519.877 M/sec
479,449 L1-dcache-load-misses:u # 1.52% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
29,991,824 L1-icache-loads:u # 495.324 M/sec
294,864 L1-icache-load-misses:u # 0.98% of all L1-icache accesses
35,154,647 dTLB-loads:u # 580.589 M/sec (23.19%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
4.986156121 seconds time elapsed
23.724703000 seconds user
145.034521000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.4697, 0.7121, 0.5987, ..., 0.2619, 0.7308, 0.3129])
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.6881086826324463 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
327,078 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,135,808 BR_RETIRED:u
5.374156677 seconds time elapsed
25.609168000 seconds user
167.278028000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.9215, 0.6706, 0.8015, ..., 0.8507, 0.8546, 0.4441])
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.2785694599151611 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
27,608,093 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,616 ITLB_WALK:u
17,185 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,866,957 L1D_TLB:u
4.861513311 seconds time elapsed
23.339077000 seconds user
141.584760000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.8973, 0.5228, 0.4492, ..., 0.7677, 0.7722, 0.1700])
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.1654376983642578 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
32,639,204 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
309,643 L1I_CACHE_REFILL:u
478,856 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
34,280,618 L1D_CACHE:u
4.677973310 seconds time elapsed
22.972655000 seconds user
125.062401000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.4542, 0.7095, 0.5701, ..., 0.2172, 0.8829, 0.7757])
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 1.1153452396392822 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
555,275 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
578,455 LL_CACHE_RD:u
188,723 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
24,635 L2D_TLB_REFILL:u
319,663 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,799,940 L2D_CACHE:u
4.655024760 seconds time elapsed
23.104641000 seconds user
122.294597000 seconds sys

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 3394143 queued and waiting for resources
srun: job 3394143 has been allocated resources
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.9170, 0.7306, 0.1175, ..., 0.0616, 0.0147, 0.6403])
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.4440653324127197 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
61.63 msec task-clock:u # 0.016 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,304 page-faults:u # 53.611 K/sec
64,734,203 cycles:u # 1.050 GHz (50.46%)
53,597,991 instructions:u # 0.83 insn per cycle (70.10%)
<not supported> branches:u
347,389 branch-misses:u (91.95%)
31,363,842 L1-dcache-loads:u # 508.915 M/sec
482,780 L1-dcache-load-misses:u # 1.54% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
30,027,001 L1-icache-loads:u # 487.223 M/sec
288,023 L1-icache-load-misses:u # 0.96% of all L1-icache accesses
44,333,825 dTLB-loads:u # 719.368 M/sec (48.58%)
74,525 dTLB-load-misses:u # 0.17% of all dTLB cache accesses (16.71%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.811654040 seconds time elapsed
15.616953000 seconds user
30.906234000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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([0.5548, 0.3514, 0.6283, ..., 0.5672, 0.1575, 0.4493])
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.44233155250549316 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
330,777 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,357,034 BR_RETIRED:u
3.835342404 seconds time elapsed
15.497637000 seconds user
28.676763000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.0953, 0.5790, 0.0112, ..., 0.9540, 0.3173, 0.4731])
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.43302106857299805 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
27,381,387 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,248 ITLB_WALK:u
17,636 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
37,436,110 L1D_TLB:u
3.828586094 seconds time elapsed
15.518057000 seconds user
31.389361000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.5456, 0.8708, 0.2037, ..., 0.8669, 0.9122, 0.2046])
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.4426534175872803 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
32,505,993 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
303,849 L1I_CACHE_REFILL:u
467,426 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
34,241,110 L1D_CACHE:u
3.811299200 seconds time elapsed
15.932195000 seconds user
30.887870000 seconds sys
/nfshomes/vut/ampere_research/pytorch/spmv.py:20: 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,
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([0.5024, 0.2304, 0.7925, ..., 0.1397, 0.5558, 0.6450])
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 0.3671383857727051 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
550,075 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
562,829 LL_CACHE_RD:u
199,285 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
24,424 L2D_TLB_REFILL:u
310,155 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,783,824 L2D_CACHE:u
3.824434783 seconds time elapsed
15.754438000 seconds user
28.226523000 seconds sys