ampere_research/pytorch/output/altra_2_2_soc-sign-Slashdot090221_100.output
2024-12-03 00:20:09 -05:00

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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 3394147 queued and waiting for 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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 29, 124, ..., 549200, 549200,
549202]),
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.2696, 0.6106, 0.1626, ..., 0.2215, 0.5107, 0.8609])
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
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':
61.26 msec task-clock:u # 0.012 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,303 page-faults:u # 53.917 K/sec
44,515,786 cycles:u # 0.727 GHz (40.46%)
81,513,738 instructions:u # 1.83 insn per cycle (73.51%)
<not supported> branches:u
344,479 branch-misses:u (89.42%)
34,411,073 L1-dcache-loads:u # 561.710 M/sec
484,811 L1-dcache-load-misses:u # 1.41% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
32,789,672 L1-icache-loads:u # 535.243 M/sec
293,487 L1-icache-load-misses:u # 0.90% of all L1-icache accesses
47,065,740 dTLB-loads:u # 768.279 M/sec (32.81%)
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-load-misses:u (0.00%)
4.966101053 seconds time elapsed
23.375418000 seconds user
148.052989000 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, ..., 549200, 549200,
549202]),
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.1999, 0.3932, 0.8035, ..., 0.5079, 0.5903, 0.7606])
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
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':
328,019 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,893,662 BR_RETIRED:u
5.529871590 seconds time elapsed
26.844356000 seconds user
190.429440000 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, ..., 549200, 549200,
549202]),
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.2933, 0.6999, 0.0078, ..., 0.6213, 0.9377, 0.6359])
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
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':
27,248,112 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
5,792 ITLB_WALK:u
16,632 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,929,042 L1D_TLB:u
4.971341163 seconds time elapsed
24.247480000 seconds user
151.276717000 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, ..., 549200, 549200,
549202]),
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.1310, 0.6695, 0.9479, ..., 0.3141, 0.9327, 0.2117])
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
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':
31,702,830 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
295,778 L1I_CACHE_REFILL:u
470,423 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,155,119 L1D_CACHE:u
4.675682406 seconds time elapsed
23.098007000 seconds user
119.827712000 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, ..., 549200, 549200,
549202]),
col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
nnz=549202, layout=torch.sparse_csr)
tensor([0.0860, 0.5402, 0.6738, ..., 0.3856, 0.5968, 0.4203])
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
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':
545,220 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
562,139 LL_CACHE_RD:u
192,206 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
24,891 L2D_TLB_REFILL:u
307,033 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,782,260 L2D_CACHE:u
4.781838296 seconds time elapsed
23.716896000 seconds user
130.971947000 seconds sys