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ampere_research/pytorch/output/altra_2_2_soc-sign-Slashdot090216_100.output

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Got output!
2024-12-03 00:20:09 -05:00
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,
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([0.3831, 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
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