159 lines
9.6 KiB
Plaintext
159 lines
9.6 KiB
Plaintext
srun: Job time limit was unset; set to partition default of 60 minutes
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srun: ################################################################################
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srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
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srun: # All submission nodes and all other compute nodes have x86_64 architecture #
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srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
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srun: # nodes may need to be rebuilt to properly execute on these nodes. #
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srun: ################################################################################
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srun: job 3394152 queued and waiting for resources
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srun: job 3394152 has been allocated resources
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/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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
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367662]),
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col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
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nnz=367662, layout=torch.sparse_csr)
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tensor([0.3626, 0.7532, 0.0782, ..., 0.6679, 0.4308, 0.6586])
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Shape: torch.Size([36692, 36692])
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NNZ: 367662
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Density: 0.0002730901120626302
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Time: 1.3745801448822021 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
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60.43 msec task-clock:u # 0.012 CPUs utilized
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0 context-switches:u # 0.000 /sec
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0 cpu-migrations:u # 0.000 /sec
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3,319 page-faults:u # 54.926 K/sec
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66,114,448 cycles:u # 1.094 GHz (58.10%)
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90,786,829 instructions:u # 1.37 insn per cycle (92.25%)
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<not supported> branches:u
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372,381 branch-misses:u
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32,997,410 L1-dcache-loads:u # 546.070 M/sec
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470,216 L1-dcache-load-misses:u # 1.43% of all L1-dcache accesses
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<not supported> LLC-loads:u
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<not supported> LLC-load-misses:u
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31,485,339 L1-icache-loads:u # 521.047 M/sec
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294,395 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
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31,376,646 dTLB-loads:u # 519.248 M/sec (10.03%)
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<not counted> dTLB-load-misses:u (0.00%)
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<not counted> iTLB-loads:u (0.00%)
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<not counted> iTLB-load-misses:u (0.00%)
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4.904488673 seconds time elapsed
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22.874521000 seconds user
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139.276239000 seconds sys
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/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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
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367662]),
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col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
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nnz=367662, layout=torch.sparse_csr)
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tensor([0.2040, 0.8252, 0.0215, ..., 0.2921, 0.9143, 0.8728])
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Shape: torch.Size([36692, 36692])
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NNZ: 367662
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Density: 0.0002730901120626302
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Time: 1.3087654113769531 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
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341,625 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,129,354 BR_RETIRED:u
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4.644873434 seconds time elapsed
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22.729927000 seconds user
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132.278582000 seconds sys
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/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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
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367662]),
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col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
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nnz=367662, layout=torch.sparse_csr)
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tensor([0.6154, 0.6641, 0.3794, ..., 0.9736, 0.0619, 0.4790])
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Shape: torch.Size([36692, 36692])
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NNZ: 367662
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Density: 0.0002730901120626302
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Time: 1.2701547145843506 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
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27,441,303 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,807 ITLB_WALK:u
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20,551 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,867,114 L1D_TLB:u
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4.861510767 seconds time elapsed
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22.111354000 seconds user
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132.431608000 seconds sys
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/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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
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367662]),
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col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
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nnz=367662, layout=torch.sparse_csr)
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tensor([0.4201, 0.4134, 0.8169, ..., 0.6631, 0.0087, 0.8439])
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Shape: torch.Size([36692, 36692])
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NNZ: 367662
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Density: 0.0002730901120626302
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Time: 1.1176586151123047 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
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31,744,243 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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271,027 L1I_CACHE_REFILL:u
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464,135 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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33,441,141 L1D_CACHE:u
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4.693803969 seconds time elapsed
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21.724904000 seconds user
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119.873018000 seconds sys
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/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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 1, 71, ..., 367660, 367661,
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367662]),
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col_indices=tensor([ 1, 0, 2, ..., 36690, 36689, 8203]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36692, 36692),
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nnz=367662, layout=torch.sparse_csr)
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tensor([0.1285, 0.3989, 0.3903, ..., 0.7892, 0.2737, 0.2659])
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Shape: torch.Size([36692, 36692])
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NNZ: 367662
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Density: 0.0002730901120626302
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Time: 1.196892261505127 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/email-Enron.mtx 100':
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539,935 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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552,519 LL_CACHE_RD:u
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188,291 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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24,177 L2D_TLB_REFILL:u
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301,281 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,737,575 L2D_CACHE:u
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4.741030347 seconds time elapsed
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23.793930000 seconds user
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125.634838000 seconds sys
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