164 lines
9.7 KiB
Plaintext
164 lines
9.7 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 3394988 queued and waiting for resources
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srun: job 3394988 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, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.2022, 0.3400, 0.2561, ..., 0.8370, 0.0285, 0.6506])
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Matrix: vt2010
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 3.74875545501709 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':
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48.59 msec task-clock:u # 0.007 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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3,274 page-faults:u # 67.376 K/sec
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55,030,923 cycles:u # 1.132 GHz (65.54%)
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78,222,423 instructions:u # 1.42 insn per cycle (83.60%)
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<not supported> branches:u
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369,917 branch-misses:u
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32,435,815 L1-dcache-loads:u # 667.500 M/sec
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467,963 L1-dcache-load-misses:u # 1.44% 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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31,013,287 L1-icache-loads:u # 638.226 M/sec
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289,982 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
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60,644,978 dTLB-loads:u # 1.248 G/sec (17.29%)
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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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6.978143797 seconds time elapsed
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18.401752000 seconds user
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28.060858000 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, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.3381, 0.0423, 0.5363, ..., 0.0429, 0.4077, 0.4744])
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Matrix: vt2010
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 3.7925527095794678 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':
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323,004 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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19,091,130 BR_RETIRED:u
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7.233250772 seconds time elapsed
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19.111768000 seconds user
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32.178633000 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, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.7962, 0.6492, 0.2778, ..., 0.5407, 0.1159, 0.3587])
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Matrix: vt2010
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 3.668635129928589 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':
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27,178,617 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,398 ITLB_WALK:u
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19,770 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,355,567 L1D_TLB:u
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6.925944164 seconds time elapsed
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18.970654000 seconds user
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30.786317000 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, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.8340, 0.3434, 0.3449, ..., 0.9828, 0.6683, 0.0312])
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Matrix: vt2010
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 3.623232126235962 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':
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31,341,858 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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291,951 L1I_CACHE_REFILL:u
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468,242 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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32,805,413 L1D_CACHE:u
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6.941260499 seconds time elapsed
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18.410270000 seconds user
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27.908787000 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, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.2754, 0.3661, 0.9484, ..., 0.7285, 0.5354, 0.4116])
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Matrix: vt2010
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 3.7337992191314697 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':
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520,057 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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541,186 LL_CACHE_RD:u
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191,068 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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22,725 L2D_TLB_REFILL:u
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288,895 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,728,320 L2D_CACHE:u
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7.164825085 seconds time elapsed
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18.193885000 seconds user
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30.023194000 seconds sys
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