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 3393718 queued and waiting for resources
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srun: job 3393718 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, 29, 124, ..., 549200, 549200,
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549202]),
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
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nnz=549202, layout=torch.sparse_csr)
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tensor([0.8320, 0.8961, 0.3119, ..., 0.2600, 0.3720, 0.6950])
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Shape: torch.Size([82144, 82144])
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NNZ: 549202
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Density: 8.13917555860553e-05
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Time: 3.012270212173462 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
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61.63 msec task-clock:u # 0.008 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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3,293 page-faults:u # 53.433 K/sec
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41,677,750 cycles:u # 0.676 GHz (43.47%)
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91,767,205 instructions:u # 2.20 insn per cycle (93.66%)
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<not supported> branches:u
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369,577 branch-misses:u
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33,184,885 L1-dcache-loads:u # 538.465 M/sec
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489,650 L1-dcache-load-misses:u # 1.48% 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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31,518,657 L1-icache-loads:u # 511.428 M/sec
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300,352 L1-icache-load-misses:u # 0.95% of all L1-icache accesses
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21,439,232 dTLB-loads:u # 347.878 M/sec (11.35%)
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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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7.285558270 seconds time elapsed
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30.820742000 seconds user
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271.093513000 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, 29, 124, ..., 549200, 549200,
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549202]),
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
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nnz=549202, layout=torch.sparse_csr)
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tensor([0.2625, 0.3727, 0.7700, ..., 0.9213, 0.0373, 0.4236])
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Shape: torch.Size([82144, 82144])
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NNZ: 549202
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Density: 8.13917555860553e-05
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Time: 3.8292958736419678 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
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329,386 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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19,813,961 BR_RETIRED:u
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7.818393438 seconds time elapsed
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35.952830000 seconds user
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333.700971000 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, 29, 124, ..., 549200, 549200,
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549202]),
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
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nnz=549202, layout=torch.sparse_csr)
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tensor([0.0340, 0.2650, 0.1324, ..., 0.0868, 0.2162, 0.5618])
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Shape: torch.Size([82144, 82144])
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NNZ: 549202
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Density: 8.13917555860553e-05
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Time: 3.464143753051758 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
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27,944,146 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,811 ITLB_WALK:u
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18,962 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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37,689,058 L1D_TLB:u
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7.541903779 seconds time elapsed
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32.666428000 seconds user
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309.938101000 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, 29, 124, ..., 549200, 549200,
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549202]),
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
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nnz=549202, layout=torch.sparse_csr)
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tensor([0.6118, 0.9275, 0.9072, ..., 0.7025, 0.2788, 0.7796])
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Shape: torch.Size([82144, 82144])
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NNZ: 549202
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Density: 8.13917555860553e-05
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Time: 1.4259674549102783 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
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31,746,573 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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290,044 L1I_CACHE_REFILL:u
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471,100 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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33,271,575 L1D_CACHE:u
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5.333100815 seconds time elapsed
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24.606404000 seconds user
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142.184021000 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, 29, 124, ..., 549200, 549200,
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549202]),
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col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144),
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nnz=549202, layout=torch.sparse_csr)
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tensor([0.1819, 0.6831, 0.7926, ..., 0.2272, 0.8215, 0.3765])
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Shape: torch.Size([82144, 82144])
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NNZ: 549202
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Density: 8.13917555860553e-05
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Time: 2.8267815113067627 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100':
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550,308 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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564,981 LL_CACHE_RD:u
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168,456 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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20,450 L2D_TLB_REFILL:u
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306,309 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,745,776 L2D_CACHE:u
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7.032343494 seconds time elapsed
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31.547129000 seconds user
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251.812633000 seconds sys
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