154 lines
9.3 KiB
Plaintext
154 lines
9.3 KiB
Plaintext
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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 3394148 queued and waiting for resources
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srun: job 3394148 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, 583, 584, ..., 65459, 65460, 65460]),
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col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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nnz=65460, layout=torch.sparse_csr)
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tensor([0.3190, 0.2829, 0.6210, ..., 0.9278, 0.7514, 0.5737])
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Shape: torch.Size([11806, 11806])
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NNZ: 65460
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Density: 0.0004696458003979807
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Time: 0.22389841079711914 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
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42.01 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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3,263 page-faults:u # 77.672 K/sec
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47,084,933 cycles:u # 1.121 GHz (65.90%)
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77,895,119 instructions:u # 1.65 insn per cycle (85.49%)
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<not supported> branches:u
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352,740 branch-misses:u
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30,958,922 L1-dcache-loads:u # 736.946 M/sec
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442,351 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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29,506,648 L1-icache-loads:u # 702.376 M/sec
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272,063 L1-icache-load-misses:u # 0.92% of all L1-icache accesses
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51,646,382 dTLB-loads:u # 1.229 G/sec (15.87%)
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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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3.513156571 seconds time elapsed
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15.150380000 seconds user
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32.922923000 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, 583, 584, ..., 65459, 65460, 65460]),
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col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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nnz=65460, layout=torch.sparse_csr)
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tensor([0.0741, 0.5476, 0.1060, ..., 0.8459, 0.8270, 0.8313])
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Shape: torch.Size([11806, 11806])
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NNZ: 65460
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Density: 0.0004696458003979807
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Time: 0.20610284805297852 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
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330,923 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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19,740,519 BR_RETIRED:u
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3.639725976 seconds time elapsed
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15.493122000 seconds user
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27.617441000 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, 583, 584, ..., 65459, 65460, 65460]),
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col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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nnz=65460, layout=torch.sparse_csr)
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tensor([0.9699, 0.9368, 0.7284, ..., 0.7182, 0.5308, 0.9833])
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Shape: torch.Size([11806, 11806])
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NNZ: 65460
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Density: 0.0004696458003979807
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Time: 0.15960955619812012 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
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27,761,239 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,471 ITLB_WALK:u
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17,268 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,993,265 L1D_TLB:u
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3.455602215 seconds time elapsed
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15.015027000 seconds user
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27.930709000 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, 583, 584, ..., 65459, 65460, 65460]),
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col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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nnz=65460, layout=torch.sparse_csr)
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tensor([0.5851, 0.3425, 0.8120, ..., 0.0829, 0.5823, 0.2256])
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Shape: torch.Size([11806, 11806])
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NNZ: 65460
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Density: 0.0004696458003979807
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Time: 0.15697884559631348 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
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31,834,980 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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298,333 L1I_CACHE_REFILL:u
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466,901 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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33,528,976 L1D_CACHE:u
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3.452279902 seconds time elapsed
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14.635240000 seconds user
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28.262858000 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, 583, 584, ..., 65459, 65460, 65460]),
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col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806),
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nnz=65460, layout=torch.sparse_csr)
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tensor([0.0772, 0.9112, 0.0293, ..., 0.4016, 0.4357, 0.5368])
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Shape: torch.Size([11806, 11806])
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NNZ: 65460
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Density: 0.0004696458003979807
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Time: 0.20962285995483398 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 100':
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525,505 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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546,521 LL_CACHE_RD:u
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184,884 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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22,933 L2D_TLB_REFILL:u
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292,367 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,706,226 L2D_CACHE:u
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3.566096255 seconds time elapsed
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15.763579000 seconds user
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28.620423000 seconds sys
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