164 lines
9.8 KiB
Plaintext
164 lines
9.8 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 3394139 queued and waiting for resources
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srun: job 3394139 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, 13, 21, ..., 116047, 116051,
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116056]),
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col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
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values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
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16949.]), size=(24115, 24115), nnz=116056,
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layout=torch.sparse_csr)
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tensor([0.4207, 0.3943, 0.6543, ..., 0.2191, 0.5415, 0.1575])
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Shape: torch.Size([24115, 24115])
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NNZ: 116056
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Density: 0.0001995689928120616
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Time: 0.36042284965515137 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
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59.88 msec task-clock:u # 0.016 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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3,313 page-faults:u # 55.328 K/sec
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58,169,777 cycles:u # 0.971 GHz (61.49%)
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57,993,431 instructions:u # 1.00 insn per cycle (81.67%)
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<not supported> branches:u
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341,266 branch-misses:u
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31,858,781 L1-dcache-loads:u # 532.049 M/sec
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467,486 L1-dcache-load-misses:u # 1.47% 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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30,461,310 L1-icache-loads:u # 508.711 M/sec
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294,156 L1-icache-load-misses:u # 0.97% of all L1-icache accesses
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43,828,130 dTLB-loads:u # 731.940 M/sec (40.26%)
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47,836 dTLB-load-misses:u # 0.11% of all dTLB cache accesses (25.52%)
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0 iTLB-loads:u # 0.000 /sec (2.73%)
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<not counted> iTLB-load-misses:u (0.00%)
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3.824054028 seconds time elapsed
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15.099361000 seconds user
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28.830417000 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, 13, 21, ..., 116047, 116051,
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116056]),
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col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
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values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
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16949.]), size=(24115, 24115), nnz=116056,
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layout=torch.sparse_csr)
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tensor([0.0456, 0.2095, 0.0276, ..., 0.4209, 0.6824, 0.5475])
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Shape: torch.Size([24115, 24115])
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NNZ: 116056
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Density: 0.0001995689928120616
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Time: 0.3598823547363281 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
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330,494 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,578,427 BR_RETIRED:u
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3.781234836 seconds time elapsed
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14.965545000 seconds user
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29.444131000 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, 13, 21, ..., 116047, 116051,
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116056]),
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col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
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values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
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16949.]), size=(24115, 24115), nnz=116056,
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layout=torch.sparse_csr)
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tensor([0.9882, 0.5477, 0.6307, ..., 0.1179, 0.6903, 0.1235])
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Shape: torch.Size([24115, 24115])
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NNZ: 116056
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Density: 0.0001995689928120616
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Time: 0.29088521003723145 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
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27,982,097 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,614 ITLB_WALK:u
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17,270 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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37,728,899 L1D_TLB:u
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3.576632300 seconds time elapsed
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14.864601000 seconds user
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29.274547000 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, 13, 21, ..., 116047, 116051,
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116056]),
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col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
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values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
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16949.]), size=(24115, 24115), nnz=116056,
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layout=torch.sparse_csr)
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tensor([0.3952, 0.0475, 0.1125, ..., 0.3481, 0.1290, 0.3495])
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Shape: torch.Size([24115, 24115])
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NNZ: 116056
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Density: 0.0001995689928120616
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Time: 0.30365920066833496 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
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29,754,926 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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278,786 L1I_CACHE_REFILL:u
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454,742 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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31,173,246 L1D_CACHE:u
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3.730995381 seconds time elapsed
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15.213930000 seconds user
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30.995070000 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, 13, 21, ..., 116047, 116051,
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116056]),
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col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]),
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values=tensor([ 14900., 33341., 20255., ..., 164227., 52413.,
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16949.]), size=(24115, 24115), nnz=116056,
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layout=torch.sparse_csr)
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tensor([0.7266, 0.7537, 0.9729, ..., 0.3349, 0.3523, 0.6532])
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Shape: torch.Size([24115, 24115])
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NNZ: 116056
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Density: 0.0001995689928120616
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Time: 0.2798902988433838 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 100':
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543,243 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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560,716 LL_CACHE_RD:u
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162,281 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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19,847 L2D_TLB_REFILL:u
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300,577 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,696,278 L2D_CACHE:u
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3.819959836 seconds time elapsed
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15.346035000 seconds user
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29.199873000 seconds sys
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