srun: Job time limit was unset; set to partition default of 60 minutes srun: ################################################################################ srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. # srun: # All submission nodes and all other compute nodes have x86_64 architecture # srun: # CPUs. Programs, environments, or other software that was built on x86_64 # srun: # nodes may need to be rebuilt to properly execute on these nodes. # srun: ################################################################################ srun: job 3394985 queued and waiting for resources srun: job 3394985 has been allocated resources /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.) ).to_sparse_csr().type(torch.float) tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051, 116056]), col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]), values=tensor([ 14900., 33341., 20255., ..., 164227., 52413., 16949.]), size=(24115, 24115), nnz=116056, layout=torch.sparse_csr) tensor([0.6055, 0.8789, 0.0482, ..., 0.0736, 0.1316, 0.6744]) Matrix: de2010 Shape: torch.Size([24115, 24115]) NNZ: 116056 Density: 0.0001995689928120616 Time: 2.6956887245178223 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 1000': 48.96 msec task-clock:u # 0.008 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,285 page-faults:u # 67.090 K/sec 48,563,060 cycles:u # 0.992 GHz (59.76%) 73,465,190 instructions:u # 1.51 insn per cycle (78.23%) branches:u 369,314 branch-misses:u (98.16%) 31,769,641 L1-dcache-loads:u # 648.836 M/sec 479,594 L1-dcache-load-misses:u # 1.51% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 30,338,929 L1-icache-loads:u # 619.616 M/sec 282,162 L1-icache-load-misses:u # 0.93% of all L1-icache accesses 55,516,925 dTLB-loads:u # 1.134 G/sec (23.54%) 12,345 dTLB-load-misses:u # 0.02% of all dTLB cache accesses (3.47%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 6.017085179 seconds time elapsed 17.484355000 seconds user 28.678064000 seconds sys /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.) ).to_sparse_csr().type(torch.float) tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051, 116056]), col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]), values=tensor([ 14900., 33341., 20255., ..., 164227., 52413., 16949.]), size=(24115, 24115), nnz=116056, layout=torch.sparse_csr) tensor([0.2815, 0.8196, 0.3706, ..., 0.1328, 0.4062, 0.9113]) Matrix: de2010 Shape: torch.Size([24115, 24115]) NNZ: 116056 Density: 0.0001995689928120616 Time: 2.7908551692962646 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 1000': 326,361 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,599,354 BR_RETIRED:u 6.215591535 seconds time elapsed 18.097112000 seconds user 27.831633000 seconds sys /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.) ).to_sparse_csr().type(torch.float) tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051, 116056]), col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]), values=tensor([ 14900., 33341., 20255., ..., 164227., 52413., 16949.]), size=(24115, 24115), nnz=116056, layout=torch.sparse_csr) tensor([0.9002, 0.0843, 0.5558, ..., 0.3931, 0.8070, 0.7414]) Matrix: de2010 Shape: torch.Size([24115, 24115]) NNZ: 116056 Density: 0.0001995689928120616 Time: 2.819589376449585 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 1000': 26,666,488 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,643 ITLB_WALK:u 17,347 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 35,986,736 L1D_TLB:u 6.243883495 seconds time elapsed 17.783312000 seconds user 31.714619000 seconds sys /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.) ).to_sparse_csr().type(torch.float) tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051, 116056]), col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]), values=tensor([ 14900., 33341., 20255., ..., 164227., 52413., 16949.]), size=(24115, 24115), nnz=116056, layout=torch.sparse_csr) tensor([0.9109, 0.6392, 0.7899, ..., 0.0945, 0.3298, 0.6865]) Matrix: de2010 Shape: torch.Size([24115, 24115]) NNZ: 116056 Density: 0.0001995689928120616 Time: 2.747800827026367 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 1000': 32,502,068 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 302,739 L1I_CACHE_REFILL:u 480,619 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 34,031,072 L1D_CACHE:u 6.126767063 seconds time elapsed 17.702029000 seconds user 29.137072000 seconds sys /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.) ).to_sparse_csr().type(torch.float) tensor(crow_indices=tensor([ 0, 13, 21, ..., 116047, 116051, 116056]), col_indices=tensor([ 250, 251, 757, ..., 23334, 23553, 24050]), values=tensor([ 14900., 33341., 20255., ..., 164227., 52413., 16949.]), size=(24115, 24115), nnz=116056, layout=torch.sparse_csr) tensor([0.7083, 0.6766, 0.7649, ..., 0.3027, 0.9885, 0.8086]) Matrix: de2010 Shape: torch.Size([24115, 24115]) NNZ: 116056 Density: 0.0001995689928120616 Time: 2.795116901397705 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/de2010.mtx 1000': 552,815 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 567,373 LL_CACHE_RD:u 188,248 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 23,165 L2D_TLB_REFILL:u 308,211 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,787,647 L2D_CACHE:u 6.041792624 seconds time elapsed 17.791735000 seconds user 29.790006000 seconds sys