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 3394987 queued and waiting for resources srun: job 3394987 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, 317, 416, ..., 239976, 239977, 239978]), col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]), values=tensor([151., 17., 6., ..., 1., 1., 1.]), size=(24818, 24818), nnz=239978, layout=torch.sparse_csr) tensor([0.8864, 0.5637, 0.9805, ..., 0.0234, 0.9487, 0.4860]) Matrix: sx-mathoverflow Shape: torch.Size([24818, 24818]) NNZ: 239978 Density: 0.00038961697406616504 Time: 5.484489917755127 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 1000': 50.36 msec task-clock:u # 0.006 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,296 page-faults:u # 65.452 K/sec 56,049,457 cycles:u # 1.113 GHz (49.66%) 72,333,565 instructions:u # 1.29 insn per cycle (66.35%) branches:u 369,218 branch-misses:u (86.12%) 33,730,437 L1-dcache-loads:u # 669.814 M/sec (93.88%) 459,922 L1-dcache-load-misses:u # 1.36% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,827,672 L1-icache-loads:u # 632.030 M/sec 295,060 L1-icache-load-misses:u # 0.93% of all L1-icache accesses 54,366,618 dTLB-loads:u # 1.080 G/sec (35.64%) 84,768 dTLB-load-misses:u # 0.16% of all dTLB cache accesses (25.48%) 12,107,953 iTLB-loads:u # 240.438 M/sec (10.11%) iTLB-load-misses:u (0.00%) 8.968532171 seconds time elapsed 20.749643000 seconds user 28.745486000 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, 317, 416, ..., 239976, 239977, 239978]), col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]), values=tensor([151., 17., 6., ..., 1., 1., 1.]), size=(24818, 24818), nnz=239978, layout=torch.sparse_csr) tensor([0.5549, 0.0336, 0.9472, ..., 0.2657, 0.3394, 0.6185]) Matrix: sx-mathoverflow Shape: torch.Size([24818, 24818]) NNZ: 239978 Density: 0.00038961697406616504 Time: 5.532417297363281 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 1000': 325,529 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,463,406 BR_RETIRED:u 8.912497962 seconds time elapsed 20.214519000 seconds user 31.566513000 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, 317, 416, ..., 239976, 239977, 239978]), col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]), values=tensor([151., 17., 6., ..., 1., 1., 1.]), size=(24818, 24818), nnz=239978, layout=torch.sparse_csr) tensor([0.3330, 0.8843, 0.5150, ..., 0.7292, 0.0873, 0.4184]) Matrix: sx-mathoverflow Shape: torch.Size([24818, 24818]) NNZ: 239978 Density: 0.00038961697406616504 Time: 5.457342863082886 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 1000': 27,374,917 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 5,203 ITLB_WALK:u 16,771 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,373,182 L1D_TLB:u 8.730534933 seconds time elapsed 20.156482000 seconds user 31.426118000 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, 317, 416, ..., 239976, 239977, 239978]), col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]), values=tensor([151., 17., 6., ..., 1., 1., 1.]), size=(24818, 24818), nnz=239978, layout=torch.sparse_csr) tensor([0.5864, 0.4449, 0.4042, ..., 0.1651, 0.7793, 0.8302]) Matrix: sx-mathoverflow Shape: torch.Size([24818, 24818]) NNZ: 239978 Density: 0.00038961697406616504 Time: 5.449937582015991 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 1000': 31,839,975 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 274,158 L1I_CACHE_REFILL:u 471,992 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,638,817 L1D_CACHE:u 8.845491835 seconds time elapsed 20.577696000 seconds user 35.105662000 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, 317, 416, ..., 239976, 239977, 239978]), col_indices=tensor([ 0, 1, 2, ..., 1483, 2179, 24817]), values=tensor([151., 17., 6., ..., 1., 1., 1.]), size=(24818, 24818), nnz=239978, layout=torch.sparse_csr) tensor([0.8880, 0.4700, 0.5542, ..., 0.8505, 0.9123, 0.5742]) Matrix: sx-mathoverflow Shape: torch.Size([24818, 24818]) NNZ: 239978 Density: 0.00038961697406616504 Time: 5.400304794311523 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/sx-mathoverflow.mtx 1000': 538,067 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 557,981 LL_CACHE_RD:u 170,169 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 21,987 L2D_TLB_REFILL:u 301,746 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,735,872 L2D_CACHE:u 8.606800178 seconds time elapsed 21.064990000 seconds user 34.158762000 seconds sys