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 3394991 queued and waiting for resources srun: job 3394991 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, 10, 10, ..., 88328, 88328, 88328]), col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682), nnz=88328, layout=torch.sparse_csr) tensor([0.3046, 0.0725, 0.4580, ..., 0.0593, 0.5121, 0.2116]) Matrix: p2p-Gnutella30 Shape: torch.Size([36682, 36682]) NNZ: 88328 Density: 6.564359899804003e-05 Time: 3.6646029949188232 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000': 56.52 msec task-clock:u # 0.008 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,194 page-faults:u # 56.515 K/sec 58,074,747 cycles:u # 1.028 GHz (51.20%) 90,036,443 instructions:u # 1.55 insn per cycle (89.06%) branches:u 363,262 branch-misses:u 33,111,438 L1-dcache-loads:u # 585.875 M/sec 454,665 L1-dcache-load-misses:u # 1.37% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,646,314 L1-icache-loads:u # 559.951 M/sec 281,443 L1-icache-load-misses:u # 0.89% of all L1-icache accesses 43,495,524 dTLB-loads:u # 769.611 M/sec (11.87%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 7.033463989 seconds time elapsed 34.670765000 seconds user 307.031553000 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, 10, 10, ..., 88328, 88328, 88328]), col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682), nnz=88328, layout=torch.sparse_csr) tensor([0.9700, 0.1728, 0.2199, ..., 0.6107, 0.3357, 0.2661]) Matrix: p2p-Gnutella30 Shape: torch.Size([36682, 36682]) NNZ: 88328 Density: 6.564359899804003e-05 Time: 2.3380045890808105 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000': 327,895 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 20,553,601 BR_RETIRED:u 5.895917276 seconds time elapsed 31.121063000 seconds user 208.127447000 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, 10, 10, ..., 88328, 88328, 88328]), col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682), nnz=88328, layout=torch.sparse_csr) tensor([0.9533, 0.7568, 0.8141, ..., 0.8395, 0.5617, 0.7830]) Matrix: p2p-Gnutella30 Shape: torch.Size([36682, 36682]) NNZ: 88328 Density: 6.564359899804003e-05 Time: 4.476518869400024 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000': 26,120,611 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 7,531 ITLB_WALK:u 19,097 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 35,744,928 L1D_TLB:u 8.109622410 seconds time elapsed 38.467161000 seconds user 370.437915000 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, 10, 10, ..., 88328, 88328, 88328]), col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682), nnz=88328, layout=torch.sparse_csr) tensor([0.6886, 0.7814, 0.9957, ..., 0.8460, 0.1015, 0.8097]) Matrix: p2p-Gnutella30 Shape: torch.Size([36682, 36682]) NNZ: 88328 Density: 6.564359899804003e-05 Time: 2.856834888458252 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000': 31,819,981 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 284,493 L1I_CACHE_REFILL:u 486,709 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,545,755 L1D_CACHE:u 6.374371632 seconds time elapsed 30.817943000 seconds user 247.363843000 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, 10, 10, ..., 88328, 88328, 88328]), col_indices=tensor([ 1, 2, 3, ..., 36675, 36676, 36677]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(36682, 36682), nnz=88328, layout=torch.sparse_csr) tensor([0.8464, 0.0437, 0.1230, ..., 0.6221, 0.9268, 0.5436]) Matrix: p2p-Gnutella30 Shape: torch.Size([36682, 36682]) NNZ: 88328 Density: 6.564359899804003e-05 Time: 4.838747978210449 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000': 544,742 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 558,323 LL_CACHE_RD:u 190,574 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 23,746 L2D_TLB_REFILL:u 305,844 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,736,964 L2D_CACHE:u 8.386896120 seconds time elapsed 39.861141000 seconds user 395.959334000 seconds sys