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 3394982 queued and waiting for resources srun: job 3394982 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, 1, 2, ..., 766390, 766394, 766396]), col_indices=tensor([ 0, 1, 2, ..., 116833, 89, 116834]), values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ..., 1.0331e+01, -1.0000e-03, 1.0000e-03]), size=(116835, 116835), nnz=766396, layout=torch.sparse_csr) tensor([0.0986, 0.6504, 0.0132, ..., 0.6525, 0.3337, 0.7557]) Matrix: dc2 Shape: torch.Size([116835, 116835]) NNZ: 766396 Density: 5.614451099680581e-05 Time: 18.46260714530945 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 1000': 58.45 msec task-clock:u # 0.003 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,471 page-faults:u # 59.382 K/sec 76,691,414 cycles:u # 1.312 GHz (41.20%) 89,547,095 instructions:u # 1.17 insn per cycle (73.16%) branches:u 382,362 branch-misses:u (96.21%) 33,271,433 L1-dcache-loads:u # 569.211 M/sec 488,730 L1-dcache-load-misses:u # 1.47% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,926,596 L1-icache-loads:u # 546.204 M/sec 304,792 L1-icache-load-misses:u # 0.95% of all L1-icache accesses 36,392,791 dTLB-loads:u # 622.612 M/sec (31.21%) 0 dTLB-load-misses:u (5.35%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 22.126601025 seconds time elapsed 103.642372000 seconds user 1434.131491000 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, 1, 2, ..., 766390, 766394, 766396]), col_indices=tensor([ 0, 1, 2, ..., 116833, 89, 116834]), values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ..., 1.0331e+01, -1.0000e-03, 1.0000e-03]), size=(116835, 116835), nnz=766396, layout=torch.sparse_csr) tensor([0.5605, 0.9374, 0.4444, ..., 0.5937, 0.3099, 0.2252]) Matrix: dc2 Shape: torch.Size([116835, 116835]) NNZ: 766396 Density: 5.614451099680581e-05 Time: 13.607120752334595 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 1000': 329,725 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,946,857 BR_RETIRED:u 17.131143957 seconds time elapsed 96.945305000 seconds user 1045.242697000 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, 1, 2, ..., 766390, 766394, 766396]), col_indices=tensor([ 0, 1, 2, ..., 116833, 89, 116834]), values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ..., 1.0331e+01, -1.0000e-03, 1.0000e-03]), size=(116835, 116835), nnz=766396, layout=torch.sparse_csr) tensor([0.8954, 0.9777, 0.8042, ..., 0.2069, 0.7063, 0.8479]) Matrix: dc2 Shape: torch.Size([116835, 116835]) NNZ: 766396 Density: 5.614451099680581e-05 Time: 17.22396969795227 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 1000': 27,648,951 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,857 ITLB_WALK:u 18,047 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 37,225,736 L1D_TLB:u 20.911480243 seconds time elapsed 107.392462000 seconds user 1329.272154000 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, 1, 2, ..., 766390, 766394, 766396]), col_indices=tensor([ 0, 1, 2, ..., 116833, 89, 116834]), values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ..., 1.0331e+01, -1.0000e-03, 1.0000e-03]), size=(116835, 116835), nnz=766396, layout=torch.sparse_csr) tensor([0.9293, 0.9606, 0.8914, ..., 0.2407, 0.2843, 0.5174]) Matrix: dc2 Shape: torch.Size([116835, 116835]) NNZ: 766396 Density: 5.614451099680581e-05 Time: 13.233965873718262 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 1000': 32,434,686 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 293,072 L1I_CACHE_REFILL:u 483,557 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 34,059,722 L1D_CACHE:u 16.956477005 seconds time elapsed 88.393687000 seconds user 1037.101858000 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, 1, 2, ..., 766390, 766394, 766396]), col_indices=tensor([ 0, 1, 2, ..., 116833, 89, 116834]), values=tensor([-1.0000e+00, -1.0000e+00, -1.0000e+00, ..., 1.0331e+01, -1.0000e-03, 1.0000e-03]), size=(116835, 116835), nnz=766396, layout=torch.sparse_csr) tensor([0.8850, 0.9552, 0.7029, ..., 0.3357, 0.0248, 0.5395]) Matrix: dc2 Shape: torch.Size([116835, 116835]) NNZ: 766396 Density: 5.614451099680581e-05 Time: 13.873224973678589 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/dc2.mtx 1000': 561,480 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 578,369 LL_CACHE_RD:u 192,306 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 25,364 L2D_TLB_REFILL:u 317,121 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,812,330 L2D_CACHE:u 17.467787426 seconds time elapsed 92.463054000 seconds user 1072.584062000 seconds sys