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 3394980 queued and waiting for resources srun: job 3394980 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, 583, 584, ..., 65459, 65460, 65460]), col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806), nnz=65460, layout=torch.sparse_csr) tensor([0.9231, 0.7723, 0.0509, ..., 0.0839, 0.6982, 0.3459]) Matrix: Oregon-2 Shape: torch.Size([11806, 11806]) NNZ: 65460 Density: 0.0004696458003979807 Time: 1.5677142143249512 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000': 64.81 msec task-clock:u # 0.013 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,244 page-faults:u # 50.056 K/sec 82,069,432 cycles:u # 1.266 GHz (59.04%) 78,292,700 instructions:u # 0.95 insn per cycle (76.75%) branches:u 341,509 branch-misses:u (90.97%) 33,032,555 L1-dcache-loads:u # 509.704 M/sec 478,674 L1-dcache-load-misses:u # 1.45% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,508,310 L1-icache-loads:u # 486.184 M/sec 297,528 L1-icache-load-misses:u # 0.94% of all L1-icache accesses 49,358,091 dTLB-loads:u # 761.613 M/sec (27.83%) 88,514 dTLB-load-misses:u # 0.18% of all dTLB cache accesses (14.82%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 5.016393105 seconds time elapsed 16.759527000 seconds user 31.429551000 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, 583, 584, ..., 65459, 65460, 65460]), col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806), nnz=65460, layout=torch.sparse_csr) tensor([0.8423, 0.9339, 0.8037, ..., 0.5953, 0.0649, 0.1559]) Matrix: Oregon-2 Shape: torch.Size([11806, 11806]) NNZ: 65460 Density: 0.0004696458003979807 Time: 1.516484022140503 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000': 319,703 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,996,903 BR_RETIRED:u 4.945699041 seconds time elapsed 16.431978000 seconds user 29.752452000 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, 583, 584, ..., 65459, 65460, 65460]), col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806), nnz=65460, layout=torch.sparse_csr) tensor([0.8058, 0.2922, 0.1227, ..., 0.2176, 0.9496, 0.8838]) Matrix: Oregon-2 Shape: torch.Size([11806, 11806]) NNZ: 65460 Density: 0.0004696458003979807 Time: 1.6458909511566162 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000': 26,988,315 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 5,988 ITLB_WALK:u 14,570 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,879,854 L1D_TLB:u 5.011871473 seconds time elapsed 16.529942000 seconds user 30.438432000 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, 583, 584, ..., 65459, 65460, 65460]), col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806), nnz=65460, layout=torch.sparse_csr) tensor([0.7728, 0.1182, 0.3337, ..., 0.2555, 0.2523, 0.5746]) Matrix: Oregon-2 Shape: torch.Size([11806, 11806]) NNZ: 65460 Density: 0.0004696458003979807 Time: 1.529954433441162 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000': 30,465,174 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 293,085 L1I_CACHE_REFILL:u 487,330 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 31,932,249 L1D_CACHE:u 4.954100105 seconds time elapsed 16.282966000 seconds user 28.926724000 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, 583, 584, ..., 65459, 65460, 65460]), col_indices=tensor([ 2, 23, 27, ..., 3324, 958, 841]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(11806, 11806), nnz=65460, layout=torch.sparse_csr) tensor([0.5613, 0.3211, 0.1739, ..., 0.5461, 0.1391, 0.8387]) Matrix: Oregon-2 Shape: torch.Size([11806, 11806]) NNZ: 65460 Density: 0.0004696458003979807 Time: 1.5726752281188965 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000': 545,501 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 558,084 LL_CACHE_RD:u 204,746 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 25,302 L2D_TLB_REFILL:u 314,594 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,828,047 L2D_CACHE:u 4.866549675 seconds time elapsed 16.609257000 seconds user 31.381282000 seconds sys