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 3393718 queued and waiting for resources srun: job 3393718 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, 29, 124, ..., 549200, 549200, 549202]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), nnz=549202, layout=torch.sparse_csr) tensor([0.8320, 0.8961, 0.3119, ..., 0.2600, 0.3720, 0.6950]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 3.012270212173462 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': 61.63 msec task-clock:u # 0.008 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,293 page-faults:u # 53.433 K/sec 41,677,750 cycles:u # 0.676 GHz (43.47%) 91,767,205 instructions:u # 2.20 insn per cycle (93.66%) branches:u 369,577 branch-misses:u 33,184,885 L1-dcache-loads:u # 538.465 M/sec 489,650 L1-dcache-load-misses:u # 1.48% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,518,657 L1-icache-loads:u # 511.428 M/sec 300,352 L1-icache-load-misses:u # 0.95% of all L1-icache accesses 21,439,232 dTLB-loads:u # 347.878 M/sec (11.35%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 7.285558270 seconds time elapsed 30.820742000 seconds user 271.093513000 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, 29, 124, ..., 549200, 549200, 549202]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), nnz=549202, layout=torch.sparse_csr) tensor([0.2625, 0.3727, 0.7700, ..., 0.9213, 0.0373, 0.4236]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 3.8292958736419678 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': 329,386 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,813,961 BR_RETIRED:u 7.818393438 seconds time elapsed 35.952830000 seconds user 333.700971000 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, 29, 124, ..., 549200, 549200, 549202]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), nnz=549202, layout=torch.sparse_csr) tensor([0.0340, 0.2650, 0.1324, ..., 0.0868, 0.2162, 0.5618]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 3.464143753051758 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': 27,944,146 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,811 ITLB_WALK:u 18,962 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 37,689,058 L1D_TLB:u 7.541903779 seconds time elapsed 32.666428000 seconds user 309.938101000 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, 29, 124, ..., 549200, 549200, 549202]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), nnz=549202, layout=torch.sparse_csr) tensor([0.6118, 0.9275, 0.9072, ..., 0.7025, 0.2788, 0.7796]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.4259674549102783 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': 31,746,573 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 290,044 L1I_CACHE_REFILL:u 471,100 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,271,575 L1D_CACHE:u 5.333100815 seconds time elapsed 24.606404000 seconds user 142.184021000 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, 29, 124, ..., 549200, 549200, 549202]), col_indices=tensor([ 1, 2, 3, ..., 82142, 81974, 82136]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(82144, 82144), nnz=549202, layout=torch.sparse_csr) tensor([0.1819, 0.6831, 0.7926, ..., 0.2272, 0.8215, 0.3765]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 2.8267815113067627 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 100': 550,308 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 564,981 LL_CACHE_RD:u 168,456 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 20,450 L2D_TLB_REFILL:u 306,309 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,745,776 L2D_CACHE:u 7.032343494 seconds time elapsed 31.547129000 seconds user 251.812633000 seconds sys