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 3394147 queued and waiting for resources srun: job 3394147 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([0.2696, 0.6106, 0.1626, ..., 0.2215, 0.5107, 0.8609]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.4500706195831299 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': 61.26 msec task-clock:u # 0.012 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,303 page-faults:u # 53.917 K/sec 44,515,786 cycles:u # 0.727 GHz (40.46%) 81,513,738 instructions:u # 1.83 insn per cycle (73.51%) branches:u 344,479 branch-misses:u (89.42%) 34,411,073 L1-dcache-loads:u # 561.710 M/sec 484,811 L1-dcache-load-misses:u # 1.41% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 32,789,672 L1-icache-loads:u # 535.243 M/sec 293,487 L1-icache-load-misses:u # 0.90% of all L1-icache accesses 47,065,740 dTLB-loads:u # 768.279 M/sec (32.81%) 146,215 dTLB-load-misses:u # 0.31% of all dTLB cache accesses (13.39%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 4.966101053 seconds time elapsed 23.375418000 seconds user 148.052989000 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([0.1999, 0.3932, 0.8035, ..., 0.5079, 0.5903, 0.7606]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.9677543640136719 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': 328,019 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,893,662 BR_RETIRED:u 5.529871590 seconds time elapsed 26.844356000 seconds user 190.429440000 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([0.2933, 0.6999, 0.0078, ..., 0.6213, 0.9377, 0.6359]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.4976201057434082 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': 27,248,112 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 5,792 ITLB_WALK:u 16,632 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,929,042 L1D_TLB:u 4.971341163 seconds time elapsed 24.247480000 seconds user 151.276717000 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([0.1310, 0.6695, 0.9479, ..., 0.3141, 0.9327, 0.2117]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.0877256393432617 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': 31,702,830 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 295,778 L1I_CACHE_REFILL:u 470,423 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,155,119 L1D_CACHE:u 4.675682406 seconds time elapsed 23.098007000 seconds user 119.827712000 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([0.0860, 0.5402, 0.6738, ..., 0.3856, 0.5968, 0.4203]) Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 1.2302696704864502 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': 545,220 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 562,139 LL_CACHE_RD:u 192,206 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 24,891 L2D_TLB_REFILL:u 307,033 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,782,260 L2D_CACHE:u 4.781838296 seconds time elapsed 23.716896000 seconds user 130.971947000 seconds sys