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 3394979 queued and waiting for resources srun: job 3394979 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([0.4201, 0.7748, 0.6565, ..., 0.0517, 0.6958, 0.5341]) Matrix: soc-sign-Slashdot090221 Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 27.35153603553772 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000': 58.57 msec task-clock:u # 0.002 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,259 page-faults:u # 55.640 K/sec 74,509,373 cycles:u # 1.272 GHz (58.00%) 88,672,751 instructions:u # 1.19 insn per cycle (90.97%) branches:u 361,568 branch-misses:u 31,594,797 L1-dcache-loads:u # 539.410 M/sec 460,467 L1-dcache-load-misses:u # 1.46% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 30,148,838 L1-icache-loads:u # 514.724 M/sec 282,768 L1-icache-load-misses:u # 0.94% of all L1-icache accesses 19,757,856 dTLB-loads:u # 337.321 M/sec (11.69%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 31.087250856 seconds time elapsed 142.716222000 seconds user 2102.420776000 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([0.7637, 0.5328, 0.8286, ..., 0.7084, 0.8903, 0.1707]) Matrix: soc-sign-Slashdot090221 Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 17.188836336135864 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000': 342,121 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 20,436,338 BR_RETIRED:u 20.753346873 seconds time elapsed 98.605331000 seconds user 1332.291974000 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([0.9017, 0.8505, 0.0023, ..., 0.4182, 0.6895, 0.5023]) Matrix: soc-sign-Slashdot090221 Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 16.22375249862671 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000': 27,189,335 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,437 ITLB_WALK:u 18,156 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,676,625 L1D_TLB:u 19.748749363 seconds time elapsed 103.049578000 seconds user 1249.814927000 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([0.4805, 0.2325, 0.2103, ..., 0.1710, 0.7638, 0.9368]) Matrix: soc-sign-Slashdot090221 Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 15.453373908996582 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000': 30,721,032 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 302,777 L1I_CACHE_REFILL:u 469,833 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 32,109,077 L1D_CACHE:u 19.090250444 seconds time elapsed 94.904880000 seconds user 1195.102767000 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([0.8430, 0.9439, 0.4260, ..., 0.8172, 0.4243, 0.3834]) Matrix: soc-sign-Slashdot090221 Shape: torch.Size([82144, 82144]) NNZ: 549202 Density: 8.13917555860553e-05 Time: 29.316507816314697 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000': 551,850 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 565,355 LL_CACHE_RD:u 200,417 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 25,536 L2D_TLB_REFILL:u 304,133 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,801,849 L2D_CACHE:u 32.859276963 seconds time elapsed 148.969816000 seconds user 2252.321936000 seconds sys