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 3394151 queued and waiting for resources srun: job 3394151 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, ..., 545669, 545669, 545671]), col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871), nnz=545671, layout=torch.sparse_csr) tensor([0.3831, 0.6714, 0.8380, ..., 0.7892, 0.5274, 0.9035]) Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 2.044952392578125 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100': 59.01 msec task-clock:u # 0.010 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,448 page-faults:u # 58.432 K/sec 73,062,796 cycles:u # 1.238 GHz (59.95%) 88,329,175 instructions:u # 1.21 insn per cycle (93.89%) branches:u 365,177 branch-misses:u 31,850,867 L1-dcache-loads:u # 539.766 M/sec 473,835 L1-dcache-load-misses:u # 1.49% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 30,385,913 L1-icache-loads:u # 514.940 M/sec 299,969 L1-icache-load-misses:u # 0.99% of all L1-icache accesses 24,365,554 dTLB-loads:u # 412.915 M/sec (8.42%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 5.680365622 seconds time elapsed 27.656957000 seconds user 194.823873000 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, ..., 545669, 545669, 545671]), col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871), nnz=545671, layout=torch.sparse_csr) tensor([0.6906, 0.4067, 0.7042, ..., 0.8333, 0.7120, 0.3519]) Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 1.3788115978240967 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100': 331,091 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 20,013,316 BR_RETIRED:u 4.886021169 seconds time elapsed 23.105025000 seconds user 141.491451000 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, ..., 545669, 545669, 545671]), col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871), nnz=545671, layout=torch.sparse_csr) tensor([0.8755, 0.6165, 0.4104, ..., 0.6974, 0.9453, 0.9872]) Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 2.8570749759674072 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100': 26,330,936 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 5,193 ITLB_WALK:u 16,837 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 35,930,477 L1D_TLB:u 6.371573603 seconds time elapsed 30.986329000 seconds user 254.347216000 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, ..., 545669, 545669, 545671]), col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871), nnz=545671, layout=torch.sparse_csr) tensor([0.3573, 0.9331, 0.0611, ..., 0.9133, 0.6057, 0.2374]) Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 2.311248540878296 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100': 31,853,890 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 306,147 L1I_CACHE_REFILL:u 479,933 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,426,019 L1D_CACHE:u 5.718741260 seconds time elapsed 28.451593000 seconds user 214.350594000 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, ..., 545669, 545669, 545671]), col_indices=tensor([ 1, 2, 3, ..., 81869, 81699, 81863]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(81871, 81871), nnz=545671, layout=torch.sparse_csr) tensor([0.6021, 0.5679, 0.4538, ..., 0.9086, 0.9552, 0.5329]) Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 1.8193013668060303 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 100': 540,302 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 553,181 LL_CACHE_RD:u 173,206 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 21,390 L2D_TLB_REFILL:u 300,032 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,739,931 L2D_CACHE:u 5.546861941 seconds time elapsed 28.194596000 seconds user 181.004698000 seconds sys