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 3394981 queued and waiting for resources srun: job 3394981 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([0.6780, 0.5234, 0.1205, ..., 0.2995, 0.6275, 0.1399]) Matrix: soc-sign-Slashdot090216 Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 30.653191089630127 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000': 67.66 msec task-clock:u # 0.002 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,317 page-faults:u # 49.022 K/sec 41,915,850 cycles:u # 0.619 GHz (57.88%) 84,471,787 instructions:u # 2.02 insn per cycle (88.19%) branches:u 375,016 branch-misses:u 32,438,527 L1-dcache-loads:u # 479.407 M/sec 499,618 L1-dcache-load-misses:u # 1.54% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 30,998,693 L1-icache-loads:u # 458.127 M/sec 306,445 L1-icache-load-misses:u # 0.99% of all L1-icache accesses 34,294,934 dTLB-loads:u # 506.842 M/sec (18.86%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 34.340632995 seconds time elapsed 149.743244000 seconds user 2355.852109000 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([0.9875, 0.2031, 0.7260, ..., 0.5908, 0.1575, 0.7971]) Matrix: soc-sign-Slashdot090216 Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 13.671181440353394 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000': 344,452 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 20,610,765 BR_RETIRED:u 17.331425967 seconds time elapsed 83.136180000 seconds user 1069.027469000 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([0.2046, 0.3645, 0.7960, ..., 0.6490, 0.4098, 0.5342]) Matrix: soc-sign-Slashdot090216 Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 19.569235801696777 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000': 27,276,117 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,358 ITLB_WALK:u 17,361 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,565,837 L1D_TLB:u 23.323243037 seconds time elapsed 108.830923000 seconds user 1521.834565000 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([0.4164, 0.2188, 0.5460, ..., 0.1057, 0.5277, 0.0624]) Matrix: soc-sign-Slashdot090216 Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 26.337355375289917 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000': 32,022,662 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 293,044 L1I_CACHE_REFILL:u 458,939 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 33,505,164 L1D_CACHE:u 30.017812847 seconds time elapsed 131.976276000 seconds user 2029.636174000 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([0.7679, 0.9196, 0.3474, ..., 0.5624, 0.0163, 0.8596]) Matrix: soc-sign-Slashdot090216 Shape: torch.Size([81871, 81871]) NNZ: 545671 Density: 8.140867447881048e-05 Time: 29.926054000854492 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000': 553,814 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 567,372 LL_CACHE_RD:u 199,301 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 25,193 L2D_TLB_REFILL:u 313,278 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,796,299 L2D_CACHE:u 33.553779692 seconds time elapsed 154.498461000 seconds user 2293.574463000 seconds sys