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 3394146 queued and waiting for resources srun: job 3394146 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, 3, 9, ..., 572056, 572061, 572066]), col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509, 114602]), values=tensor([160642., 31335., 282373., ..., 88393., 99485., 18651.]), size=(115406, 115406), nnz=572066, layout=torch.sparse_csr) tensor([0.4608, 0.1516, 0.8492, ..., 0.8920, 0.4275, 0.8070]) Shape: torch.Size([115406, 115406]) NNZ: 572066 Density: 4.295259032005559e-05 Time: 1.3751039505004883 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100': 60.55 msec task-clock:u # 0.012 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,490 page-faults:u # 57.638 K/sec 49,977,496 cycles:u # 0.825 GHz (40.93%) 78,622,993 instructions:u # 1.57 insn per cycle (85.37%) branches:u 358,029 branch-misses:u 31,478,500 L1-dcache-loads:u # 519.877 M/sec 479,449 L1-dcache-load-misses:u # 1.52% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 29,991,824 L1-icache-loads:u # 495.324 M/sec 294,864 L1-icache-load-misses:u # 0.98% of all L1-icache accesses 35,154,647 dTLB-loads:u # 580.589 M/sec (23.19%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 4.986156121 seconds time elapsed 23.724703000 seconds user 145.034521000 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, 3, 9, ..., 572056, 572061, 572066]), col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509, 114602]), values=tensor([160642., 31335., 282373., ..., 88393., 99485., 18651.]), size=(115406, 115406), nnz=572066, layout=torch.sparse_csr) tensor([0.4697, 0.7121, 0.5987, ..., 0.2619, 0.7308, 0.3129]) Shape: torch.Size([115406, 115406]) NNZ: 572066 Density: 4.295259032005559e-05 Time: 1.6881086826324463 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100': 327,078 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 20,135,808 BR_RETIRED:u 5.374156677 seconds time elapsed 25.609168000 seconds user 167.278028000 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, 3, 9, ..., 572056, 572061, 572066]), col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509, 114602]), values=tensor([160642., 31335., 282373., ..., 88393., 99485., 18651.]), size=(115406, 115406), nnz=572066, layout=torch.sparse_csr) tensor([0.9215, 0.6706, 0.8015, ..., 0.8507, 0.8546, 0.4441]) Shape: torch.Size([115406, 115406]) NNZ: 572066 Density: 4.295259032005559e-05 Time: 1.2785694599151611 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100': 27,608,093 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,616 ITLB_WALK:u 17,185 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,866,957 L1D_TLB:u 4.861513311 seconds time elapsed 23.339077000 seconds user 141.584760000 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, 3, 9, ..., 572056, 572061, 572066]), col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509, 114602]), values=tensor([160642., 31335., 282373., ..., 88393., 99485., 18651.]), size=(115406, 115406), nnz=572066, layout=torch.sparse_csr) tensor([0.8973, 0.5228, 0.4492, ..., 0.7677, 0.7722, 0.1700]) Shape: torch.Size([115406, 115406]) NNZ: 572066 Density: 4.295259032005559e-05 Time: 1.1654376983642578 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100': 32,639,204 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 309,643 L1I_CACHE_REFILL:u 478,856 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 34,280,618 L1D_CACHE:u 4.677973310 seconds time elapsed 22.972655000 seconds user 125.062401000 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, 3, 9, ..., 572056, 572061, 572066]), col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509, 114602]), values=tensor([160642., 31335., 282373., ..., 88393., 99485., 18651.]), size=(115406, 115406), nnz=572066, layout=torch.sparse_csr) tensor([0.4542, 0.7095, 0.5701, ..., 0.2172, 0.8829, 0.7757]) Shape: torch.Size([115406, 115406]) NNZ: 572066 Density: 4.295259032005559e-05 Time: 1.1153452396392822 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100': 555,275 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 578,455 LL_CACHE_RD:u 188,723 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 24,635 L2D_TLB_REFILL:u 319,663 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,799,940 L2D_CACHE:u 4.655024760 seconds time elapsed 23.104641000 seconds user 122.294597000 seconds sys