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 3394140 queued and waiting for resources srun: job 3394140 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, 9, 9, ..., 54704, 54704, 54705]), col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687), nnz=54705, layout=torch.sparse_csr) tensor([0.8199, 0.9849, 0.4642, ..., 0.7594, 0.3568, 0.4020]) Shape: torch.Size([22687, 22687]) NNZ: 54705 Density: 0.00010628522108964806 Time: 0.19272208213806152 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100': 64.71 msec task-clock:u # 0.018 CPUs utilized 0 context-switches:u # 0.000 /sec 0 cpu-migrations:u # 0.000 /sec 3,319 page-faults:u # 51.288 K/sec 57,611,295 cycles:u # 0.890 GHz (39.00%) 83,148,228 instructions:u # 1.44 insn per cycle (82.73%) branches:u 375,111 branch-misses:u 32,759,228 L1-dcache-loads:u # 506.221 M/sec 475,086 L1-dcache-load-misses:u # 1.45% of all L1-dcache accesses LLC-loads:u LLC-load-misses:u 31,366,158 L1-icache-loads:u # 484.694 M/sec 297,293 L1-icache-load-misses:u # 0.95% of all L1-icache accesses 35,611,781 dTLB-loads:u # 550.301 M/sec (25.73%) dTLB-load-misses:u (0.00%) iTLB-loads:u (0.00%) iTLB-load-misses:u (0.00%) 3.578384817 seconds time elapsed 14.435258000 seconds user 27.700836000 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, 9, 9, ..., 54704, 54704, 54705]), col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687), nnz=54705, layout=torch.sparse_csr) tensor([0.0069, 0.9904, 0.5316, ..., 0.2082, 0.4858, 0.4936]) Shape: torch.Size([22687, 22687]) NNZ: 54705 Density: 0.00010628522108964806 Time: 0.1423017978668213 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100': 318,386 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio 19,233,431 BR_RETIRED:u 3.555753224 seconds time elapsed 14.642518000 seconds user 30.112207000 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, 9, 9, ..., 54704, 54704, 54705]), col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687), nnz=54705, layout=torch.sparse_csr) tensor([0.2250, 0.5676, 0.3018, ..., 0.5431, 0.7314, 0.5593]) Shape: torch.Size([22687, 22687]) NNZ: 54705 Density: 0.00010628522108964806 Time: 0.14638042449951172 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100': 27,039,805 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio 6,375 ITLB_WALK:u 17,290 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio 36,688,544 L1D_TLB:u 3.566915241 seconds time elapsed 16.116565000 seconds user 28.752519000 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, 9, 9, ..., 54704, 54704, 54705]), col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687), nnz=54705, layout=torch.sparse_csr) tensor([0.0220, 0.7494, 0.7913, ..., 0.8924, 0.8542, 0.5491]) Shape: torch.Size([22687, 22687]) NNZ: 54705 Density: 0.00010628522108964806 Time: 0.17815685272216797 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100': 32,508,072 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio 297,568 L1I_CACHE_REFILL:u 477,654 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio 34,044,579 L1D_CACHE:u 3.435706033 seconds time elapsed 14.690285000 seconds user 28.763423000 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, 9, 9, ..., 54704, 54704, 54705]), col_indices=tensor([ 1, 2, 3, ..., 17949, 22685, 144]), values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(22687, 22687), nnz=54705, layout=torch.sparse_csr) tensor([0.6277, 0.4955, 0.9335, ..., 0.1476, 0.2079, 0.0931]) Shape: torch.Size([22687, 22687]) NNZ: 54705 Density: 0.00010628522108964806 Time: 0.14432048797607422 seconds Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 100': 549,474 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio 561,939 LL_CACHE_RD:u 185,622 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio 23,295 L2D_TLB_REFILL:u 305,878 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio 1,763,089 L2D_CACHE:u 3.538826979 seconds time elapsed 15.006109000 seconds user 29.644298000 seconds sys