ampere_research/pytorch/output/altra_10_30_p2p-Gnutella30_1000.output

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 3394991 queued and waiting for resources
srun: job 3394991 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,    10,    10,  ..., 88328, 88328, 88328]),
       col_indices=tensor([    1,     2,     3,  ..., 36675, 36676, 36677]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36682, 36682),
       nnz=88328, layout=torch.sparse_csr)
tensor([0.3046, 0.0725, 0.4580,  ..., 0.0593, 0.5121, 0.2116])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 3.6646029949188232 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000':

             56.52 msec task-clock:u                     #    0.008 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,194      page-faults:u                    #   56.515 K/sec                     
        58,074,747      cycles:u                         #    1.028 GHz                         (51.20%)
        90,036,443      instructions:u                   #    1.55  insn per cycle              (89.06%)
   <not supported>      branches:u                                                            
           363,262      branch-misses:u                                                       
        33,111,438      L1-dcache-loads:u                #  585.875 M/sec                     
           454,665      L1-dcache-load-misses:u          #    1.37% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,646,314      L1-icache-loads:u                #  559.951 M/sec                     
           281,443      L1-icache-load-misses:u          #    0.89% of all L1-icache accesses 
        43,495,524      dTLB-loads:u                     #  769.611 M/sec                       (11.87%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       7.033463989 seconds time elapsed

      34.670765000 seconds user
     307.031553000 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,    10,    10,  ..., 88328, 88328, 88328]),
       col_indices=tensor([    1,     2,     3,  ..., 36675, 36676, 36677]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36682, 36682),
       nnz=88328, layout=torch.sparse_csr)
tensor([0.9700, 0.1728, 0.2199,  ..., 0.6107, 0.3357, 0.2661])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 2.3380045890808105 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000':

           327,895      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,553,601      BR_RETIRED:u                                                          

       5.895917276 seconds time elapsed

      31.121063000 seconds user
     208.127447000 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,    10,    10,  ..., 88328, 88328, 88328]),
       col_indices=tensor([    1,     2,     3,  ..., 36675, 36676, 36677]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36682, 36682),
       nnz=88328, layout=torch.sparse_csr)
tensor([0.9533, 0.7568, 0.8141,  ..., 0.8395, 0.5617, 0.7830])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 4.476518869400024 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000':

        26,120,611      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             7,531      ITLB_WALK:u                                                           
            19,097      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        35,744,928      L1D_TLB:u                                                             

       8.109622410 seconds time elapsed

      38.467161000 seconds user
     370.437915000 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,    10,    10,  ..., 88328, 88328, 88328]),
       col_indices=tensor([    1,     2,     3,  ..., 36675, 36676, 36677]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36682, 36682),
       nnz=88328, layout=torch.sparse_csr)
tensor([0.6886, 0.7814, 0.9957,  ..., 0.8460, 0.1015, 0.8097])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 2.856834888458252 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000':

        31,819,981      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           284,493      L1I_CACHE_REFILL:u                                                    
           486,709      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,545,755      L1D_CACHE:u                                                           

       6.374371632 seconds time elapsed

      30.817943000 seconds user
     247.363843000 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,    10,    10,  ..., 88328, 88328, 88328]),
       col_indices=tensor([    1,     2,     3,  ..., 36675, 36676, 36677]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36682, 36682),
       nnz=88328, layout=torch.sparse_csr)
tensor([0.8464, 0.0437, 0.1230,  ..., 0.6221, 0.9268, 0.5436])
Matrix: p2p-Gnutella30
Shape: torch.Size([36682, 36682])
NNZ: 88328
Density: 6.564359899804003e-05
Time: 4.838747978210449 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella30.mtx 1000':

           544,742      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           558,323      LL_CACHE_RD:u                                                         
           190,574      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            23,746      L2D_TLB_REFILL:u                                                      
           305,844      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,736,964      L2D_CACHE:u                                                           

       8.386896120 seconds time elapsed

      39.861141000 seconds user
     395.959334000 seconds sys