ampere_research/pytorch/output/altra_10_30_sx-mathoverflow_1000.output

srun: Job time limit was unset; set to partition default of 60 minutes
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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.             #
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srun: job 3394987 queued and waiting for resources
srun: job 3394987 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,    317,    416,  ..., 239976, 239977,
                            239978]),
       col_indices=tensor([    0,     1,     2,  ...,  1483,  2179, 24817]),
       values=tensor([151.,  17.,   6.,  ...,   1.,   1.,   1.]),
       size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.8864, 0.5637, 0.9805,  ..., 0.0234, 0.9487, 0.4860])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 5.484489917755127 seconds

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

             50.36 msec task-clock:u                     #    0.006 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,296      page-faults:u                    #   65.452 K/sec                     
        56,049,457      cycles:u                         #    1.113 GHz                         (49.66%)
        72,333,565      instructions:u                   #    1.29  insn per cycle              (66.35%)
   <not supported>      branches:u                                                            
           369,218      branch-misses:u                                                         (86.12%)
        33,730,437      L1-dcache-loads:u                #  669.814 M/sec                       (93.88%)
           459,922      L1-dcache-load-misses:u          #    1.36% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,827,672      L1-icache-loads:u                #  632.030 M/sec                     
           295,060      L1-icache-load-misses:u          #    0.93% of all L1-icache accesses 
        54,366,618      dTLB-loads:u                     #    1.080 G/sec                       (35.64%)
            84,768      dTLB-load-misses:u               #    0.16% of all dTLB cache accesses  (25.48%)
        12,107,953      iTLB-loads:u                     #  240.438 M/sec                       (10.11%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       8.968532171 seconds time elapsed

      20.749643000 seconds user
      28.745486000 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,    317,    416,  ..., 239976, 239977,
                            239978]),
       col_indices=tensor([    0,     1,     2,  ...,  1483,  2179, 24817]),
       values=tensor([151.,  17.,   6.,  ...,   1.,   1.,   1.]),
       size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.5549, 0.0336, 0.9472,  ..., 0.2657, 0.3394, 0.6185])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 5.532417297363281 seconds

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

           325,529      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,463,406      BR_RETIRED:u                                                          

       8.912497962 seconds time elapsed

      20.214519000 seconds user
      31.566513000 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,    317,    416,  ..., 239976, 239977,
                            239978]),
       col_indices=tensor([    0,     1,     2,  ...,  1483,  2179, 24817]),
       values=tensor([151.,  17.,   6.,  ...,   1.,   1.,   1.]),
       size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.3330, 0.8843, 0.5150,  ..., 0.7292, 0.0873, 0.4184])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 5.457342863082886 seconds

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

        27,374,917      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             5,203      ITLB_WALK:u                                                           
            16,771      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,373,182      L1D_TLB:u                                                             

       8.730534933 seconds time elapsed

      20.156482000 seconds user
      31.426118000 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,    317,    416,  ..., 239976, 239977,
                            239978]),
       col_indices=tensor([    0,     1,     2,  ...,  1483,  2179, 24817]),
       values=tensor([151.,  17.,   6.,  ...,   1.,   1.,   1.]),
       size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.5864, 0.4449, 0.4042,  ..., 0.1651, 0.7793, 0.8302])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 5.449937582015991 seconds

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

        31,839,975      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           274,158      L1I_CACHE_REFILL:u                                                    
           471,992      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,638,817      L1D_CACHE:u                                                           

       8.845491835 seconds time elapsed

      20.577696000 seconds user
      35.105662000 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,    317,    416,  ..., 239976, 239977,
                            239978]),
       col_indices=tensor([    0,     1,     2,  ...,  1483,  2179, 24817]),
       values=tensor([151.,  17.,   6.,  ...,   1.,   1.,   1.]),
       size=(24818, 24818), nnz=239978, layout=torch.sparse_csr)
tensor([0.8880, 0.4700, 0.5542,  ..., 0.8505, 0.9123, 0.5742])
Matrix: sx-mathoverflow
Shape: torch.Size([24818, 24818])
NNZ: 239978
Density: 0.00038961697406616504
Time: 5.400304794311523 seconds

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

           538,067      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           557,981      LL_CACHE_RD:u                                                         
           170,169      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            21,987      L2D_TLB_REFILL:u                                                      
           301,746      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,735,872      L2D_CACHE:u                                                           

       8.606800178 seconds time elapsed

      21.064990000 seconds user
      34.158762000 seconds sys