ampere_research/pytorch/output/altra_10_30_p2p-Gnutella04_1000.output

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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    #
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srun: job 3394992 queued and waiting for resources
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/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,    20,  ..., 39994, 39994, 39994]),
       col_indices=tensor([    1,     2,     3,  ...,  9711, 10875, 10876]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(10879, 10879),
       nnz=39994, layout=torch.sparse_csr)
tensor([0.1181, 0.8387, 0.0554,  ..., 0.8107, 0.4393, 0.9489])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 1.061662197113037 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 1000':

             50.59 msec task-clock:u                     #    0.012 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,303      page-faults:u                    #   65.291 K/sec                     
        51,318,459      cycles:u                         #    1.014 GHz                         (59.34%)
        74,705,078      instructions:u                   #    1.46  insn per cycle              (83.02%)
   <not supported>      branches:u                                                            
           366,825      branch-misses:u                                                       
        31,809,194      L1-dcache-loads:u                #  628.781 M/sec                     
           466,198      L1-dcache-load-misses:u          #    1.47% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        30,390,161      L1-icache-loads:u                #  600.731 M/sec                     
           296,270      L1-icache-load-misses:u          #    0.97% of all L1-icache accesses 
        61,518,375      dTLB-loads:u                     #    1.216 G/sec                       (17.94%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       4.302241563 seconds time elapsed

      16.122298000 seconds user
      29.141140000 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,    20,  ..., 39994, 39994, 39994]),
       col_indices=tensor([    1,     2,     3,  ...,  9711, 10875, 10876]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(10879, 10879),
       nnz=39994, layout=torch.sparse_csr)
tensor([0.7249, 0.8723, 0.3843,  ..., 0.2264, 0.4891, 0.9107])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 1.0079431533813477 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 1000':

           328,853      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,620,312      BR_RETIRED:u                                                          

       4.241400567 seconds time elapsed

      15.325937000 seconds user
      28.223386000 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,    20,  ..., 39994, 39994, 39994]),
       col_indices=tensor([    1,     2,     3,  ...,  9711, 10875, 10876]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(10879, 10879),
       nnz=39994, layout=torch.sparse_csr)
tensor([0.7608, 0.2449, 0.5322,  ..., 0.5547, 0.8659, 0.8437])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 1.1017234325408936 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 1000':

        27,939,682      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             5,470      ITLB_WALK:u                                                           
            17,679      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        37,425,602      L1D_TLB:u                                                             

       4.296820500 seconds time elapsed

      15.875162000 seconds user
      28.803412000 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,    20,  ..., 39994, 39994, 39994]),
       col_indices=tensor([    1,     2,     3,  ...,  9711, 10875, 10876]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(10879, 10879),
       nnz=39994, layout=torch.sparse_csr)
tensor([0.9980, 0.9991, 0.6749,  ..., 0.4225, 0.7297, 0.3717])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 1.0812580585479736 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 1000':

        30,276,633      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           291,467      L1I_CACHE_REFILL:u                                                    
           479,061      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        31,689,326      L1D_CACHE:u                                                           

       4.500137840 seconds time elapsed

      15.794710000 seconds user
      27.773851000 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,    20,  ..., 39994, 39994, 39994]),
       col_indices=tensor([    1,     2,     3,  ...,  9711, 10875, 10876]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(10879, 10879),
       nnz=39994, layout=torch.sparse_csr)
tensor([0.8707, 0.5871, 0.5970,  ..., 0.8826, 0.4673, 0.4994])
Matrix: p2p-Gnutella04
Shape: torch.Size([10879, 10879])
NNZ: 39994
Density: 0.0003379223282393842
Time: 0.9900743961334229 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella04.mtx 1000':

           529,426      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           550,033      LL_CACHE_RD:u                                                         
           171,913      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            20,624      L2D_TLB_REFILL:u                                                      
           296,662      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,714,211      L2D_CACHE:u                                                           

       4.284402033 seconds time elapsed

      15.584671000 seconds user
      27.523772000 seconds sys