ampere_research/pytorch/output/altra_10_30_p2p-Gnutella25_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.             #
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srun: job 3394994 queued and waiting for resources
srun: job 3394994 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([0.1465, 0.4354, 0.7334,  ..., 0.2837, 0.5913, 0.9525])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.4786670207977295 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 1000':

             48.61 msec task-clock:u                     #    0.010 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,308      page-faults:u                    #   68.054 K/sec                     
        60,072,179      cycles:u                         #    1.236 GHz                         (53.26%)
        70,991,785      instructions:u                   #    1.18  insn per cycle              (71.54%)
   <not supported>      branches:u                                                            
           371,197      branch-misses:u                                                       
        32,964,378      L1-dcache-loads:u                #  678.165 M/sec                     
           465,448      L1-dcache-load-misses:u          #    1.41% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,435,424      L1-icache-loads:u                #  646.710 M/sec                     
           293,561      L1-icache-load-misses:u          #    0.93% of all L1-icache accesses 
        56,761,270      dTLB-loads:u                     #    1.168 G/sec                       (30.54%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       4.700046411 seconds time elapsed

      16.235801000 seconds user
      28.396327000 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([0.7780, 0.3388, 0.1540,  ..., 0.2989, 0.3682, 0.9160])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.4235138893127441 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 1000':

           331,765      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,906,014      BR_RETIRED:u                                                          

       4.757340585 seconds time elapsed

      16.412311000 seconds user
      29.238029000 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([0.4944, 0.8057, 0.8211,  ..., 0.5137, 0.3388, 0.6316])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.4664146900177002 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 1000':

        28,194,337      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             5,083      ITLB_WALK:u                                                           
            17,916      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        37,944,713      L1D_TLB:u                                                             

       4.844329421 seconds time elapsed

      16.081022000 seconds user
      28.021902000 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([0.0963, 0.5806, 0.0397,  ..., 0.1604, 0.5700, 0.8103])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.3717434406280518 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 1000':

        31,162,212      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           270,684      L1I_CACHE_REFILL:u                                                    
           465,467      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        32,857,500      L1D_CACHE:u                                                           

       4.598461782 seconds time elapsed

      15.609727000 seconds user
      30.606837000 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([0.9137, 0.5009, 0.7507,  ..., 0.6623, 0.8760, 0.2991])
Matrix: p2p-Gnutella25
Shape: torch.Size([22687, 22687])
NNZ: 54705
Density: 0.00010628522108964806
Time: 1.4291880130767822 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella25.mtx 1000':

           541,118      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           564,199      LL_CACHE_RD:u                                                         
           194,022      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            23,932      L2D_TLB_REFILL:u                                                      
           311,476      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,783,574      L2D_CACHE:u                                                           

       4.792239951 seconds time elapsed

      15.902307000 seconds user
      28.747620000 seconds sys