ampere_research/pytorch/output/altra_10_30_p2p-Gnutella24_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 3394989 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,     9,     9,  ..., 65369, 65369, 65369]),
       col_indices=tensor([    1,     2,     3,  ..., 15065,  9401, 26517]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(26518, 26518),
       nnz=65369, layout=torch.sparse_csr)
tensor([0.2470, 0.4231, 0.1036,  ..., 0.7937, 0.3241, 0.7116])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 1.6974337100982666 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 1000':

             66.78 msec task-clock:u                     #    0.013 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,520      page-faults:u                    #   52.713 K/sec                     
        28,858,055      cycles:u                         #    0.432 GHz                         (26.93%)
        64,429,843      instructions:u                   #    2.23  insn per cycle              (67.63%)
   <not supported>      branches:u                                                            
           296,857      branch-misses:u                                                         (84.08%)
        33,646,348      L1-dcache-loads:u                #  503.866 M/sec                     
           493,998      L1-dcache-load-misses:u          #    1.47% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        32,070,415      L1-icache-loads:u                #  480.266 M/sec                     
           305,993      L1-icache-load-misses:u          #    0.95% of all L1-icache accesses 
        46,903,081      dTLB-loads:u                     #  702.391 M/sec                       (46.16%)
           114,272      dTLB-load-misses:u               #    0.24% of all dTLB cache accesses  (32.45%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       5.106933083 seconds time elapsed

      16.391614000 seconds user
      28.913912000 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,  ..., 65369, 65369, 65369]),
       col_indices=tensor([    1,     2,     3,  ..., 15065,  9401, 26517]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(26518, 26518),
       nnz=65369, layout=torch.sparse_csr)
tensor([0.2307, 0.4662, 0.3789,  ..., 0.0144, 0.6300, 0.7829])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 1.6379659175872803 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 1000':

           331,167      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,518,210      BR_RETIRED:u                                                          

       5.017894585 seconds time elapsed

      16.446505000 seconds user
      31.004338000 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,  ..., 65369, 65369, 65369]),
       col_indices=tensor([    1,     2,     3,  ..., 15065,  9401, 26517]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(26518, 26518),
       nnz=65369, layout=torch.sparse_csr)
tensor([0.7309, 0.0314, 0.4424,  ..., 0.7434, 0.2124, 0.1432])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 1.7232718467712402 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 1000':

        26,964,483      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             4,666      ITLB_WALK:u                                                           
            14,001      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,143,905      L1D_TLB:u                                                             

       5.053286721 seconds time elapsed

      16.447780000 seconds user
      28.580949000 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,  ..., 65369, 65369, 65369]),
       col_indices=tensor([    1,     2,     3,  ..., 15065,  9401, 26517]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(26518, 26518),
       nnz=65369, layout=torch.sparse_csr)
tensor([0.5695, 0.5025, 0.1946,  ..., 0.7428, 0.9634, 0.4327])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 1.644775629043579 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 1000':

        31,901,160      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           302,516      L1I_CACHE_REFILL:u                                                    
           475,663      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,507,563      L1D_CACHE:u                                                           

       4.978338941 seconds time elapsed

      16.455298000 seconds user
      30.249373000 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,  ..., 65369, 65369, 65369]),
       col_indices=tensor([    1,     2,     3,  ..., 15065,  9401, 26517]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(26518, 26518),
       nnz=65369, layout=torch.sparse_csr)
tensor([0.0969, 0.1950, 0.8456,  ..., 0.3315, 0.1512, 0.3182])
Matrix: p2p-Gnutella24
Shape: torch.Size([26518, 26518])
NNZ: 65369
Density: 9.295875717624285e-05
Time: 1.752812385559082 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/p2p-Gnutella24.mtx 1000':

           558,546      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           578,676      LL_CACHE_RD:u                                                         
           187,549      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            22,990      L2D_TLB_REFILL:u                                                      
           321,826      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,816,571      L2D_CACHE:u                                                           

       4.952297819 seconds time elapsed

      16.648691000 seconds user
      27.005944000 seconds sys