ampere_research/pytorch/output/altra_10_30_soc-sign-epinions_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    #
srun: # CPUs. Programs, environments, or other software that was built on x86_64     #
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srun: job 3394990 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,      1,      2,  ..., 841371, 841371,
                            841372]),
       col_indices=tensor([     1, 128552,      3,  ..., 131824, 131826,
                             7714]),
       values=tensor([-1., -1.,  1.,  ...,  1.,  1.,  1.]),
       size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.3914, 0.2076, 0.6733,  ..., 0.4758, 0.6360, 0.6316])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 20.04187798500061 seconds

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

             63.90 msec task-clock:u                     #    0.003 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,446      page-faults:u                    #   53.927 K/sec                     
        55,931,043      cycles:u                         #    0.875 GHz                         (85.43%)
        77,907,356      instructions:u                   #    1.39  insn per cycle            
   <not supported>      branches:u                                                            
           357,739      branch-misses:u                                                       
        33,000,188      L1-dcache-loads:u                #  516.421 M/sec                     
           466,824      L1-dcache-load-misses:u          #    1.41% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,503,048      L1-icache-loads:u                #  492.992 M/sec                     
           301,112      L1-icache-load-misses:u          #    0.96% of all L1-icache accesses 
        34,740,872      dTLB-loads:u                     #  543.661 M/sec                       (18.37%)
            32,355      dTLB-load-misses:u               #    0.09% of all dTLB cache accesses  (12.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

      23.478083368 seconds time elapsed

     119.232326000 seconds user
    1541.081607000 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,      1,      2,  ..., 841371, 841371,
                            841372]),
       col_indices=tensor([     1, 128552,      3,  ..., 131824, 131826,
                             7714]),
       values=tensor([-1., -1.,  1.,  ...,  1.,  1.,  1.]),
       size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.3970, 0.5643, 0.0036,  ..., 0.0338, 0.0807, 0.3885])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 16.115705490112305 seconds

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

           332,778      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,000,746      BR_RETIRED:u                                                          

      19.765627973 seconds time elapsed

     103.591961000 seconds user
    1250.845091000 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,      1,      2,  ..., 841371, 841371,
                            841372]),
       col_indices=tensor([     1, 128552,      3,  ..., 131824, 131826,
                             7714]),
       values=tensor([-1., -1.,  1.,  ...,  1.,  1.,  1.]),
       size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.0049, 0.4550, 0.3166,  ..., 0.3734, 0.8337, 0.5156])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 18.55180263519287 seconds

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

        27,000,304      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,713      ITLB_WALK:u                                                           
            18,689      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,395,663      L1D_TLB:u                                                             

      22.333459337 seconds time elapsed

     109.075160000 seconds user
    1441.055730000 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,      1,      2,  ..., 841371, 841371,
                            841372]),
       col_indices=tensor([     1, 128552,      3,  ..., 131824, 131826,
                             7714]),
       values=tensor([-1., -1.,  1.,  ...,  1.,  1.,  1.]),
       size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.0560, 0.8530, 0.8946,  ..., 0.4591, 0.5391, 0.2898])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 25.587534427642822 seconds

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

        32,396,405      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           292,629      L1I_CACHE_REFILL:u                                                    
           473,799      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        34,061,981      L1D_CACHE:u                                                           

      29.367381835 seconds time elapsed

     142.233743000 seconds user
    1962.747683000 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,      1,      2,  ..., 841371, 841371,
                            841372]),
       col_indices=tensor([     1, 128552,      3,  ..., 131824, 131826,
                             7714]),
       values=tensor([-1., -1.,  1.,  ...,  1.,  1.,  1.]),
       size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
tensor([0.7002, 0.7829, 0.1511,  ..., 0.3651, 0.2391, 0.7788])
Matrix: soc-sign-epinions
Shape: torch.Size([131828, 131828])
NNZ: 841372
Density: 4.841419648464106e-05
Time: 23.656178951263428 seconds

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

           542,765      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           557,193      LL_CACHE_RD:u                                                         
           203,626      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            24,363      L2D_TLB_REFILL:u                                                      
           303,397      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,772,084      L2D_CACHE:u                                                           

      27.453055481 seconds time elapsed

     128.709934000 seconds user
    1831.887905000 seconds sys