ampere_research/pytorch/output/altra_10_30_vt2010_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 3394988 queued and waiting for resources
srun: job 3394988 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,      4,      7,  ..., 155588, 155592,
                            155598]),
       col_indices=tensor([  131,   561,   996,  ..., 32237, 32238, 32570]),
       values=tensor([79040.,  7820., 15136.,  ...,  2828., 17986.,  2482.]),
       size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.2022, 0.3400, 0.2561,  ..., 0.8370, 0.0285, 0.6506])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 3.74875545501709 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':

             48.59 msec task-clock:u                     #    0.007 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,274      page-faults:u                    #   67.376 K/sec                     
        55,030,923      cycles:u                         #    1.132 GHz                         (65.54%)
        78,222,423      instructions:u                   #    1.42  insn per cycle              (83.60%)
   <not supported>      branches:u                                                            
           369,917      branch-misses:u                                                       
        32,435,815      L1-dcache-loads:u                #  667.500 M/sec                     
           467,963      L1-dcache-load-misses:u          #    1.44% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,013,287      L1-icache-loads:u                #  638.226 M/sec                     
           289,982      L1-icache-load-misses:u          #    0.94% of all L1-icache accesses 
        60,644,978      dTLB-loads:u                     #    1.248 G/sec                       (17.29%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       6.978143797 seconds time elapsed

      18.401752000 seconds user
      28.060858000 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,      4,      7,  ..., 155588, 155592,
                            155598]),
       col_indices=tensor([  131,   561,   996,  ..., 32237, 32238, 32570]),
       values=tensor([79040.,  7820., 15136.,  ...,  2828., 17986.,  2482.]),
       size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.3381, 0.0423, 0.5363,  ..., 0.0429, 0.4077, 0.4744])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 3.7925527095794678 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':

           323,004      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,091,130      BR_RETIRED:u                                                          

       7.233250772 seconds time elapsed

      19.111768000 seconds user
      32.178633000 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,      4,      7,  ..., 155588, 155592,
                            155598]),
       col_indices=tensor([  131,   561,   996,  ..., 32237, 32238, 32570]),
       values=tensor([79040.,  7820., 15136.,  ...,  2828., 17986.,  2482.]),
       size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.7962, 0.6492, 0.2778,  ..., 0.5407, 0.1159, 0.3587])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 3.668635129928589 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':

        27,178,617      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,398      ITLB_WALK:u                                                           
            19,770      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,355,567      L1D_TLB:u                                                             

       6.925944164 seconds time elapsed

      18.970654000 seconds user
      30.786317000 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,      4,      7,  ..., 155588, 155592,
                            155598]),
       col_indices=tensor([  131,   561,   996,  ..., 32237, 32238, 32570]),
       values=tensor([79040.,  7820., 15136.,  ...,  2828., 17986.,  2482.]),
       size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.8340, 0.3434, 0.3449,  ..., 0.9828, 0.6683, 0.0312])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 3.623232126235962 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':

        31,341,858      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           291,951      L1I_CACHE_REFILL:u                                                    
           468,242      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        32,805,413      L1D_CACHE:u                                                           

       6.941260499 seconds time elapsed

      18.410270000 seconds user
      27.908787000 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,      4,      7,  ..., 155588, 155592,
                            155598]),
       col_indices=tensor([  131,   561,   996,  ..., 32237, 32238, 32570]),
       values=tensor([79040.,  7820., 15136.,  ...,  2828., 17986.,  2482.]),
       size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
tensor([0.2754, 0.3661, 0.9484,  ..., 0.7285, 0.5354, 0.4116])
Matrix: vt2010
Shape: torch.Size([32580, 32580])
NNZ: 155598
Density: 0.00014658915806621921
Time: 3.7337992191314697 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 1000':

           520,057      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           541,186      LL_CACHE_RD:u                                                         
           191,068      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            22,725      L2D_TLB_REFILL:u                                                      
           288,895      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,728,320      L2D_CACHE:u                                                           

       7.164825085 seconds time elapsed

      18.193885000 seconds user
      30.023194000 seconds sys