ampere_research/pytorch/output/altra_10_30_Oregon-2_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 3394980 queued and waiting for resources
srun: job 3394980 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,   583,   584,  ..., 65459, 65460, 65460]),
       col_indices=tensor([   2,   23,   27,  ..., 3324,  958,  841]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(11806, 11806),
       nnz=65460, layout=torch.sparse_csr)
tensor([0.9231, 0.7723, 0.0509,  ..., 0.0839, 0.6982, 0.3459])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 1.5677142143249512 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000':

             64.81 msec task-clock:u                     #    0.013 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,244      page-faults:u                    #   50.056 K/sec                     
        82,069,432      cycles:u                         #    1.266 GHz                         (59.04%)
        78,292,700      instructions:u                   #    0.95  insn per cycle              (76.75%)
   <not supported>      branches:u                                                            
           341,509      branch-misses:u                                                         (90.97%)
        33,032,555      L1-dcache-loads:u                #  509.704 M/sec                     
           478,674      L1-dcache-load-misses:u          #    1.45% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,508,310      L1-icache-loads:u                #  486.184 M/sec                     
           297,528      L1-icache-load-misses:u          #    0.94% of all L1-icache accesses 
        49,358,091      dTLB-loads:u                     #  761.613 M/sec                       (27.83%)
            88,514      dTLB-load-misses:u               #    0.18% of all dTLB cache accesses  (14.82%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       5.016393105 seconds time elapsed

      16.759527000 seconds user
      31.429551000 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,   583,   584,  ..., 65459, 65460, 65460]),
       col_indices=tensor([   2,   23,   27,  ..., 3324,  958,  841]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(11806, 11806),
       nnz=65460, layout=torch.sparse_csr)
tensor([0.8423, 0.9339, 0.8037,  ..., 0.5953, 0.0649, 0.1559])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 1.516484022140503 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000':

           319,703      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        19,996,903      BR_RETIRED:u                                                          

       4.945699041 seconds time elapsed

      16.431978000 seconds user
      29.752452000 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,   583,   584,  ..., 65459, 65460, 65460]),
       col_indices=tensor([   2,   23,   27,  ..., 3324,  958,  841]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(11806, 11806),
       nnz=65460, layout=torch.sparse_csr)
tensor([0.8058, 0.2922, 0.1227,  ..., 0.2176, 0.9496, 0.8838])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 1.6458909511566162 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000':

        26,988,315      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             5,988      ITLB_WALK:u                                                           
            14,570      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,879,854      L1D_TLB:u                                                             

       5.011871473 seconds time elapsed

      16.529942000 seconds user
      30.438432000 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,   583,   584,  ..., 65459, 65460, 65460]),
       col_indices=tensor([   2,   23,   27,  ..., 3324,  958,  841]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(11806, 11806),
       nnz=65460, layout=torch.sparse_csr)
tensor([0.7728, 0.1182, 0.3337,  ..., 0.2555, 0.2523, 0.5746])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 1.529954433441162 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000':

        30,465,174      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           293,085      L1I_CACHE_REFILL:u                                                    
           487,330      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        31,932,249      L1D_CACHE:u                                                           

       4.954100105 seconds time elapsed

      16.282966000 seconds user
      28.926724000 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,   583,   584,  ..., 65459, 65460, 65460]),
       col_indices=tensor([   2,   23,   27,  ..., 3324,  958,  841]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(11806, 11806),
       nnz=65460, layout=torch.sparse_csr)
tensor([0.5613, 0.3211, 0.1739,  ..., 0.5461, 0.1391, 0.8387])
Matrix: Oregon-2
Shape: torch.Size([11806, 11806])
NNZ: 65460
Density: 0.0004696458003979807
Time: 1.5726752281188965 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/Oregon-2.mtx 1000':

           545,501      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           558,084      LL_CACHE_RD:u                                                         
           204,746      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            25,302      L2D_TLB_REFILL:u                                                      
           314,594      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,828,047      L2D_CACHE:u                                                           

       4.866549675 seconds time elapsed

      16.609257000 seconds user
      31.381282000 seconds sys