ampere_research/pytorch/output/altra_10_30_ri2010_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 3394984 queued and waiting for resources
srun: job 3394984 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,      3,      8,  ..., 125742, 125747,
                            125750]),
       col_indices=tensor([   25,    56,   662,  ..., 21738, 22279, 23882]),
       values=tensor([17171., 37318.,  5284.,  ..., 25993., 24918.,   803.]),
       size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.5906, 0.9651, 0.2033,  ..., 0.2175, 0.4484, 0.0412])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 3.107008934020996 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 1000':

             64.49 msec task-clock:u                     #    0.010 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,473      page-faults:u                    #   53.852 K/sec                     
        42,783,607      cycles:u                         #    0.663 GHz                         (37.27%)
        84,598,454      instructions:u                   #    1.98  insn per cycle              (73.53%)
   <not supported>      branches:u                                                            
           353,558      branch-misses:u                                                         (89.57%)
        33,192,964      L1-dcache-loads:u                #  514.689 M/sec                     
           466,217      L1-dcache-load-misses:u          #    1.40% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,727,502      L1-icache-loads:u                #  491.965 M/sec                     
           292,570      L1-icache-load-misses:u          #    0.92% of all L1-icache accesses 
        38,623,737      dTLB-loads:u                     #  598.898 M/sec                       (34.88%)
           124,174      dTLB-load-misses:u               #    0.32% of all dTLB cache accesses  (14.74%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

       6.612563197 seconds time elapsed

      18.114584000 seconds user
      29.808542000 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,      3,      8,  ..., 125742, 125747,
                            125750]),
       col_indices=tensor([   25,    56,   662,  ..., 21738, 22279, 23882]),
       values=tensor([17171., 37318.,  5284.,  ..., 25993., 24918.,   803.]),
       size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.6092, 0.5511, 0.6052,  ..., 0.8002, 0.0295, 0.2972])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 2.9385879039764404 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 1000':

           331,326      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,438,455      BR_RETIRED:u                                                          

       6.446731410 seconds time elapsed

      17.939571000 seconds user
      33.272929000 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,      3,      8,  ..., 125742, 125747,
                            125750]),
       col_indices=tensor([   25,    56,   662,  ..., 21738, 22279, 23882]),
       values=tensor([17171., 37318.,  5284.,  ..., 25993., 24918.,   803.]),
       size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.3348, 0.2974, 0.2569,  ..., 0.2397, 0.1965, 0.5651])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 2.972891330718994 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 1000':

        26,869,742      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,302      ITLB_WALK:u                                                           
            14,926      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,876,841      L1D_TLB:u                                                             

       6.376775396 seconds time elapsed

      17.836418000 seconds user
      29.830135000 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,      3,      8,  ..., 125742, 125747,
                            125750]),
       col_indices=tensor([   25,    56,   662,  ..., 21738, 22279, 23882]),
       values=tensor([17171., 37318.,  5284.,  ..., 25993., 24918.,   803.]),
       size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.7889, 0.7395, 0.6553,  ..., 0.3938, 0.2478, 0.7923])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 2.9658284187316895 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 1000':

        31,664,385      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           301,678      L1I_CACHE_REFILL:u                                                    
           493,536      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,219,437      L1D_CACHE:u                                                           

       6.559158078 seconds time elapsed

      19.008146000 seconds user
      38.233666000 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,      3,      8,  ..., 125742, 125747,
                            125750]),
       col_indices=tensor([   25,    56,   662,  ..., 21738, 22279, 23882]),
       values=tensor([17171., 37318.,  5284.,  ..., 25993., 24918.,   803.]),
       size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.1256, 0.1417, 0.9800,  ..., 0.2509, 0.8121, 0.6210])
Matrix: ri2010
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 2.9228267669677734 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 1000':

           552,180      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           564,990      LL_CACHE_RD:u                                                         
           167,824      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            19,594      L2D_TLB_REFILL:u                                                      
           304,114      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,716,370      L2D_CACHE:u                                                           

       6.135787277 seconds time elapsed

      18.029630000 seconds user
      28.723217000 seconds sys