ampere_research/pytorch/output/altra_10_30_soc-sign-Slashdot090216_1000.output

srun: Job time limit was unset; set to partition default of 60 minutes
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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     #
srun: # nodes may need to be rebuilt to properly execute on these nodes.             #
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srun: job 3394981 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,     29,    124,  ..., 545669, 545669,
                            545671]),
       col_indices=tensor([    1,     2,     3,  ..., 81869, 81699, 81863]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(81871, 81871),
       nnz=545671, layout=torch.sparse_csr)
tensor([0.6780, 0.5234, 0.1205,  ..., 0.2995, 0.6275, 0.1399])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 30.653191089630127 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000':

             67.66 msec task-clock:u                     #    0.002 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,317      page-faults:u                    #   49.022 K/sec                     
        41,915,850      cycles:u                         #    0.619 GHz                         (57.88%)
        84,471,787      instructions:u                   #    2.02  insn per cycle              (88.19%)
   <not supported>      branches:u                                                            
           375,016      branch-misses:u                                                       
        32,438,527      L1-dcache-loads:u                #  479.407 M/sec                     
           499,618      L1-dcache-load-misses:u          #    1.54% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        30,998,693      L1-icache-loads:u                #  458.127 M/sec                     
           306,445      L1-icache-load-misses:u          #    0.99% of all L1-icache accesses 
        34,294,934      dTLB-loads:u                     #  506.842 M/sec                       (18.86%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

      34.340632995 seconds time elapsed

     149.743244000 seconds user
    2355.852109000 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,     29,    124,  ..., 545669, 545669,
                            545671]),
       col_indices=tensor([    1,     2,     3,  ..., 81869, 81699, 81863]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(81871, 81871),
       nnz=545671, layout=torch.sparse_csr)
tensor([0.9875, 0.2031, 0.7260,  ..., 0.5908, 0.1575, 0.7971])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 13.671181440353394 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000':

           344,452      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,610,765      BR_RETIRED:u                                                          

      17.331425967 seconds time elapsed

      83.136180000 seconds user
    1069.027469000 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,     29,    124,  ..., 545669, 545669,
                            545671]),
       col_indices=tensor([    1,     2,     3,  ..., 81869, 81699, 81863]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(81871, 81871),
       nnz=545671, layout=torch.sparse_csr)
tensor([0.2046, 0.3645, 0.7960,  ..., 0.6490, 0.4098, 0.5342])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 19.569235801696777 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000':

        27,276,117      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,358      ITLB_WALK:u                                                           
            17,361      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,565,837      L1D_TLB:u                                                             

      23.323243037 seconds time elapsed

     108.830923000 seconds user
    1521.834565000 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,     29,    124,  ..., 545669, 545669,
                            545671]),
       col_indices=tensor([    1,     2,     3,  ..., 81869, 81699, 81863]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(81871, 81871),
       nnz=545671, layout=torch.sparse_csr)
tensor([0.4164, 0.2188, 0.5460,  ..., 0.1057, 0.5277, 0.0624])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 26.337355375289917 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000':

        32,022,662      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           293,044      L1I_CACHE_REFILL:u                                                    
           458,939      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,505,164      L1D_CACHE:u                                                           

      30.017812847 seconds time elapsed

     131.976276000 seconds user
    2029.636174000 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,     29,    124,  ..., 545669, 545669,
                            545671]),
       col_indices=tensor([    1,     2,     3,  ..., 81869, 81699, 81863]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(81871, 81871),
       nnz=545671, layout=torch.sparse_csr)
tensor([0.7679, 0.9196, 0.3474,  ..., 0.5624, 0.0163, 0.8596])
Matrix: soc-sign-Slashdot090216
Shape: torch.Size([81871, 81871])
NNZ: 545671
Density: 8.140867447881048e-05
Time: 29.926054000854492 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090216.mtx 1000':

           553,814      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           567,372      LL_CACHE_RD:u                                                         
           199,301      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            25,193      L2D_TLB_REFILL:u                                                      
           313,278      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,796,299      L2D_CACHE:u                                                           

      33.553779692 seconds time elapsed

     154.498461000 seconds user
    2293.574463000 seconds sys