ampere_research/pytorch/output/altra_10_30_soc-sign-Slashdot090221_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 3394979 queued and waiting for resources
srun: job 3394979 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,     29,    124,  ..., 549200, 549200,
                            549202]),
       col_indices=tensor([    1,     2,     3,  ..., 82142, 81974, 82136]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(82144, 82144),
       nnz=549202, layout=torch.sparse_csr)
tensor([0.4201, 0.7748, 0.6565,  ..., 0.0517, 0.6958, 0.5341])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
Time: 27.35153603553772 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000':

             58.57 msec task-clock:u                     #    0.002 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,259      page-faults:u                    #   55.640 K/sec                     
        74,509,373      cycles:u                         #    1.272 GHz                         (58.00%)
        88,672,751      instructions:u                   #    1.19  insn per cycle              (90.97%)
   <not supported>      branches:u                                                            
           361,568      branch-misses:u                                                       
        31,594,797      L1-dcache-loads:u                #  539.410 M/sec                     
           460,467      L1-dcache-load-misses:u          #    1.46% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        30,148,838      L1-icache-loads:u                #  514.724 M/sec                     
           282,768      L1-icache-load-misses:u          #    0.94% of all L1-icache accesses 
        19,757,856      dTLB-loads:u                     #  337.321 M/sec                       (11.69%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

      31.087250856 seconds time elapsed

     142.716222000 seconds user
    2102.420776000 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,  ..., 549200, 549200,
                            549202]),
       col_indices=tensor([    1,     2,     3,  ..., 82142, 81974, 82136]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(82144, 82144),
       nnz=549202, layout=torch.sparse_csr)
tensor([0.7637, 0.5328, 0.8286,  ..., 0.7084, 0.8903, 0.1707])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
Time: 17.188836336135864 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000':

           342,121      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,436,338      BR_RETIRED:u                                                          

      20.753346873 seconds time elapsed

      98.605331000 seconds user
    1332.291974000 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,  ..., 549200, 549200,
                            549202]),
       col_indices=tensor([    1,     2,     3,  ..., 82142, 81974, 82136]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(82144, 82144),
       nnz=549202, layout=torch.sparse_csr)
tensor([0.9017, 0.8505, 0.0023,  ..., 0.4182, 0.6895, 0.5023])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
Time: 16.22375249862671 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000':

        27,189,335      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,437      ITLB_WALK:u                                                           
            18,156      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,676,625      L1D_TLB:u                                                             

      19.748749363 seconds time elapsed

     103.049578000 seconds user
    1249.814927000 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,  ..., 549200, 549200,
                            549202]),
       col_indices=tensor([    1,     2,     3,  ..., 82142, 81974, 82136]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(82144, 82144),
       nnz=549202, layout=torch.sparse_csr)
tensor([0.4805, 0.2325, 0.2103,  ..., 0.1710, 0.7638, 0.9368])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
Time: 15.453373908996582 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000':

        30,721,032      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           302,777      L1I_CACHE_REFILL:u                                                    
           469,833      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        32,109,077      L1D_CACHE:u                                                           

      19.090250444 seconds time elapsed

      94.904880000 seconds user
    1195.102767000 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,  ..., 549200, 549200,
                            549202]),
       col_indices=tensor([    1,     2,     3,  ..., 82142, 81974, 82136]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(82144, 82144),
       nnz=549202, layout=torch.sparse_csr)
tensor([0.8430, 0.9439, 0.4260,  ..., 0.8172, 0.4243, 0.3834])
Matrix: soc-sign-Slashdot090221
Shape: torch.Size([82144, 82144])
NNZ: 549202
Density: 8.13917555860553e-05
Time: 29.316507816314697 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-Slashdot090221.mtx 1000':

           551,850      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           565,355      LL_CACHE_RD:u                                                         
           200,417      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            25,536      L2D_TLB_REFILL:u                                                      
           304,133      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,801,849      L2D_CACHE:u                                                           

      32.859276963 seconds time elapsed

     148.969816000 seconds user
    2252.321936000 seconds sys