ampere_research/pytorch/output/altra_10_30_email-Enron_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 3394986 queued and waiting for resources
srun: job 3394986 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,      1,     71,  ..., 367660, 367661,
                            367662]),
       col_indices=tensor([    1,     0,     2,  ..., 36690, 36689,  8203]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36692, 36692),
       nnz=367662, layout=torch.sparse_csr)
tensor([0.9906, 0.9401, 0.5661,  ..., 0.4491, 0.7550, 0.2452])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 12.80848503112793 seconds

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

             48.76 msec task-clock:u                     #    0.003 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,281      page-faults:u                    #   67.289 K/sec                     
        45,495,589      cycles:u                         #    0.933 GHz                         (57.79%)
        79,104,832      instructions:u                   #    1.74  insn per cycle              (81.70%)
   <not supported>      branches:u                                                            
           372,161      branch-misses:u                                                       
        32,089,348      L1-dcache-loads:u                #  658.113 M/sec                     
           467,576      L1-dcache-load-misses:u          #    1.46% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        30,688,995      L1-icache-loads:u                #  629.393 M/sec                     
           289,698      L1-icache-load-misses:u          #    0.94% of all L1-icache accesses 
        47,006,355      dTLB-loads:u                     #  964.042 M/sec                       (22.12%)
     <not counted>      dTLB-load-misses:u                                                      (0.00%)
     <not counted>      iTLB-loads:u                                                            (0.00%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

      16.331438990 seconds time elapsed

      76.869141000 seconds user
     999.179638000 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,      1,     71,  ..., 367660, 367661,
                            367662]),
       col_indices=tensor([    1,     0,     2,  ..., 36690, 36689,  8203]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36692, 36692),
       nnz=367662, layout=torch.sparse_csr)
tensor([0.7565, 0.5273, 0.1038,  ..., 0.9432, 0.1309, 0.5542])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 26.91536283493042 seconds

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

           335,574      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,121,415      BR_RETIRED:u                                                          

      30.559245388 seconds time elapsed

     126.799314000 seconds user
    2081.777635000 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,      1,     71,  ..., 367660, 367661,
                            367662]),
       col_indices=tensor([    1,     0,     2,  ..., 36690, 36689,  8203]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36692, 36692),
       nnz=367662, layout=torch.sparse_csr)
tensor([0.2321, 0.0702, 0.2538,  ..., 0.6254, 0.6308, 0.5317])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 14.841739892959595 seconds

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

        26,011,880      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             5,842      ITLB_WALK:u                                                           
            16,448      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        35,000,292      L1D_TLB:u                                                             

      18.443612527 seconds time elapsed

      80.694133000 seconds user
    1159.740575000 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,      1,     71,  ..., 367660, 367661,
                            367662]),
       col_indices=tensor([    1,     0,     2,  ..., 36690, 36689,  8203]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36692, 36692),
       nnz=367662, layout=torch.sparse_csr)
tensor([0.7091, 0.9447, 0.0959,  ..., 0.0090, 0.7012, 0.6025])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 10.863199234008789 seconds

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

        32,193,112      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           310,304      L1I_CACHE_REFILL:u                                                    
           495,806      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        33,829,187      L1D_CACHE:u                                                           

      14.426841778 seconds time elapsed

      70.728541000 seconds user
     853.184507000 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,      1,     71,  ..., 367660, 367661,
                            367662]),
       col_indices=tensor([    1,     0,     2,  ..., 36690, 36689,  8203]),
       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(36692, 36692),
       nnz=367662, layout=torch.sparse_csr)
tensor([0.8267, 0.6185, 0.8015,  ..., 0.8593, 0.4881, 0.8599])
Matrix: email-Enron
Shape: torch.Size([36692, 36692])
NNZ: 367662
Density: 0.0002730901120626302
Time: 12.076026678085327 seconds

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

           546,628      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           570,044      LL_CACHE_RD:u                                                         
           196,794      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            24,071      L2D_TLB_REFILL:u                                                      
           316,028      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,836,018      L2D_CACHE:u                                                           

      15.581045199 seconds time elapsed

      77.345591000 seconds user
     942.987439000 seconds sys