ampere_research/pytorch/output/altra_10_30_ut2010_1000.output

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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    #
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srun: job 3394993 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,      3,      9,  ..., 572056, 572061,
                            572066]),
       col_indices=tensor([   453,   1291,   1979,  ..., 113521, 114509,
                           114602]),
       values=tensor([160642.,  31335., 282373.,  ...,  88393.,  99485.,
                       18651.]), size=(115406, 115406), nnz=572066,
       layout=torch.sparse_csr)
tensor([0.6983, 0.2845, 0.5984,  ..., 0.1182, 0.9468, 0.3161])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 8.604448795318604 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 1000':

             52.22 msec task-clock:u                     #    0.004 CPUs utilized             
                 0      context-switches:u               #    0.000 /sec                      
                 0      cpu-migrations:u                 #    0.000 /sec                      
             3,288      page-faults:u                    #   62.965 K/sec                     
        67,463,873      cycles:u                         #    1.292 GHz                         (52.95%)
        73,042,754      instructions:u                   #    1.08  insn per cycle              (71.78%)
   <not supported>      branches:u                                                            
           376,297      branch-misses:u                                                         (87.57%)
        34,189,906      L1-dcache-loads:u                #  654.731 M/sec                       (97.72%)
           471,636      L1-dcache-load-misses:u          #    1.38% of all L1-dcache accesses 
   <not supported>      LLC-loads:u                                                           
   <not supported>      LLC-load-misses:u                                                     
        31,870,328      L1-icache-loads:u                #  610.312 M/sec                     
           297,680      L1-icache-load-misses:u          #    0.93% of all L1-icache accesses 
        57,623,823      dTLB-loads:u                     #    1.103 G/sec                       (30.16%)
            75,454      dTLB-load-misses:u               #    0.13% of all dTLB cache accesses  (24.31%)
                 0      iTLB-loads:u                     #    0.000 /sec                        (3.96%)
     <not counted>      iTLB-load-misses:u                                                      (0.00%)

      12.112100803 seconds time elapsed

      66.253313000 seconds user
     675.855469000 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,      9,  ..., 572056, 572061,
                            572066]),
       col_indices=tensor([   453,   1291,   1979,  ..., 113521, 114509,
                           114602]),
       values=tensor([160642.,  31335., 282373.,  ...,  88393.,  99485.,
                       18651.]), size=(115406, 115406), nnz=572066,
       layout=torch.sparse_csr)
tensor([0.0260, 0.8569, 0.4315,  ..., 0.5243, 0.8018, 0.1763])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 8.702903270721436 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 1000':

           344,635      BR_MIS_PRED_RETIRED:u            #      0.0 per branch  branch_misprediction_ratio
        20,775,821      BR_RETIRED:u                                                          

      12.383096073 seconds time elapsed

      64.544546000 seconds user
     688.477174000 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,      9,  ..., 572056, 572061,
                            572066]),
       col_indices=tensor([   453,   1291,   1979,  ..., 113521, 114509,
                           114602]),
       values=tensor([160642.,  31335., 282373.,  ...,  88393.,  99485.,
                       18651.]), size=(115406, 115406), nnz=572066,
       layout=torch.sparse_csr)
tensor([0.7940, 0.1585, 0.6879,  ..., 0.4017, 0.1738, 0.9713])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 7.38647985458374 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 1000':

        27,488,750      L1I_TLB:u                        #      0.0 per TLB access  itlb_walk_ratio
             6,494      ITLB_WALK:u                                                           
            18,293      DTLB_WALK:u                      #      0.0 per TLB access  dtlb_walk_ratio
        36,697,113      L1D_TLB:u                                                             

      10.936742446 seconds time elapsed

      63.993242000 seconds user
     580.515047000 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,      9,  ..., 572056, 572061,
                            572066]),
       col_indices=tensor([   453,   1291,   1979,  ..., 113521, 114509,
                           114602]),
       values=tensor([160642.,  31335., 282373.,  ...,  88393.,  99485.,
                       18651.]), size=(115406, 115406), nnz=572066,
       layout=torch.sparse_csr)
tensor([0.2725, 0.6578, 0.8180,  ..., 0.0148, 0.5094, 0.1155])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 12.719107389450073 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 1000':

        31,066,176      L1I_CACHE:u                      #      0.0 per cache access  l1i_cache_miss_ratio
           298,652      L1I_CACHE_REFILL:u                                                    
           473,808      L1D_CACHE_REFILL:u               #      0.0 per cache access  l1d_cache_miss_ratio
        32,572,985      L1D_CACHE:u                                                           

      16.299576479 seconds time elapsed

      86.072431000 seconds user
     987.199923000 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,      9,  ..., 572056, 572061,
                            572066]),
       col_indices=tensor([   453,   1291,   1979,  ..., 113521, 114509,
                           114602]),
       values=tensor([160642.,  31335., 282373.,  ...,  88393.,  99485.,
                       18651.]), size=(115406, 115406), nnz=572066,
       layout=torch.sparse_csr)
tensor([0.1156, 0.5715, 0.3099,  ..., 0.3964, 0.9672, 0.5694])
Matrix: ut2010
Shape: torch.Size([115406, 115406])
NNZ: 572066
Density: 4.295259032005559e-05
Time: 12.682909727096558 seconds

 Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 1000':

           547,428      LL_CACHE_MISS_RD:u               #      1.0 per cache access  ll_cache_read_miss_ratio
           566,356      LL_CACHE_RD:u                                                         
           162,858      L2D_TLB:u                        #      0.1 per TLB access  l2_tlb_miss_ratio
            19,852      L2D_TLB_REFILL:u                                                      
           304,056      L2D_CACHE_REFILL:u               #      0.2 per cache access  l2_cache_miss_ratio
         1,713,420      L2D_CACHE:u                                                           

      16.221517033 seconds time elapsed

      79.927661000 seconds user
     988.333919000 seconds sys