159 lines
9.7 KiB
Plaintext
159 lines
9.7 KiB
Plaintext
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srun: Job time limit was unset; set to partition default of 60 minutes
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srun: ################################################################################
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srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
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srun: # All submission nodes and all other compute nodes have x86_64 architecture #
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srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
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srun: # nodes may need to be rebuilt to properly execute on these nodes. #
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srun: ################################################################################
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srun: job 3394143 queued and waiting for resources
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srun: job 3394143 has been allocated 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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.9170, 0.7306, 0.1175, ..., 0.0616, 0.0147, 0.6403])
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 0.4440653324127197 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
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61.63 msec task-clock:u # 0.016 CPUs utilized
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0 context-switches:u # 0.000 /sec
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0 cpu-migrations:u # 0.000 /sec
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3,304 page-faults:u # 53.611 K/sec
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64,734,203 cycles:u # 1.050 GHz (50.46%)
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53,597,991 instructions:u # 0.83 insn per cycle (70.10%)
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<not supported> branches:u
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347,389 branch-misses:u (91.95%)
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31,363,842 L1-dcache-loads:u # 508.915 M/sec
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482,780 L1-dcache-load-misses:u # 1.54% of all L1-dcache accesses
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<not supported> LLC-loads:u
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<not supported> LLC-load-misses:u
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30,027,001 L1-icache-loads:u # 487.223 M/sec
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288,023 L1-icache-load-misses:u # 0.96% of all L1-icache accesses
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44,333,825 dTLB-loads:u # 719.368 M/sec (48.58%)
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74,525 dTLB-load-misses:u # 0.17% of all dTLB cache accesses (16.71%)
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<not counted> iTLB-loads:u (0.00%)
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<not counted> iTLB-load-misses:u (0.00%)
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3.811654040 seconds time elapsed
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15.616953000 seconds user
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30.906234000 seconds sys
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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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.5548, 0.3514, 0.6283, ..., 0.5672, 0.1575, 0.4493])
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 0.44233155250549316 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
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330,777 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,357,034 BR_RETIRED:u
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3.835342404 seconds time elapsed
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15.497637000 seconds user
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28.676763000 seconds sys
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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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.0953, 0.5790, 0.0112, ..., 0.9540, 0.3173, 0.4731])
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 0.43302106857299805 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
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27,381,387 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,248 ITLB_WALK:u
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17,636 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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37,436,110 L1D_TLB:u
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3.828586094 seconds time elapsed
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15.518057000 seconds user
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31.389361000 seconds sys
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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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.5456, 0.8708, 0.2037, ..., 0.8669, 0.9122, 0.2046])
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 0.4426534175872803 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
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32,505,993 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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303,849 L1I_CACHE_REFILL:u
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467,426 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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34,241,110 L1D_CACHE:u
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3.811299200 seconds time elapsed
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15.932195000 seconds user
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30.887870000 seconds sys
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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.)
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).to_sparse_csr().type(torch.float)
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tensor(crow_indices=tensor([ 0, 4, 7, ..., 155588, 155592,
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155598]),
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col_indices=tensor([ 131, 561, 996, ..., 32237, 32238, 32570]),
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values=tensor([79040., 7820., 15136., ..., 2828., 17986., 2482.]),
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size=(32580, 32580), nnz=155598, layout=torch.sparse_csr)
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tensor([0.5024, 0.2304, 0.7925, ..., 0.1397, 0.5558, 0.6450])
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Shape: torch.Size([32580, 32580])
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NNZ: 155598
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Density: 0.00014658915806621921
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Time: 0.3671383857727051 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/vt2010.mtx 100':
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550,075 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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562,829 LL_CACHE_RD:u
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199,285 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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24,424 L2D_TLB_REFILL:u
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310,155 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,783,824 L2D_CACHE:u
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3.824434783 seconds time elapsed
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15.754438000 seconds user
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28.226523000 seconds sys
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