169 lines
9.9 KiB
Plaintext
169 lines
9.9 KiB
Plaintext
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 3394990 queued and waiting for resources
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srun: job 3394990 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, 1, 2, ..., 841371, 841371,
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841372]),
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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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7714]),
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
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tensor([0.3914, 0.2076, 0.6733, ..., 0.4758, 0.6360, 0.6316])
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Matrix: soc-sign-epinions
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Shape: torch.Size([131828, 131828])
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NNZ: 841372
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Density: 4.841419648464106e-05
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Time: 20.04187798500061 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 1000':
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63.90 msec task-clock:u # 0.003 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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3,446 page-faults:u # 53.927 K/sec
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55,931,043 cycles:u # 0.875 GHz (85.43%)
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77,907,356 instructions:u # 1.39 insn per cycle
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<not supported> branches:u
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357,739 branch-misses:u
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33,000,188 L1-dcache-loads:u # 516.421 M/sec
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466,824 L1-dcache-load-misses:u # 1.41% 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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31,503,048 L1-icache-loads:u # 492.992 M/sec
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301,112 L1-icache-load-misses:u # 0.96% of all L1-icache accesses
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34,740,872 dTLB-loads:u # 543.661 M/sec (18.37%)
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32,355 dTLB-load-misses:u # 0.09% of all dTLB cache accesses (12.00%)
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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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23.478083368 seconds time elapsed
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119.232326000 seconds user
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1541.081607000 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, 1, 2, ..., 841371, 841371,
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841372]),
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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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7714]),
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
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tensor([0.3970, 0.5643, 0.0036, ..., 0.0338, 0.0807, 0.3885])
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Matrix: soc-sign-epinions
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Shape: torch.Size([131828, 131828])
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NNZ: 841372
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Density: 4.841419648464106e-05
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Time: 16.115705490112305 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 1000':
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332,778 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,000,746 BR_RETIRED:u
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19.765627973 seconds time elapsed
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103.591961000 seconds user
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1250.845091000 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, 1, 2, ..., 841371, 841371,
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841372]),
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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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7714]),
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
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tensor([0.0049, 0.4550, 0.3166, ..., 0.3734, 0.8337, 0.5156])
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Matrix: soc-sign-epinions
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Shape: torch.Size([131828, 131828])
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NNZ: 841372
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Density: 4.841419648464106e-05
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Time: 18.55180263519287 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 1000':
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27,000,304 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,713 ITLB_WALK:u
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18,689 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,395,663 L1D_TLB:u
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22.333459337 seconds time elapsed
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109.075160000 seconds user
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1441.055730000 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, 1, 2, ..., 841371, 841371,
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841372]),
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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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7714]),
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
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tensor([0.0560, 0.8530, 0.8946, ..., 0.4591, 0.5391, 0.2898])
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Matrix: soc-sign-epinions
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Shape: torch.Size([131828, 131828])
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NNZ: 841372
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Density: 4.841419648464106e-05
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Time: 25.587534427642822 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 1000':
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32,396,405 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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292,629 L1I_CACHE_REFILL:u
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473,799 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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34,061,981 L1D_CACHE:u
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29.367381835 seconds time elapsed
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142.233743000 seconds user
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1962.747683000 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, 1, 2, ..., 841371, 841371,
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841372]),
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col_indices=tensor([ 1, 128552, 3, ..., 131824, 131826,
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7714]),
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values=tensor([-1., -1., 1., ..., 1., 1., 1.]),
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size=(131828, 131828), nnz=841372, layout=torch.sparse_csr)
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tensor([0.7002, 0.7829, 0.1511, ..., 0.3651, 0.2391, 0.7788])
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Matrix: soc-sign-epinions
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Shape: torch.Size([131828, 131828])
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NNZ: 841372
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Density: 4.841419648464106e-05
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Time: 23.656178951263428 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/soc-sign-epinions.mtx 1000':
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542,765 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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557,193 LL_CACHE_RD:u
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203,626 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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24,363 L2D_TLB_REFILL:u
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303,397 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,772,084 L2D_CACHE:u
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27.453055481 seconds time elapsed
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128.709934000 seconds user
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1831.887905000 seconds sys
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