164 lines
9.6 KiB
Plaintext
164 lines
9.6 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 3394983 queued and waiting for resources
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srun: job 3394983 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, 0, 0, ..., 106761, 106761,
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106762]),
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col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
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nnz=106762, layout=torch.sparse_csr)
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tensor([0.4886, 0.3652, 0.5691, ..., 0.6466, 0.4355, 0.8397])
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Matrix: as-caida
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Shape: torch.Size([31379, 31379])
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NNZ: 106762
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Density: 0.00010842726485909405
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Time: 2.6297245025634766 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
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61.40 msec task-clock:u # 0.010 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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3,507 page-faults:u # 57.117 K/sec
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78,967,021 cycles:u # 1.286 GHz (61.13%)
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94,334,531 instructions:u # 1.19 insn per cycle (95.16%)
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<not supported> branches:u
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365,239 branch-misses:u
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33,334,312 L1-dcache-loads:u # 542.906 M/sec
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457,950 L1-dcache-load-misses:u # 1.37% 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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31,725,851 L1-icache-loads:u # 516.709 M/sec
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297,720 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
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25,188,580 dTLB-loads:u # 410.239 M/sec (5.16%)
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<not counted> dTLB-load-misses:u (0.00%)
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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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6.049042045 seconds time elapsed
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17.649315000 seconds user
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29.335859000 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, 0, 0, ..., 106761, 106761,
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106762]),
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col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
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nnz=106762, layout=torch.sparse_csr)
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tensor([0.8344, 0.2588, 0.2246, ..., 0.5607, 0.8141, 0.9893])
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Matrix: as-caida
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Shape: torch.Size([31379, 31379])
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NNZ: 106762
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Density: 0.00010842726485909405
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Time: 2.6495532989501953 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
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325,893 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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19,069,753 BR_RETIRED:u
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6.023780447 seconds time elapsed
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17.654658000 seconds user
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28.848805000 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, 0, 0, ..., 106761, 106761,
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106762]),
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col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
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nnz=106762, layout=torch.sparse_csr)
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tensor([0.0814, 0.1132, 0.8515, ..., 0.8987, 0.5912, 0.5002])
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Matrix: as-caida
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Shape: torch.Size([31379, 31379])
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NNZ: 106762
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Density: 0.00010842726485909405
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Time: 2.5444185733795166 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
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27,181,279 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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5,995 ITLB_WALK:u
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17,412 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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37,016,930 L1D_TLB:u
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5.790360666 seconds time elapsed
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17.919315000 seconds user
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30.569858000 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, 0, 0, ..., 106761, 106761,
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106762]),
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col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
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nnz=106762, layout=torch.sparse_csr)
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tensor([0.0439, 0.1884, 0.3342, ..., 0.2027, 0.5532, 0.7245])
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Matrix: as-caida
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Shape: torch.Size([31379, 31379])
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NNZ: 106762
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Density: 0.00010842726485909405
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Time: 2.620804786682129 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
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31,535,482 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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292,676 L1I_CACHE_REFILL:u
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471,752 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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33,119,145 L1D_CACHE:u
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6.002311801 seconds time elapsed
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17.427887000 seconds user
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30.063688000 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, 0, 0, ..., 106761, 106761,
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106762]),
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col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
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values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
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nnz=106762, layout=torch.sparse_csr)
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tensor([0.1495, 0.5856, 0.8600, ..., 0.2101, 0.6229, 0.2019])
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Matrix: as-caida
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Shape: torch.Size([31379, 31379])
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NNZ: 106762
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Density: 0.00010842726485909405
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Time: 2.561279296875 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
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540,894 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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554,700 LL_CACHE_RD:u
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191,772 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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23,711 L2D_TLB_REFILL:u
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306,195 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,755,986 L2D_CACHE:u
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5.946428572 seconds time elapsed
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17.396567000 seconds user
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32.141235000 seconds sys
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