169 lines
10 KiB
Plaintext
169 lines
10 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 3394146 queued and waiting for resources
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srun: job 3394146 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, 3, 9, ..., 572056, 572061,
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572066]),
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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
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114602]),
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values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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18651.]), size=(115406, 115406), nnz=572066,
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layout=torch.sparse_csr)
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tensor([0.4608, 0.1516, 0.8492, ..., 0.8920, 0.4275, 0.8070])
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Shape: torch.Size([115406, 115406])
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NNZ: 572066
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Density: 4.295259032005559e-05
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Time: 1.3751039505004883 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
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60.55 msec task-clock:u # 0.012 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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3,490 page-faults:u # 57.638 K/sec
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49,977,496 cycles:u # 0.825 GHz (40.93%)
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78,622,993 instructions:u # 1.57 insn per cycle (85.37%)
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<not supported> branches:u
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358,029 branch-misses:u
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31,478,500 L1-dcache-loads:u # 519.877 M/sec
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479,449 L1-dcache-load-misses:u # 1.52% 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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29,991,824 L1-icache-loads:u # 495.324 M/sec
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294,864 L1-icache-load-misses:u # 0.98% of all L1-icache accesses
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35,154,647 dTLB-loads:u # 580.589 M/sec (23.19%)
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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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4.986156121 seconds time elapsed
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23.724703000 seconds user
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145.034521000 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, 3, 9, ..., 572056, 572061,
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572066]),
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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
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114602]),
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values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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18651.]), size=(115406, 115406), nnz=572066,
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layout=torch.sparse_csr)
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tensor([0.4697, 0.7121, 0.5987, ..., 0.2619, 0.7308, 0.3129])
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Shape: torch.Size([115406, 115406])
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NNZ: 572066
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Density: 4.295259032005559e-05
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Time: 1.6881086826324463 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
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327,078 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,135,808 BR_RETIRED:u
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5.374156677 seconds time elapsed
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25.609168000 seconds user
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167.278028000 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, 3, 9, ..., 572056, 572061,
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572066]),
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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
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114602]),
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values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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18651.]), size=(115406, 115406), nnz=572066,
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layout=torch.sparse_csr)
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tensor([0.9215, 0.6706, 0.8015, ..., 0.8507, 0.8546, 0.4441])
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Shape: torch.Size([115406, 115406])
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NNZ: 572066
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Density: 4.295259032005559e-05
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Time: 1.2785694599151611 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
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27,608,093 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,616 ITLB_WALK:u
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17,185 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,866,957 L1D_TLB:u
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4.861513311 seconds time elapsed
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23.339077000 seconds user
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141.584760000 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, 3, 9, ..., 572056, 572061,
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572066]),
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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
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114602]),
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values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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18651.]), size=(115406, 115406), nnz=572066,
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layout=torch.sparse_csr)
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tensor([0.8973, 0.5228, 0.4492, ..., 0.7677, 0.7722, 0.1700])
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Shape: torch.Size([115406, 115406])
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NNZ: 572066
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Density: 4.295259032005559e-05
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Time: 1.1654376983642578 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
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32,639,204 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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309,643 L1I_CACHE_REFILL:u
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478,856 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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34,280,618 L1D_CACHE:u
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4.677973310 seconds time elapsed
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22.972655000 seconds user
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125.062401000 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, 3, 9, ..., 572056, 572061,
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572066]),
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col_indices=tensor([ 453, 1291, 1979, ..., 113521, 114509,
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114602]),
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values=tensor([160642., 31335., 282373., ..., 88393., 99485.,
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18651.]), size=(115406, 115406), nnz=572066,
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layout=torch.sparse_csr)
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tensor([0.4542, 0.7095, 0.5701, ..., 0.2172, 0.8829, 0.7757])
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Shape: torch.Size([115406, 115406])
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NNZ: 572066
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Density: 4.295259032005559e-05
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Time: 1.1153452396392822 seconds
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Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ut2010.mtx 100':
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555,275 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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578,455 LL_CACHE_RD:u
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188,723 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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24,635 L2D_TLB_REFILL:u
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319,663 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,799,940 L2D_CACHE:u
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4.655024760 seconds time elapsed
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23.104641000 seconds user
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122.294597000 seconds sys
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