164 lines
9.8 KiB
Plaintext
164 lines
9.8 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 3394154 queued and waiting for resources
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srun: job 3394154 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([0.5842, 0.3042, 0.7358, ..., 0.7882, 0.7596, 0.5895])
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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: 2.4407293796539307 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 100':
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49.87 msec task-clock:u # 0.008 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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3,300 page-faults:u # 66.174 K/sec
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51,935,476 cycles:u # 1.041 GHz (65.00%)
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83,731,856 instructions:u # 1.61 insn per cycle (84.25%)
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<not supported> branches:u
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375,900 branch-misses:u
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34,169,837 L1-dcache-loads:u # 685.197 M/sec
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474,410 L1-dcache-load-misses:u # 1.39% 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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32,443,215 L1-icache-loads:u # 650.574 M/sec
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294,146 L1-icache-load-misses:u # 0.91% of all L1-icache accesses
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63,709,518 dTLB-loads:u # 1.278 G/sec (16.44%)
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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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6.058862056 seconds time elapsed
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29.101578000 seconds user
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224.790489000 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([0.9696, 0.8139, 0.4858, ..., 0.2374, 0.1716, 0.9756])
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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: 2.0945546627044678 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 100':
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326,464 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
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20,341,367 BR_RETIRED:u
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5.525378890 seconds time elapsed
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28.841740000 seconds user
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199.678982000 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([0.3478, 0.0057, 0.8574, ..., 0.6409, 0.1876, 0.8429])
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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: 2.8504912853240967 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 100':
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27,590,154 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
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6,210 ITLB_WALK:u
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17,536 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
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36,763,243 L1D_TLB:u
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6.425887143 seconds time elapsed
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33.069094000 seconds user
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256.667850000 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([0.5381, 0.6651, 0.4689, ..., 0.7251, 0.3759, 0.8516])
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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: 1.6941111087799072 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 100':
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31,663,300 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
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289,727 L1I_CACHE_REFILL:u
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462,864 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
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33,262,254 L1D_CACHE:u
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5.304170809 seconds time elapsed
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25.992245000 seconds user
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173.752913000 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([0.4145, 0.8515, 0.7222, ..., 0.1386, 0.6641, 0.6662])
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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: 3.0850296020507812 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 100':
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530,272 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
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551,373 LL_CACHE_RD:u
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196,152 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
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23,542 L2D_TLB_REFILL:u
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301,998 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
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1,732,662 L2D_CACHE:u
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6.733517838 seconds time elapsed
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34.030476000 seconds user
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271.397968000 seconds sys
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