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ampere_research/pytorch/output/altra_2_2_ri2010_100.output

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Got output!
2024-12-03 00:20:09 -05:00
srun: Job time limit was unset; set to partition default of 60 minutes
srun: ################################################################################
srun: # Please note that the oasis compute nodes have aarch64 architecture CPUs. #
srun: # All submission nodes and all other compute nodes have x86_64 architecture #
srun: # CPUs. Programs, environments, or other software that was built on x86_64 #
srun: # nodes may need to be rebuilt to properly execute on these nodes. #
srun: ################################################################################
srun: job 3394145 queued and waiting for resources
srun: job 3394145 has been allocated resources
/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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.1402, 0.0708, 0.4576, ..., 0.4700, 0.5629, 0.9120])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3585643768310547 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
60.77 msec task-clock:u # 0.016 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,361 page-faults:u # 55.311 K/sec
63,493,475 cycles:u # 1.045 GHz (49.59%)
91,578,911 instructions:u # 1.44 insn per cycle (92.22%)
<not supported> branches:u
374,941 branch-misses:u
33,905,978 L1-dcache-loads:u # 557.979 M/sec
470,553 L1-dcache-load-misses:u # 1.39% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
32,247,376 L1-icache-loads:u # 530.684 M/sec
299,037 L1-icache-load-misses:u # 0.93% of all L1-icache accesses
27,428,635 dTLB-loads:u # 451.384 M/sec (13.50%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
3.818532962 seconds time elapsed
15.563570000 seconds user
30.194882000 seconds sys
/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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.1841, 0.4436, 0.8281, ..., 0.0546, 0.5967, 0.9496])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3050577640533447 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
329,084 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
20,406,595 BR_RETIRED:u
3.673527837 seconds time elapsed
15.520198000 seconds user
29.068211000 seconds sys
/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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.1849, 0.5991, 0.5040, ..., 0.4916, 0.4789, 0.8887])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3605458736419678 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
26,859,919 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
6,237 ITLB_WALK:u
16,689 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
36,348,977 L1D_TLB:u
3.769690988 seconds time elapsed
15.173839000 seconds user
29.963392000 seconds sys
/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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.0513, 0.4498, 0.6748, ..., 0.2114, 0.6847, 0.2188])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3485410213470459 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
30,979,764 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
292,038 L1I_CACHE_REFILL:u
469,219 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
32,411,890 L1D_CACHE:u
3.598754329 seconds time elapsed
16.139631000 seconds user
29.287026000 seconds sys
/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.)
).to_sparse_csr().type(torch.float)
tensor(crow_indices=tensor([ 0, 3, 8, ..., 125742, 125747,
125750]),
col_indices=tensor([ 25, 56, 662, ..., 21738, 22279, 23882]),
values=tensor([17171., 37318., 5284., ..., 25993., 24918., 803.]),
size=(25181, 25181), nnz=125750, layout=torch.sparse_csr)
tensor([0.7270, 0.7858, 0.3165, ..., 0.7139, 0.8270, 0.9478])
Shape: torch.Size([25181, 25181])
NNZ: 125750
Density: 0.00019831796057928155
Time: 0.3687746524810791 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/ri2010.mtx 100':
571,870 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
598,306 LL_CACHE_RD:u
205,488 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
26,392 L2D_TLB_REFILL:u
342,141 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,857,697 L2D_CACHE:u
3.726794738 seconds time elapsed
15.231331000 seconds user
32.108693000 seconds sys
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