ampere_research/pytorch/output/altra_10_30_as-caida_1000.output
2024-12-03 08:53:39 -05:00

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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 3394983 queued and waiting for resources
srun: job 3394983 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, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.4886, 0.3652, 0.5691, ..., 0.6466, 0.4355, 0.8397])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 2.6297245025634766 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
61.40 msec task-clock:u # 0.010 CPUs utilized
0 context-switches:u # 0.000 /sec
0 cpu-migrations:u # 0.000 /sec
3,507 page-faults:u # 57.117 K/sec
78,967,021 cycles:u # 1.286 GHz (61.13%)
94,334,531 instructions:u # 1.19 insn per cycle (95.16%)
<not supported> branches:u
365,239 branch-misses:u
33,334,312 L1-dcache-loads:u # 542.906 M/sec
457,950 L1-dcache-load-misses:u # 1.37% of all L1-dcache accesses
<not supported> LLC-loads:u
<not supported> LLC-load-misses:u
31,725,851 L1-icache-loads:u # 516.709 M/sec
297,720 L1-icache-load-misses:u # 0.94% of all L1-icache accesses
25,188,580 dTLB-loads:u # 410.239 M/sec (5.16%)
<not counted> dTLB-load-misses:u (0.00%)
<not counted> iTLB-loads:u (0.00%)
<not counted> iTLB-load-misses:u (0.00%)
6.049042045 seconds time elapsed
17.649315000 seconds user
29.335859000 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, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.8344, 0.2588, 0.2246, ..., 0.5607, 0.8141, 0.9893])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 2.6495532989501953 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
325,893 BR_MIS_PRED_RETIRED:u # 0.0 per branch branch_misprediction_ratio
19,069,753 BR_RETIRED:u
6.023780447 seconds time elapsed
17.654658000 seconds user
28.848805000 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, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.0814, 0.1132, 0.8515, ..., 0.8987, 0.5912, 0.5002])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 2.5444185733795166 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
27,181,279 L1I_TLB:u # 0.0 per TLB access itlb_walk_ratio
5,995 ITLB_WALK:u
17,412 DTLB_WALK:u # 0.0 per TLB access dtlb_walk_ratio
37,016,930 L1D_TLB:u
5.790360666 seconds time elapsed
17.919315000 seconds user
30.569858000 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, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.0439, 0.1884, 0.3342, ..., 0.2027, 0.5532, 0.7245])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 2.620804786682129 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
31,535,482 L1I_CACHE:u # 0.0 per cache access l1i_cache_miss_ratio
292,676 L1I_CACHE_REFILL:u
471,752 L1D_CACHE_REFILL:u # 0.0 per cache access l1d_cache_miss_ratio
33,119,145 L1D_CACHE:u
6.002311801 seconds time elapsed
17.427887000 seconds user
30.063688000 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, 0, 0, ..., 106761, 106761,
106762]),
col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]),
values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379),
nnz=106762, layout=torch.sparse_csr)
tensor([0.1495, 0.5856, 0.8600, ..., 0.2101, 0.6229, 0.2019])
Matrix: as-caida
Shape: torch.Size([31379, 31379])
NNZ: 106762
Density: 0.00010842726485909405
Time: 2.561279296875 seconds
Performance counter stats for 'apptainer run pytorch-altra.sif -c numactl --cpunodebind=0 --membind=0 python spmv.py matrices/as-caida.mtx 1000':
540,894 LL_CACHE_MISS_RD:u # 1.0 per cache access ll_cache_read_miss_ratio
554,700 LL_CACHE_RD:u
191,772 L2D_TLB:u # 0.1 per TLB access l2_tlb_miss_ratio
23,711 L2D_TLB_REFILL:u
306,195 L2D_CACHE_REFILL:u # 0.2 per cache access l2_cache_miss_ratio
1,755,986 L2D_CACHE:u
5.946428572 seconds time elapsed
17.396567000 seconds user
32.141235000 seconds sys