enable OpenMP for prod_force and prod_virial
#1360
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About 1 ms can be saved in each training step.
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@@ Coverage Diff @@
## devel #1360 +/- ##
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- Coverage 75.53% 74.32% -1.22%
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Files 91 91
Lines 7506 7482 -24
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- Hits 5670 5561 -109
- Misses 1836 1921 +85
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| @@ -36,14 +36,17 @@ prod_force_a_cpu( | |||
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| memset(force, 0.0, sizeof(FPTYPE) * nall * 3); | |||
| // compute force of a frame | |||
| #pragma omp parallel | |||
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Two threads with different i_idx may write on the same force force[j_idx * 3 + xxx], which gives unpredictable result.
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I realized that, so that pragma omp for is inside this loop and before another loop. The current version can pass the tests.
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OK I understand you compute the neighbors of the same atom using multi-threading.
What I do not understand is why you need omp parallel to generate threads here, but not at L49 where you really need multi-threading
wanghan-iapcm
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Dec 17, 2021
denghuilu
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Dec 17, 2021
njzjz
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Aug 17, 2022
Sometimes when box is quite small (i.e. box size < 2 * rcut), the same atom may repeat to appear in the neighbor list. This cause inaccurate results when using OMP.
wanghan-iapcm
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Aug 19, 2022
* revert prod_force OMP in #1360 Sometimes when box is quite small (i.e. box size < 2 * rcut), the same atom may repeat to appear in the neighbor list. This cause inaccurate results when using OMP. * do not update pip Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu> * revert pining pip; setting env for setuptools>=64 Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu> Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
mingzhong15
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Jan 15, 2023
* revert prod_force OMP in deepmodeling#1360 Sometimes when box is quite small (i.e. box size < 2 * rcut), the same atom may repeat to appear in the neighbor list. This cause inaccurate results when using OMP. * do not update pip Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu> * revert pining pip; setting env for setuptools>=64 Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu> Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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About 1 ms can be saved in each training step.