Implementation of Monte Carlo Load Balancing Based on Nearest  Neighbor Algorithm

Cui Xiantao; Qiang Shenglong; Kuang Denghui; Yin Qiang; Zhang Wenxin; Liu Yuan; Wu Bin

doi:10.13832/j.jnpe.2021.S2.0037

Volume 42 Issue S2

Dec. 2021

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Cui Xiantao, Qiang Shenglong, Kuang Denghui, Yin Qiang, Zhang Wenxin, Liu Yuan, Wu Bin. Implementation of Monte Carlo Load Balancing Based on Nearest Neighbor Algorithm[J]. Nuclear Power Engineering, 2021, 42(S2): 37-40. doi: 10.13832/j.jnpe.2021.S2.0037

Citation:

Cui Xiantao, Qiang Shenglong, Kuang Denghui, Yin Qiang, Zhang Wenxin, Liu Yuan, Wu Bin. Implementation of Monte Carlo Load Balancing Based on Nearest Neighbor Algorithm[J]. Nuclear Power Engineering, 2021, 42(S2): 37-40. doi: 10.13832/j.jnpe.2021.S2.0037

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Implementation of Monte Carlo Load Balancing Based on Nearest Neighbor Algorithm

doi: 10.13832/j.jnpe.2021.S2.0037

1.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China
2.
National University of Defense Technology, Changsha, 410073, China

Received Date: 2021-07-19
Accepted Date: 2021-12-06
Rev Recd Date: 2021-11-13
Publish Date: 2021-12-29

Abstract

Abstract

In order to solve the problem of low parallelism caused by particle load imbalance of Monte Carlo program at the end of iteration, this paper analyzes the advantages and disadvantages of different algorithms, and the nearest neighbor algorithm is used on MOI Monte Carlo program to solve load balancing. Finally, we adapt an example of ten million grid to test this algorithm, it reduces the computation time by at least 10%, and as the burnup deepens, the calculation time will decrease even further. The results show that the algorithm is effective for the problem of particle load imbalance.
- Nearest neighbor algorithm,
- Load imbalance,
- MOI Monte Carlo program,
- Parallel

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References(4)

References

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