DALB-MC: dynamic Adaptive load balancing for efficient Monte Carlo particle transport on heterogeneous architectures

This paper proposes a Dynamic Adaptive Load Balancing Algorithm for Monte Carlo Particle Transport (DALB-MC algorithm) to enhance computational performance of Monte Carlo codes, particularly in heterogeneous environments. While Monte Carlo codes have adopted local thread parallelism and global paral...

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Bibliographic Details
Published in:Journal of nuclear science and technology Vol. 62; no. 11; pp. 1100 - 1111
Main Authors: Wu, Jiajun, Zhang, Hui, Hu, Ankang, Pu, Yanheng, Wu, Zhen, Qiu, Rui, Li, Junli
Format: Journal Article
Language:English
Published: Tokyo Taylor & Francis 02.11.2025
Taylor & Francis Ltd
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ISSN:0022-3131, 1881-1248
Online Access:Get full text
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Summary:This paper proposes a Dynamic Adaptive Load Balancing Algorithm for Monte Carlo Particle Transport (DALB-MC algorithm) to enhance computational performance of Monte Carlo codes, particularly in heterogeneous environments. While Monte Carlo codes have adopted local thread parallelism and global parallelism through message passing between nodes, traditional parallel algorithms face challenges in adapting to diverse hardware resources like tailing effect and long particle history in weight-window method. The proposed DALB-MC algorithm addresses this by dynamically partitioning tasks into sub-tasks and allocating them in real time based on each node's computational capacity, ensuring optimized resource utilization and reduced simulation time. Implemented on MCShield, the algorithm achieves approximately 20% computation time reduction in massively parallel simulations and significantly improves overall efficiency. To verify its effectiveness, self-defined arithmetic examples confirm the correctness of the algorithm, and additional tests on a Linux supercomputing platform with a complex benchmark case demonstrate its scalability and performance optimization under real-world engineering applications. The results highlight DALB-MC algorithm's capability to adaptively balance workloads, making it highly suitable for modern Monte Carlo particle transport simulations in diverse computational environments.
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ISSN:0022-3131
1881-1248
DOI:10.1080/00223131.2025.2526784