Abstract
The Random Ray Method (TRRM) is a new approach to solving partial differential equations (PDEs) based on the method of characteristics (MOC). It employs stochastic rather than deterministic discretization of characteristic tracks and can be used for the numerical simulation of nuclear reactors. In this paper, we propose SW-TRRM, a parallel optimization program for TRRM based on the Sunway Bluelight II Supercomputer for the first time. We present a two-level parallelization scheme that consists of thread-level and process-level optimization. At the thread-level, we introduce three schemes for speeding up within a single core group, including direct parallelization, parallelization by energy groups, and loop structure optimization. At the process-level, we implement task parallelization among multiple processes using domain replication. Moreover, we devise an algorithm to optimize the MPI collective communication across super-nodes. Experimental results show that SW-TRRM achieves a 17.40\(\times \) speedup within a single core group compared to the original TRRM program. When scaled up to 2,048 processes and 133,120 cores, SW-TRRM maintains good strong and weak scalability.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant 62002186, 12105150, and 2021 Shandong Youth Innovation Talent Introduction and Education Plan (Parallel Computing Industrial Software Innovation Team Based on Chinese supercomputer), and TaiShan Scholars Program NO. tsqnz20221148, and the unveiling project of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2022JBZ01-01.
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Ren, Z. et al. (2024). SW-TRRM: Parallel Optimization Research of the Random Ray Method Based on Sunway Bluelight II Supercomputer. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14491. Springer, Singapore. https://doi.org/10.1007/978-981-97-0808-6_22
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DOI: https://doi.org/10.1007/978-981-97-0808-6_22
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