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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.17743 |
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| _version_ | 1866912289049804800 |
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| author | Zhou, Faguo Li, Shunde Xue, Rong Bu, Lingkun Nie, Ningming Shi, Peng Wang, Jue Hu, Yun Wang, Zongguo Wang, Yangang Yang, Qinmeng Yu, Miao |
| author_facet | Zhou, Faguo Li, Shunde Xue, Rong Bu, Lingkun Nie, Ningming Shi, Peng Wang, Jue Hu, Yun Wang, Zongguo Wang, Yangang Yang, Qinmeng Yu, Miao |
| contents | Three-dimensional neutron transport calculations using the Method of Characteristics (MOC) are highly regarded for their exceptional computational efficiency, precision, and stability. Nevertheless, when dealing with extensive-scale computations, the computational demands are substantial, leading to prolonged computation times. To address this challenge while considering GPU memory limitations, this study transplants the real-time generation and characteristic line computation techniques onto the GPU platform. Empirical evidence emphasizes that the GPU-optimized approach maintains a heightened level of precision in computation results and produces a significant acceleration effect. Furthermore, to fully harness the computational capabilities of GPUs, a dual approach involving characteristic line preloading and load balancing mechanisms is adopted, further enhancing computational efficiency. The resulting increase in computational efficiency, compared to traditional methods, reaches an impressive 300 to 400-fold improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neutron particle transport 3D method of characteristic Multi GPU platform Parallel Computing Zhou, Faguo Li, Shunde Xue, Rong Bu, Lingkun Nie, Ningming Shi, Peng Wang, Jue Hu, Yun Wang, Zongguo Wang, Yangang Yang, Qinmeng Yu, Miao Distributed, Parallel, and Cluster Computing Three-dimensional neutron transport calculations using the Method of Characteristics (MOC) are highly regarded for their exceptional computational efficiency, precision, and stability. Nevertheless, when dealing with extensive-scale computations, the computational demands are substantial, leading to prolonged computation times. To address this challenge while considering GPU memory limitations, this study transplants the real-time generation and characteristic line computation techniques onto the GPU platform. Empirical evidence emphasizes that the GPU-optimized approach maintains a heightened level of precision in computation results and produces a significant acceleration effect. Furthermore, to fully harness the computational capabilities of GPUs, a dual approach involving characteristic line preloading and load balancing mechanisms is adopted, further enhancing computational efficiency. The resulting increase in computational efficiency, compared to traditional methods, reaches an impressive 300 to 400-fold improvement. |
| title | Neutron particle transport 3D method of characteristic Multi GPU platform Parallel Computing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.17743 |