喻高远, 李俊杰, 楼云锋, 金先龙. 结构动力学有限元混合分层并行计算方法[J]. 工程力学, 2024, 41(9): 1-8. DOI: 10.6052/j.issn.1000-4750.2022.07.0649
引用本文: 喻高远, 李俊杰, 楼云锋, 金先龙. 结构动力学有限元混合分层并行计算方法[J]. 工程力学, 2024, 41(9): 1-8. DOI: 10.6052/j.issn.1000-4750.2022.07.0649
YU Gao-yuan, LI Jun-jie, LOU Yun-feng, JIN Xian-long. HYBRID HIERARCHICAL PARALLEL ALGORITHMS FOR STRUCTURE DYNAMIC ANALYSIS[J]. Engineering Mechanics, 2024, 41(9): 1-8. DOI: 10.6052/j.issn.1000-4750.2022.07.0649
Citation: YU Gao-yuan, LI Jun-jie, LOU Yun-feng, JIN Xian-long. HYBRID HIERARCHICAL PARALLEL ALGORITHMS FOR STRUCTURE DYNAMIC ANALYSIS[J]. Engineering Mechanics, 2024, 41(9): 1-8. DOI: 10.6052/j.issn.1000-4750.2022.07.0649

结构动力学有限元混合分层并行计算方法

HYBRID HIERARCHICAL PARALLEL ALGORITHMS FOR STRUCTURE DYNAMIC ANALYSIS

  • 摘要: 为减小利用异构众核分布式存储计算机并行求解大规模、超大规模系统结构动力学有限元问题对计算效率造成的损失,在区域分解法的基础上,提出了一种结构动力学有限元混合分层并行计算方法。在该方法中,引入计算过程数据的分布式存储提升数据的内存访问效率。为了弥补传统区域分解法界面方程规模随子区域增加造成通信开销增加的缺点,采用基于网格区域和求解算法区域的混合并行分区,从而降低界面方程的规模,大幅度减少界面方程的求解时间。利用计算过程的三层并行实现计算节点间通信、异构群组间通信与异构群组内通信分离,从而有效提高了通信效率。因此,该方法能够充分利用国产异构众核分布式存储计算机的体系结构特点提升大规模、超大规模系统结构动力学并行计算效率。通过对基准实例的求解,验证了该计算方法的有效性和优越性。

     

    Abstract: To reduce the parallel efficiency degradation of the system dynamic analysis on an entire large structure caused by the heterogeneous multi-core and distributed memory parallel computers, a hybrid hierarchical parallel algorithm for structure dynamic analysis is proposed on the basis of distributed domain solver. In this algorithm, the distributed storage of large amount of data is introduced to improve the memory access. To make up for the shortcoming that the communication cost increases with increase of the interface equation size when using the traditional distributed domain solver, a hybrid partitioning based on distributed domain solver and parallel solver is introduced to reduce the interface equation size and further reduce the solution time. Moreover, a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-node, heterogeneous core groups and inside- heterogeneous core-groups, which can significantly improve the communication rate. Thus, it can improve the efficiency rates of parallel computing of large-scale dynamic analysis by fully exploiting the architecture characteristics of the heterogeneous multi-core and distributed memory parallel computers. By solving benchmark instances, the effectiveness and superiority of the proposed method are proved.

     

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