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A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Gaoyuan Yu1, Yunfeng Lou2, Hang Dong3, Junjie Li1, Xianlong Jin1,*

1 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
2 Aerospace System Engineering Shanghai, Shanghai, 201108, China
3 School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, 2006, Australia

* Corresponding Author: Xianlong Jin. Email: email

Computer Modeling in Engineering & Sciences 2023, 136(1), 135-155. https://doi.org/10.32604/cmes.2023.025166

Abstract

Heterogeneous multicore clusters are becoming more popular for high-performance computing due to their great computing power and cost-to-performance effectiveness nowadays. Nevertheless, parallel efficiency degradation is still a problem in large-scale structural analysis based on heterogeneous multicore clusters. To solve it, a hybrid hierarchical parallel algorithm (HHPA) is proposed on the basis of the conventional domain decomposition algorithm (CDDA) and the parallel sparse solver. In this new algorithm, a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-nodes, heterogeneous-core-groups (HCGs) and inside-heterogeneous-core-groups through mapping computing tasks to various hardware layers. This approach can not only achieve load balancing at different layers efficiently but can also improve the communication rate significantly through hierarchical communication. Additionally, the proposed hybrid parallel approach in this article can reduce the interface equation size and further reduce the solution time, which can make up for the shortcoming of growing communication overheads with the increase of interface equation size when employing CDDA. Moreover, the distributed sparse storage of a large amount of data is introduced to improve memory access. By solving benchmark instances on the Shenwei-Taihuzhiguang supercomputer, the results show that the proposed method can obtain higher speedup and parallel efficiency compared with CDDA and more superior extensibility of parallel partition compared with the two-level parallel computing algorithm (TPCA).

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APA Style
Yu, G., Lou, Y., Dong, H., Li, J., Jin, X. (2023). A new hybrid hierarchical parallel algorithm to enhance the performance of large-scale structural analysis based on heterogeneous multicore clusters. Computer Modeling in Engineering & Sciences, 136(1), 135-155. https://doi.org/10.32604/cmes.2023.025166
Vancouver Style
Yu G, Lou Y, Dong H, Li J, Jin X. A new hybrid hierarchical parallel algorithm to enhance the performance of large-scale structural analysis based on heterogeneous multicore clusters. Comput Model Eng Sci. 2023;136(1):135-155 https://doi.org/10.32604/cmes.2023.025166
IEEE Style
G. Yu, Y. Lou, H. Dong, J. Li, and X. Jin "A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters," Comput. Model. Eng. Sci., vol. 136, no. 1, pp. 135-155. 2023. https://doi.org/10.32604/cmes.2023.025166



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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