Guidong Wang , Yujie Wang , Zeyu Chen , Feiqi Wang , She Li , Xiangyang Cui
{"title":"A GPU-Accelerated automated multilevel substructuring method for modal analysis of structures","authors":"Guidong Wang , Yujie Wang , Zeyu Chen , Feiqi Wang , She Li , Xiangyang Cui","doi":"10.1016/j.compstruc.2024.107516","DOIUrl":null,"url":null,"abstract":"<div><p>In this work, a novel GPU-accelerated heterogeneous method for the automated multilevel substructuring method(HAMLS) is presented for dealing large finite element models in structural dynamics. Different parallel modes based on node, subtree, and eigenpair have been developed in the solution steps of AMLS to achieve a heterogeneous strategy. First, a new data management method is designed during the model transformation phase to eliminate the determinacy race in the parallel strategy of the separator tree. Considering the distribution characteristics of the nodes in the separator tree and the dependence of node tasks, a load balancing heterogeneous parallel strategy is designed to take full advantage of hosts and devices. By developing an adaptive batch processing program for solving eigenvectors during the back transformation phase, the overheads of launching kernels, as well as the GPU memory requirements, can be reduced by several orders of magnitude. Several numerical examples have been employed to validate the efficiency and practicality of the novel GPU-accelerated heterogeneous strategy. The results demonstrate that the computational efficiency of the novel strategy using one GPU can increase to 3.0x that of the original parallel AMLS method when 16 CPU threads are used.</p></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"305 ","pages":"Article 107516"},"PeriodicalIF":4.4000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045794924002451","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, a novel GPU-accelerated heterogeneous method for the automated multilevel substructuring method(HAMLS) is presented for dealing large finite element models in structural dynamics. Different parallel modes based on node, subtree, and eigenpair have been developed in the solution steps of AMLS to achieve a heterogeneous strategy. First, a new data management method is designed during the model transformation phase to eliminate the determinacy race in the parallel strategy of the separator tree. Considering the distribution characteristics of the nodes in the separator tree and the dependence of node tasks, a load balancing heterogeneous parallel strategy is designed to take full advantage of hosts and devices. By developing an adaptive batch processing program for solving eigenvectors during the back transformation phase, the overheads of launching kernels, as well as the GPU memory requirements, can be reduced by several orders of magnitude. Several numerical examples have been employed to validate the efficiency and practicality of the novel GPU-accelerated heterogeneous strategy. The results demonstrate that the computational efficiency of the novel strategy using one GPU can increase to 3.0x that of the original parallel AMLS method when 16 CPU threads are used.
期刊介绍:
Computers & Structures publishes advances in the development and use of computational methods for the solution of problems in engineering and the sciences. The range of appropriate contributions is wide, and includes papers on establishing appropriate mathematical models and their numerical solution in all areas of mechanics. The journal also includes articles that present a substantial review of a field in the topics of the journal.