| 基于局部通信的有限元分析并行算法优化研究 |
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| 引用本文： | 吴建平,蒋涛,彭军,银福康,杨锦辉.基于局部通信的有限元分析并行算法优化研究[J].计算力学学报,2021,38(1):51-59. |
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| 作者姓名： | 吴建平 蒋涛 彭军 银福康 杨锦辉 |
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| 作者单位： | 国防科技大学气象海洋学院,长沙410073;国防科技大学气象海洋学院,长沙410073;国防科技大学计算机学院,长沙410073 |
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| 基金项目： | 国家自然科学基金（41875121；61379022）资助项目. |
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| 摘 要： | 针对有限元分析的计算问题,在现有采用全局通信方案的简单并行算法基础上,对其所涉核心算法,采用稀疏数据结构与局部通信进行并行算法优化设计,有效减少了通信所涉及的处理器个数与通信量.同时,通过采用非阻塞通信,并将与通信无关计算进行分离与前置的方法,进行计算与通信重叠,以有效隐藏通信开销的影响.实验结果表明,优化所得算法相比...
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| 关 键 词： | 有限元 刚度矩阵 稀疏矩阵 并行算法 混凝土试件 |
| 收稿时间： | 2020/3/16 0:00:00 |
| 修稿时间： | 2020/7/8 0:00:00 |
Research on optimization techniques for parallel finite element analysis based on local communication operations |
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| WU Jian-ping,JIANG Tao,PENG Jun,YIN Fu-kang,YNAG Jin-hui.Research on optimization techniques for parallel finite element analysis based on local communication operations[J].Chinese Journal of Computational Mechanics,2021,38(1):51-59. |
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| Authors: | WU Jian-ping JIANG Tao PENG Jun YIN Fu-kang YNAG Jin-hui |
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| Institution: | College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China,College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China;College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China,College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China,College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China and College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China |
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| Abstract: | In this paper,for finite element analyses,based on the existing simple parallel algorithm with global communication operations,the core algorithms are optimized with sparse data structures and local communication operators.These strategies reduce the number of processors and traffic involved in communication.At the same time,by using non-blocking communication operations and executing the communication-free computations first,the overlapping of communication with computations is carried out to effectively hide the communication overhead.The experimental results show that the optimized algorithm has been greatly improved,especially for the multiplication of a sparse matrix by a dense vector and the assembly of elemental contributions.In addition,with the increase of the number of tasks,the improvement is more and more significant. |
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| Keywords: | finite element stiffness matrix sparse matrix parallel algorithm concrete specimen |
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