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首页> 外文期刊>Advances in Engineering Software >Explicit nonlinear dynamic finite element analysis on homogeneous/heterogeneous parallel computing environment
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Explicit nonlinear dynamic finite element analysis on homogeneous/heterogeneous parallel computing environment

机译:同构/异构并行计算环境中的显式非线性动力有限元分析

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摘要

This paper presents parallel computational strategies to implement explicit nonlinear finite element analysis code onto distributed memory parallel computers for solving large-scale problems in structural dynamics. Implementation details on both homogeneous and heterogeneous parallel processing environments are considered in detail in this paper. Implementation of an explicit nonlinear finite element dynamic analysis code on homogeneous systems is discussed first and this is later moved onto heterogeneous systems. Domain decomposition with explicit message passing is preferred for parallel implementation. The message passing implementation in the parallel algorithm is based on MPI (Message Passing Interface) libraries. Implementation aspects of overlapped, non-overlapped domain decomposition techniques, Dynamic Task Allocation (DTA) and clustering techniques for DTA and their relative merits are presented. The interprocessor communications are optimised by overlapping with computations to improve the performance of the domain decomposition based explicit dynamic analysis finite element code. The issues related to implementation of finite element code for nonlinear dynamic analysis on heterogeneous parallel computing environment are later presented. A new dynamic load-balancing algorithm is developed for this purpose and it is integrated with the domain decomposition based parallel explicit finite element code to test our algorithms on a coarse grain heterogeneous cluster of workstations. Numerical experiments have been carried out on PAR AM-10000, an Indian parallel computer and also on cluster of Unix workstations.
机译:本文提出了并行计算策略,以在分布式内存并行计算机上实现显式非线性有限元分析代码,以解决结构动力学中的大规模问题。本文详细考虑了同构和异构并行处理环境的实现细节。首先讨论了在齐次系统上实现显式非线性有限元动态分析代码的过程,然后将其转移到异构系统中。对于并行实现,首选使用带有显式消息传递的域分解。并行算法中的消息传递实现基于MPI(消息传递接口)库。介绍了重叠,非重叠域分解技术,动态任务分配(DTA)和DTA聚类技术及其相对优点的实现方面。通过与计算重叠来优化处理器间通信,以提高基于域分解的显式动态分析有限元代码的性能。稍后介绍与在异构并行计算环境中进行非线性动态分析的有限元代码的实现有关的问题。为此,开发了一种新的动态负载平衡算法,并将其与基于域分解的并行显式有限元代码集成在一起,以在工作站的粗粒度异构集群上测试我们的算法。在印度的并行计算机PAR AM-10000以及Unix工作站集群上都进行了数值实验。

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