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首页> 外文期刊>Concurrency and Computation >A scalable HPF implementation of a finite-volume computational electromagnetics application on a CRAY T3E parallel system
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A scalable HPF implementation of a finite-volume computational electromagnetics application on a CRAY T3E parallel system

机译:在CRAY T3E并行系统上可扩展的HPF有限体积计算电磁应用的实现

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摘要

The time-dependent Maxwell equations are one of the most important approaches to describing dynamic or wide-band frequency electromagnetic phenomena. A sequential finite-volume, characteristic-based procedure for solving the time-dependent, three-dimensional Maxwell equations has been successfully implemented in Fortran before. Due to its need for a large memory space and high demand on CPU time, it is impossible to test the code for a large array. Hence, it is essential to implement the code on a parallel computing system. In this paper, we discuss an efficient and scalable parallelization of the sequential Fortran time-dependent Maxwell equations solver using High Performance Fortran (HPF). The background to the project, the theory behind the efficiency being achieved, the parallelization methodologies employed and the experimental results obtained on the Cray T3E massively parallel computing system will be described in detail. Experimental runs show that the execution time is reduced drastically through parallel computing. The code is scalable up to 98 processors on the Cray T3E and has a performance similar to that of an MPI implementation. Based on the experimentation carried out in this research, we believe that a high-level parallel programming language such as HPF is a fast, viable and economical approach to parallelizing many existing sequential codes which exhibit a lot of parallelism.
机译:时间相关的麦克斯韦方程是描述动态或宽带频率电磁现象的最重要方法之一。以前,在Fortran中已成功实现了求解基于时间的三维Maxwell方程的基于有限特征的顺序过程。由于需要大的存储空间和对CPU时间的高要求,因此无法测试大型阵列的代码。因此,在并行计算系统上实现代码至关重要。在本文中,我们讨论了使用高性能Fortran(HPF)对顺序Fortran时间相关的Maxwell方程求解器进行的高效且可扩展的并行化。将详细描述该项目的背景,实现效率的理论,所采用的并行化方法以及在Cray T3E大规模并行计算系统上获得的实验结果。实验表明,通过并行计算可以大大减少执行时间。该代码在Cray T3E上最多可扩展至98个处理器,并具有与MPI实现类似的性能。基于这项研究中的实验,我们相信诸如HPF之类的高级并行编程语言是一种快速,可行且经济的方法,可以并行化许多现有的具有很多并行性的顺序代码。

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