Distributed memory implementation of elliptic partial differential equations in a dataparallel functional language

H. Kuchen, H. Stoltze, I. Dimov, A. Karaivanova
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引用次数: 4

Abstract

We show that the numerical solution of partial differential equations can be elegantly and efficiently addressed in a functional language. Two statistical numerical methods are considered. We discuss why current parallel imperative languages are difficult to use and why general (expression parallel) functional languages are not efficient enough. The key point of our approach is to offer "unique" arrays and some operations on them which allow to handle their elements in parallel, including operations which exchange the partitions of an array between the processors. These operations constitute a deadlock-free high-level way of communication.
椭圆型偏微分方程在数据并行函数语言中的分布式内存实现
我们证明了偏微分方程的数值解可以用函数语言优雅而有效地求解。考虑了两种统计数值方法。我们讨论了为什么当前的并行命令式语言难以使用,以及为什么通用(表达式并行)函数式语言不够高效。我们方法的关键是提供“唯一”数组和一些允许并行处理数组元素的操作,包括在处理器之间交换数组分区的操作。这些操作构成了一种无死锁的高级通信方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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