基于遗传算法的HPF自动数据分区方案

S. Anand, Y. Srikant
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引用次数: 0

摘要

并行程序的性能在很大程度上取决于它的数据分区。因此,一个好的数据分区方案是当务之急。然而,很难得出一个好的解决方案,因为给定的实际程序的可能数据分区的数量是程序大小的指数。我们提出了一种启发式的HPF自动数据分区技术。我们的方法基于遗传算法,非常简单,但非常有效,可以快速找到适当的数据分区,即使对于具有大量数据分布替代方案的大型程序也是如此。它利用静态和动态数据分布,主要目的是减少整个程序的总体执行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic algorithm based automatic data partitioning scheme for HPF
The performance of a parallel program depends largely on its data partitions. So a good data partitioning scheme is the need of the time. However it is very difficult to arrive at a good solution as the number of possible data partitions for a given real life program is exponential in the size of the program. We present a heuristic technique for automatic data partitioning for HPF. Our approach is based on genetic algorithms and is very simple, yet very efficient to quickly find appropriate data partitions even for large programs with large number of alternatives for data distribution. It makes use of both static as well as dynamic data distribution with the main aim of reducing the overall execution time of the entire program.
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