A Parallel Version of the JADE Algorithm using GPUS

A. Mexicano, J. C. Carmona, Nelva N. Almaza, Lilia Garcia, Ricardo D. Lopez
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Abstract

: This work presents a parallel implementation of JADE: Adaptive Differential Evolution With Optional External Archive, using the Compute Unified Device Architecture (CUDA), in order to reduce the execution run-time of the algorithm. The algorithm was tested using the well-known function Sphere and the execution run time was compared against its sequential version. The results were measured in terms of “Speed-up” and they show that the execution run-time can be reduced significantly by the use of CUDA, this benefit can be observed better when working with large amounts of data. However, not necessarily the population with more data reaches the best performance.
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基于gpu的并行JADE算法
这项工作提出了JADE的并行实现:自适应差分进化与可选的外部存档,使用计算统一设备架构(CUDA),以减少算法的执行运行时间。使用众所周知的函数Sphere对该算法进行了测试,并将其执行运行时间与其顺序版本进行了比较。结果是根据“加速”来衡量的,它们表明使用CUDA可以显著减少执行运行时间,在处理大量数据时可以更好地观察到这种好处。然而,拥有更多数据的人群不一定能达到最佳性能。
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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