期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：86
期号：2
出版社：Journal of Theoretical and Applied
摘要：The computational power of a Graphics Processing Unit (GPU), relative to a single CPU, presents a promising alternative to write parallel codes in an efficient and economical way. Differential Evolution (DE) algorithm is a global optimization based on bio-inspired heuristic. DE has a good performance, low computational complexity and need few parameters. This article presents parallel implementation of this population-based heuristic, implemented on a NVIDIA GPU device with multi-thread support and using CUDA as the model of parallel programming for these case. Our goal is to give some insights about GPUs parallel programming by a simple and almost straightforward parallel code, and compare the performance of DE algorithm running on a multithreading GPU. This work shows that with a parallel code and a NVIDIA GPU not only the execution time is reduced but also the convergence behavior to the global optimum may be changed in a significant manner with respect the original sequential code.
关键词：Multithreading; Parallel Programming; GPU; Differential Evolution And Fine Grain.
