Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading GPU

被引：0

作者：

Laguna-Sanchez, Gerardo A. ^{[1
]}

Olguin-Carbajal, Mauricio ^{[2
]}

Cruz-Cortes, Nareli ^{[1
]}

Barron-Fernandez, Ricardo ^{[1
]}

Alvarez-Cedillo, Jesus A. ^{[2
]}

机构：

[1] Inst Politecn Nacl, Ctr Invest Computac, Artificial Intelligence Lab, Mexico City, DF, Mexico

[2] Inst Politecn Nacl, CIDETEC, Mexico City, DF, Mexico

来源：

JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY | 2009年 / 7卷 / 03期

关键词：

Multithreading GPU; PSO; general-purpose GPU; parallel programming; global optimization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The Particle Swarm Optimization (PSO) algorithm is a well known alternative for global optimization based on a bio-inspired heuristic. PSO has good performance, low computational complexity and few parameters. Heuristic techniques have been widely studied in the last twenty years and the scientific community is still interested in technological alternatives that accelerate these algorithms in order to apply them to bigger and more complex problems. This article presents an empirical study of some parallel variants for a PSO algorithm, implemented on a Graphic Process Unit (GPU) device with multi-thread support and using the most recent model of parallel programming for these cases. The main idea is to show that, with the help of a multithreading GPU, it is possible to significantly improve the PSO algorithm performance by means of a simple and almost straightforward parallel programming, getting the computing power of cluster in a conventional personal computer.

引用

页码：292 / 309

页数：18

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