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

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Autore principale: Gerardo A. Laguna-Sánchez
Natura: Artículo científico
Lingua:en
Pubblicazione: Universidad Nacional Autónoma de México 2009
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author Gerardo A. Laguna-Sánchez
author_facet Gerardo A. Laguna-Sánchez
contents Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading GPU Gerardo A. Laguna-Sánchez Mauricio Olguín-Carbajal Nareli Cruz-Cortés Ricardo Barrón-Fernández Jesús A. Álvarez-Cedillo Ingeniería PSO general purpose GPU Multithreading GPU global optimization 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. 2009 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47413020004 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.3 Vol.7
format Artículo científico
id redalyc_47413020004
institution Redalyc
language en
publishDate 2009
publisher Universidad Nacional Autónoma de México
spellingShingle Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading GPU
Gerardo A. Laguna-Sánchez
Ingeniería
PSO
general
purpose GPU
Multithreading GPU
global optimization
Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading GPU Gerardo A. Laguna-Sánchez Mauricio Olguín-Carbajal Nareli Cruz-Cortés Ricardo Barrón-Fernández Jesús A. Álvarez-Cedillo Ingeniería PSO general purpose GPU Multithreading GPU global optimization 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. 2009 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47413020004 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.3 Vol.7
title Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading GPU
topic Ingeniería
PSO
general
purpose GPU
Multithreading GPU
global optimization
url https://www.redalyc.org/articulo.oa?id=47413020004