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

 

Gerardo A. Laguna–Sánchez^*1, Mauricio Olguín–Carbajal², Nareli Cruz–Cortés³, Ricardo Barrón–Fernández⁴, Jesús A. Álvarez–Cedillo⁵

 

^{1, 2, 3, 4} Centro de Investigación en Computación (CIC–IPN) Instituto Politécnico Nacional, México D.F, Mexico *galagunab07@sagitario.cic.ipn.mx

⁵ CIDETEC–IPN Instituto Politécnico Nacional, México D.F, México

 

ABSTRACT

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.

Keywords: Multithreading GPU, PSO, general–purpose GPU, parallel programming, global optimization.

 

RESUMEN

El algoritmo Particle Swarm Optimization (PSO) ha tenido gran aceptación como alternativa de optimización global con base en heurísticas bio–inspiradas. Sus principales ventajas son su buen desempeño, baja complejidad computacional y un mínimo de parámetros. En general, las técnicas heurísticas han tenido un gran auge en los últimos veinte años y aún hoy resulta atractivo estudiar alternativas tecnológicas que permitan acelerar estos algoritmos para aplicarlos a problemas mucho más grandes y complejos. En este artículo se presenta un estudio empírico sobre la aplicación de algunas variantes paralelas para un algoritmo PSO, empleando un dispositivo de procesamiento gráfico (GPU) con capacidad multi–hilos y el más reciente modelo de programación paralela para estos casos. La idea principal es demostrar que es posible mejorar significativamente el desempeño del algoritmo PSO, mediante una programación paralela sencilla y directa, logrando con ello el poder computacional de un cluster en una computadora personal convencional.

Palabras clave: GPU con capacidad miltihilos, PSO, GPU para propósitos generales, programación paralela, optimización global.

 

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Acknowledgments

G.A. Laguna–Sánchez and R. Barrón–Fernández acknowledge Instituto Politécnico Nacional (IPN) and Consejo Nacional de Ciencia y Tecnologíal (CONACyT) of Mexico for their support to this research through SIP–20090620 project (IPN), I0013/91434 fund (CONACyT), and Ph. D. scholarship # 210397 (CONACyT).

 

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