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Título : A differential evolution algorithm parallel implementation in a GPU
Autor(es) : Gerardo Abel Laguna-Sanchez, 0000-0001-5145-1248
Autor(es) sin ID: Olguín Carbajal, M.Cruz Cortés, N.Barrón Fernández, R.Cadena Martínez, R.
Fecha de publicación : 2016
Tipo de resultado Científico: article
Palabras clave: Multithreading; Parallel Programming; GPU; Differential Evolution and Fine Grain
Descripción : 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 GPU’s 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.
Editor: JATIT
URI : http://hdl.handle.net/20.500.12222/141
Condiciones de licencia: https://creativecommons.org/licenses/by-nc-nd/4.0/
Fuente: Journal of Theoretical and Applied Information Technology (JATIT) (2) vol.86 (2016)
ISSN: 1992-8645
Aparece en las colecciones: Artículos Científicos

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