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GPU Computation in Bioinspired Algorithms: A Review

  • M. G. Arenas
  • A. M. Mora
  • G. Romero
  • P. A. Castillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6691)

Abstract

Bioinspired methods usually need a high amount of computational resources. For this reason, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also refered as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages (CUDA, OpenCL, etc.), graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This paper reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.

Keywords

Graphic Processing Unit Quadratic Assignment Problem Island Model Parallel Genetic Algorithm Bioinspired Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. G. Arenas
    • 1
  • A. M. Mora
    • 1
  • G. Romero
    • 1
  • P. A. Castillo
    • 1
  1. 1.Department of Architecture and Computer Technology. CITICUniversity of GranadaSpain

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