GPU Computation in Bioinspired Algorithms: A Review
Department of Architecture and Computer Technology. CITIC, University of Granada, Spain
Advances in Computational Intelligence, Lecture Notes in Computer Science, 2011, Volume 6691/2011, 433-440
@incollection{springerlink:10.1007/978-3-642-21501-8_54,
author={Arenas, M. and Mora, A. and Romero, G. and Castillo, P.},
affiliation={Department of Architecture and Computer Technology. CITIC, University of Granada, Spain},
title={GPU Computation in Bioinspired Algorithms: A Review},
booktitle={Advances in Computational Intelligence},
series={Lecture Notes in Computer Science},
editor={Cabestany, Joan and Rojas, Ignacio and Joya, Gonzalo},
publisher={Springer Berlin / Heidelberg},
isbn={978-3-642-21500-1},
keyword={Computer Science},
pages={433-440},
volume={6691},
url={http://dx.doi.org/10.1007/978-3-642-21501-8_54},
note={10.1007/978-3-642-21501-8_54},
year={2011}
}
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.
September 8, 2011 by hgpu
