Abstract
Although GPUs have been traditionally used only for computer graphics, a recent technique called GPGPU (General-purpose computing on graphics processing units) allows the GPUs to perform numerical computations usually handled by CPU. The advantage of using GPUs for general purpose computation is the performance speed up that can be achieved due to the parallel architecture of these devices. This paper describes the use of Bio-Inspired Optimization Methods as Particle Swarm Optimization and Genetic Algorithms on GPUs to demonstrate the performance that can be achieved using this technology with regard to use CPU primarily.
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Valdez, F., Melin, P., Castillo, O. (2013). Bio-inspired Optimization Methods on Graphic Processing Unit for Minimization of Complex Mathematical Functions. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_25
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DOI: https://doi.org/10.1007/978-3-642-33021-6_25
Publisher Name: Springer, Berlin, Heidelberg
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