GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

TitleGPGPU Implementation of a Genetic Algorithm for Stereo Refinement
Publication TypeJournal Article
Year of Publication2015
AuthorsArranz, Á., and M. Alvar
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
IssueSpecial Issue on Digital Economy
Date Published03/2015

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.

KeywordsGenetic Algorithms, GPGPU, Parallel Processing
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