GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

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...

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Published in:International journal of interactive multimedia and artificial intelligence Vol. 3; no. 2; pp. 69 - 76
Main Authors: Arranz, Álvaro, Alvar, Manuel
Format: Journal Article
Language:English
Published: IMAI Software 01.03.2015
UNIR-Universidad Internacional de La Rioja
Universidad Internacional de La Rioja (UNIR)
Subjects:
Computer vision
genetic algorithm
Genetic Algorithms
GPGPU
Parallel Processing
stereo
Spain
ISSN:1989-1660, 1989-1660
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Abstract 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. Keywords--Parallel processing, GPGPU, genetic algorithm, stereo.
AbstractList 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. Keywords--Parallel processing, GPGPU, genetic algorithm, stereo.
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.
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.
Audience Academic
Author Arranz, Álvaro
Alvar, Manuel
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SubjectTerms Computer vision
genetic algorithm
Genetic Algorithms
GPGPU
Parallel Processing
stereo
Title GPGPU Implementation of a Genetic Algorithm for Stereo Refinement
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