Accelerating ant colony optimization-based edge detection on the GPU using CUDA

Dawson, L.; Stewart, I.A.

doi:10.1109/cec.2014.6900638

Accelerating ant colony optimization-based edge detection on the GPU using CUDA

Dawson, L.; Stewart, I.A.

Authors

L. Dawson

Professor Iain Stewart i.a.stewart@durham.ac.uk
Professor

Abstract

Ant Colony Optimization (ACO) is a nature-inspired metaheuristic that can be applied to a wide range of optimization problems. In this paper we present the first parallel implementation of an ACO-based (image processing) edge detection algorithm on the Graphics Processing Unit (GPU) using NVIDIA CUDA. We extend recent work so that we are able to implement a novel data-parallel approach that maps individual ants to thread warps. By exploiting the massively parallel nature of the GPU, we are able to execute significantly more ants per ACO-iteration allowing us to reduce the total number of iterations required to create an edge map. We hope that reducing the execution time of an ACO-based implementation of edge detection will increase its viability in image processing and computer vision.

Citation

Dawson, L., & Stewart, I. (2014). Accelerating ant colony optimization-based edge detection on the GPU using CUDA. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation : July 6-11, 2014, Beijing, China (1736-1743). https://doi.org/10.1109/cec.2014.6900638

Conference Name	2014 IEEE Congress on Evolutionary Computation (CEC)
Conference Location	Beijing, China
Start Date	Jul 6, 2014
End Date	Jul 11, 2014
Publication Date	Jul 1, 2014
Deposit Date	Dec 4, 2014
Publicly Available Date	Aug 8, 2016
Pages	1736-1743
Series ISSN	1089-778X
Book Title	Proceedings of the 2014 IEEE Congress on Evolutionary Computation : July 6-11, 2014, Beijing, China.
ISBN	9781479914883
DOI	https://doi.org/10.1109/cec.2014.6900638
Related Public URLs	http://community.dur.ac.uk/i.a.stewart/Papers/AcceleratingACO.pdf

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