Accelerating Ant Colony Optimization-based Edge Detection on the GPU using CUDA
School of Mathematics and Computer Sciences, Durham University
IEEE Congress on Evolutionary Computation, (IEEE-CEC’14), 2014
@article{dawson2014accelerating,
title={Accelerating Ant Colony Optimization-based Edge Detection on the GPU using CUDA},
author={Dawson, Laurence and Stewart, Iain A},
year={2014}
}
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.
April 30, 2014 by hgpu
