Enhancing data parallelism for Ant Colony Optimization on GPUs

Cecilia, José, García, José, Nisbet, Andy, Amos, Martyn and Ujaldón, Manuel (2013) Enhancing data parallelism for Ant Colony Optimization on GPUs. Journal of Parallel and Distributed Computing, 73 (1). pp. 42-51. ISSN 0743-7315

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.jpdc.2012.01.002

Abstract

Graphics Processing Units (GPUs) have evolved into highly parallel and fully programmable architecture over the past five years, and the advent of CUDA has facilitated their application to many real-world applications. In this paper, we deal with a GPU implementation of Ant Colony Optimization (ACO), a population-based optimization method which comprises two major stages: tour construction and pheromone update. Because of its inherently parallel nature, ACO is well-suited to GPU implementation, but it also poses significant challenges due to irregular memory access patterns. Our contribution within this context is threefold: (1) a data parallelism scheme for tour construction tailored to GPUs, (2) novel GPU programming strategies for the pheromone update stage, and (3) a new mechanism called I-Roulette to replicate the classic roulette wheel while improving GPU parallelism. Our implementation leads to factor gains exceeding 20x for any of the two stages of the ACO algorithm as applied to the TSP when compared to its sequential counterpart version running on a similar single-threaded high-end CPU. Moreover, an extensive discussion focused on different implementation paths on GPUs shows the way to deal with parallel graph connected components. This, in turn, suggests a broader area of inquiry, where algorithm designers may learn to adapt similar optimization methods to GPU architecture.

Item Type: Article
Uncontrolled Keywords: Metaheuristics, GPU programming, Ant Colony Optimization, TSP, Performance analysis
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 19 Feb 2019 16:15
Last Modified: 10 Oct 2019 23:31
URI: http://nrl.northumbria.ac.uk/id/eprint/38123

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence