Parallelization Strategies for Ant Colony Optimisation on GPUs

TL;DR

This paper explores various GPU-based parallelization strategies for Ant Colony Optimization, achieving significant speed-ups in both tour construction and pheromone update stages, and demonstrating ACO's suitability for GPU acceleration.

Contribution

It introduces new data-based parallelism schemes for ACO's stages on GPUs and provides comprehensive strategies that improve performance over previous task-based implementations.

Findings

Speed-up of over 28x in tour construction

Speed-up of over 20x in pheromone update

Demonstrates ACO's potential for GPU acceleration

Abstract

Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a potentially fruitful area for future research in the GPU domain.