Enhancing GPU parallelism in nature-inspired algorithms

Cecilia, José, Nisbet, Andy, Amos, Martyn, García, José and Ujaldón, Manuel (2013) Enhancing GPU parallelism in nature-inspired algorithms. The Journal of Supercomputing, 63 (3). pp. 773-789. ISSN 0920-8542

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s11227-012-0770-1

Abstract

We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia’s Fermi architecture.

Item Type: Article
Uncontrolled Keywords: GPUs, HPC, ACO, P systems, Bioinspired methods
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 20 Feb 2019 08:56
Last Modified: 10 Oct 2019 23:31
URI: http://nrl.northumbria.ac.uk/id/eprint/38126

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