Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

Llanes, Antonio, Cecilia, José M., Sánchez, Antonia, García, José M., Amos, Martyn and Ujaldón, Manuel (2016) Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Computing, 19 (1). pp. 1-11. ISSN 1386-7857

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s10586-016-0534-4

Abstract

Ant colony optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to “real world” problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.

Item Type: Article
Uncontrolled Keywords: Heterogeneous computing, Ant colony optimization, CUDA, Power-aware systems
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 18 Sep 2018 14:46
Last Modified: 11 Oct 2019 19:15
URI: http://nrl.northumbria.ac.uk/id/eprint/35768

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