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Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

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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.

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This work is jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under Grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC under grants TIN2012-31345 and TIN2013-42253-P, by the Nils Coordinated Mobility under Grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF), and by the Junta de Andalucía under Project of Excellence P12-TIC-1741. We also thank Nvidia for hardware donations within UCAM and UMA CUDA Teaching and Research Centers awards.

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Correspondence to Manuel Ujaldón.

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Llanes, A., Cecilia, J.M., Sánchez, A. et al. Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Comput 19, 1–11 (2016).

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