期刊
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
卷 19, 期 1, 页码 1-11出版社
SPRINGER
DOI: 10.1007/s10586-016-0534-4
关键词
Heterogeneous computing; Ant colony optimization; CUDA; Power-aware systems
资金
- Fundacion Seneca (Agencia Regional de Ciencia y Tecnologia, Region de Murcia) [15290/PI/2010, 18946/JLI/13]
- Spanish MEC [TIN2012-31345, TIN2013-42253-P]
- Nils Coordinated Mobility [012-ABEL-CM-2014A]
- European Regional Development Fund (ERDF)
- Junta de Andalucia [P12-TIC-1741]
- Nvidia
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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