An improved CUDA-based implementation of differential evolution on GPU

Kai Qin 1 Federico Raimondo 1 Florence Forbes 1, * Yew Soon Ong 2
* Auteur correspondant
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Modern GPUs enable widely affordable personal computers to carry out massively parallel computation tasks. NVIDIA's CUDA technology provides a wieldy parallel computing platform. Many state-of-the-art algorithms arising from different fields have been redesigned based on CUDA to achieve computational speedup. Differential evolution (DE), as a very promising evolutionary algorithm, is highly suitable for parallelization owing to its data-parallel algorithmic structure. However, most existing CUDA-based DE implementations suffer from excessive low-throughput memory access and less efficient device utilization. This work presents an improved CUDA-based DE to optimize memory and device utilization: several logically-related kernels are combined into one composite kernel to reduce global memory access; kernel execution configuration parameters are automatically determined to maximize device occupancy; streams are employed to enable concurrent kernel execution to maximize device utilization. Experimental results on several numerical problems demonstrate superior computational time efficiency of the proposed method over two recent CUDA-based DE and the sequential DE across varying problem dimensions and algorithmic population sizes.
Type de document :
Communication dans un congrès
GECCO '12 - 14th international conference on Genetic and evolutionary computation conference, Jul 2012, Philadelphia, United States. ACM, pp.991-998, 2012, 〈http://dl.acm.org/citation.cfm?id=2330301&preflayout=tabs〉. 〈10.1145/2330163.2330301〉
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https://hal.inria.fr/hal-00780081
Contributeur : Florence Forbes <>
Soumis le : mercredi 23 janvier 2013 - 10:38:40
Dernière modification le : mercredi 11 avril 2018 - 01:59:44

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Kai Qin, Federico Raimondo, Florence Forbes, Yew Soon Ong. An improved CUDA-based implementation of differential evolution on GPU. GECCO '12 - 14th international conference on Genetic and evolutionary computation conference, Jul 2012, Philadelphia, United States. ACM, pp.991-998, 2012, 〈http://dl.acm.org/citation.cfm?id=2330301&preflayout=tabs〉. 〈10.1145/2330163.2330301〉. 〈hal-00780081〉

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