An improved CUDA-based implementation of differential evolution on GPU

Qin, K, Raimondo, F, Forbes, F and Ong, Y 2012, 'An improved CUDA-based implementation of differential evolution on GPU', in Jason H. Moore (ed.) Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO 2012), Philadelphia, PA, USA, 7-11 July 2012, pp. 991-998.


Document type: Conference Paper
Collection: Conference Papers

Title An improved CUDA-based implementation of differential evolution on GPU
Author(s) Qin, K
Raimondo, F
Forbes, F
Ong, Y
Year 2012
Conference name GECCO '12
Conference location Philadelphia, PA, USA
Conference dates 7-11 July 2012
Proceedings title Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO 2012)
Editor(s) Jason H. Moore
Publisher Association for Computing Machinery (ACM)
Place of publication New York, NY, USA
Start page 991
End page 998
Total pages 8
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.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) CUDA
Compute Unified Device Architecture
DE
Differential Evolution
GPU
Graphics Processing Unit
Massively Parallel Computing
DOI - identifier 10.1145/2330163.2330301
Copyright notice © 2012 ACM
ISBN 9781450311779
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