A heterogeneous computing accelerated SCE-UA global optimization method using OpenMP, OpenCL, CUDA, and OpenACC | Zendy

Guangyuan Kan | Zendy; Xiaoyan He | Zendy; Liuqian Ding | Zendy; Jiren Li | Zendy; Ke Liang | Zendy; Yang Hong | Zendy

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A heterogeneous computing accelerated SCE-UA global optimization method using OpenMP, OpenCL, CUDA, and OpenACC

Author(s) -

Guangyuan Kan,

Xiaoyan He,

Liuqian Ding,

Jiren Li,

Ke Liang,

Yang Hong

Publication year - 2017

Publication title -

water science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.406

H-Index - 137

eISSN - 1996-9732

pISSN - 0273-1223

DOI - 10.2166/wst.2017.322

Subject(s) - cuda , parallel computing , xeon phi , computer science , benchmark (surveying) , xeon , computational science , general purpose computing on graphics processing units , acceleration , robustness (evolution) , supercomputer , simd , graphics , chemistry , operating system , physics , biochemistry , geodesy , classical mechanics , gene , geography

The shuffled complex evolution optimization developed at the University of Arizona (SCE-UA) has been successfully applied in various kinds of scientific and engineering optimization applications, such as hydrological model parameter calibration, for many years. The algorithm possesses good global optimality, convergence stability and robustness. However, benchmark and real-world applications reveal the poor computational efficiency of the SCE-UA. This research aims at the parallelization and acceleration of the SCE-UA method based on powerful heterogeneous computing technology. The parallel SCE-UA is implemented on Intel Xeon multi-core CPU (by using OpenMP and OpenCL) and NVIDIA Tesla many-core GPU (by using OpenCL, CUDA, and OpenACC). The serial and parallel SCE-UA were tested based on the Griewank benchmark function. Comparison results indicate the parallel SCE-UA significantly improves computational efficiency compared to the original serial version. The OpenCL implementation obtains the best overall acceleration results however, with the most complex source code. The parallel SCE-UA has bright prospects to be applied in real-world applications.

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