Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 17
• Main Authors: Wang, Shengqian, Estevez, Claudio, Kang, Chuanxiong, Xu, Gang, Li, Qingpeng, Qiao, Yu, Yu, Xiang, Wu, Zhaoming
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 31.07.2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Case studies; Computation; Data processing services; Digital signal processors; Global optimization; Graphics processing units; Heuristic methods; Learning; Linear programming; Mathematical optimization; Microprocessors; Optimization; Parallel processing; Particle swarm optimization; Population; Random numbers; Reservoirs; Semiconductor industry; United States; China
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2020/6589658

Comprehensive learning particle swarm optimization (CLPSO) is a powerful metaheuristic for global optimization. This paper studies parallelizing CLPSO by open computing language (OpenCL) on the integrated Intel HD Graphics 520 (IHDG520) graphical processing unit (GPU) with a low clock rate. We implement a coarse-grained all-GPU model that maps each particle to a separate work item. Two enhancement strategies, namely, generating and transferring random numbers from the central processor to the GPU as well as reducing the number of instructions in the kernel, are proposed to shorten the model’s execution time. This paper further investigates parallelizing deterministic optimization for implicit stochastic optimization of China’s Xiaowan Reservoir. The deterministic optimization is performed on an ensemble of 62 years’ historical inflow records with monthly time steps, is solved by CLPSO, and is parallelized by a coarse-grained multipopulation model extended from the all-GPU model. The multipopulation model involves a large number of work items. Because of the capacity limit for a buffer transferring data from the central processor to the GPU and the size of the global memory region, the random number generation strategy is modified by generating a small number of random numbers that can be flexibly exploited by the large number of work items. Experiments conducted on various benchmark functions and the case study demonstrate that our proposed all-GPU and multipopulation parallelization models are appropriate; and the multipopulation model achieves the consumption of significantly less execution time than the corresponding sequential model.