Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit
Joint Authors
Wang, Shengqian
Yu, Xiang
Qiao, Yu
Li, Qingpeng
Xu, Gang
Kang, Chuanxiong
Estevez, Claudio
Wu, Zhaoming
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-31
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yu, Xiang& Qiao, Yu& Li, Qingpeng& Xu, Gang& Kang, Chuanxiong& Estevez, Claudio…[et al.]. 2020. Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit. Complexity،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1142990
Modern Language Association (MLA)
Yu, Xiang…[et al.]. Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit. Complexity No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1142990
American Medical Association (AMA)
Yu, Xiang& Qiao, Yu& Li, Qingpeng& Xu, Gang& Kang, Chuanxiong& Estevez, Claudio…[et al.]. Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit. Complexity. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1142990
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142990