scholarly journals GPU-PSO: Parallel Particle Swarm Optimization Approaches on Graphical Processing Unit for Constraint Reasoning: Case of Max-CSPs

2015 ◽  
Vol 60 ◽  
pp. 1070-1080 ◽  
Author(s):  
Narjess Dali ◽  
Sadok Bouamama
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Swarm Optimization ◽  
Constraint Reasoning ◽  
Parallel Particle Swarm Optimization ◽  
Graphical Processing

scholarly journals Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

2018 ◽  
Vol 32 (3) ◽  
pp. 256-268 ◽  
Author(s):  
Hacer Yalim Keles
Keyword(s):  
Particle Swarm Optimization ◽  
Graphics Processing Units ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Method ◽  
Processing Unit ◽  
Shape Grammars ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization ◽  
Graphics Processing

AbstractEmbedding emergent parts in shape grammars is computationally challenging. The first challenge is the representation of shapes, which needs to enable reinterpretation of parts regardless of the creation history of the shapes. The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space–time efficiently. In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization-based algorithm to compute emergent parts. The execution time of the proposed method is improved substantially by dividing the search space into small parts and carrying out searches in each part concurrently using a graphics processing unit. The experiments show that the proposed implementation detects emergent parts accurately and time efficiently.

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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiang Yu ◽  
Yu Qiao ◽  
Qingpeng Li ◽  
Gang Xu ◽  
Chuanxiong Kang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Execution Time ◽  
Graphical Processing Unit ◽  
Coarse Grained ◽  
Random Numbers ◽  
Processing Unit ◽  
Central Processor ◽  
Swarm Optimization ◽  
Deterministic Optimization ◽  
Graphical Processing

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.

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