Parallel Implementation of PSO Algorithm using GPGPU

被引：3
|
作者：
Kaur, Jaspreet [1 ]
Singh, Satvir [1 ]
Singh, Sarabjeet [1 ]
机构：
[1] SBS State Tech Campus, Ferozepur 152004, India
来源：
2016 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT) | 2016年
关键词：
Graphic Processing Unit (GPU); General Purpose computing on GPU (GPGPU); Compute Unified Device Architecture (CUDA); Particle Swarm Optimization (PSO); PARTICLE SWARM;
D O I：
10.1109/CICT.2016.38
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The goal of this paper is to show how swarm intelligence inspired optimization algorithms can take benefit of the parallel computing mechanism supported by general purpose computing ability of a Graphical Processing Unit (GPU). In this paper, Particle Swarm Optimization (PSO) algorithm is implemented both in C (serial) and C-CUDA (parallel) and their performances are compared on a testbed of well-known optimization test functions. Simulation results showed that parallel implementation of PSO using C-CUDA searches near optimal solution in lesser time as compared to that of serial algorithm implemented using C.
引用
收藏
页码：155 / 159
页数：5
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