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2016•Unpublished venueRequires access

Parallel Implementation of PSO Algorithm Using GPGPU

Jaspreet Kaur, Satvir Singh, Sarabjeet Singh

Open publisher page 3 citations

Abstract

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.

About this research paper

What this paper is about

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.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

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.

Key concepts: CUDA, Computer science, Particle swarm optimization, Testbed, Parallel computing, General-purpose computing on graphics processing units, Graphics processing unit, Parallel algorithm

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