Evaluation of parallel particle swarm optimization algorithms within the CUDA(TM) architecture

Details

Serval ID
serval:BIB_5E246AF5BF66
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Evaluation of parallel particle swarm optimization algorithms within the CUDA(TM) architecture
Journal
Information Sciences
Author(s)
Luca Mussi, Fabio Daolio, Stefano Cagnoni
ISSN
0020-0255
Publication state
Published
Issued date
2010
Peer-reviewed
Oui
Volume
181
Number
20
Pages
4642 - 4657
Language
english
Notes
Special Issue on Interpretable Fuzzy Systems
Abstract
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically parallel and can be effectively implemented on Graphics Processing Units (GPUs), which are, in fact, massively parallel processing architectures. In this paper we discuss possible approaches to parallelizing PSO on graphics hardware within the Compute Unified Device Architecture (CUDA(TM)), a GPU programming environment by nVIDIA(TM) which supports the company's latest cards. In particular, two different ways of exploiting GPU parallelism are explored and evaluated. The execution speed of the two parallel algorithms is compared, on functions which are typically used as benchmarks for PSO, with a standard sequential implementation of PSO (SPSO), as well as with recently published results of other parallel implementations. An in-depth study of the computation efficiency of our parallel algorithms is carried out by assessing speed-up and scale-up with respect to SPSO. Also reported are some results about the optimization effectiveness of the parallel implementations with respect to SPSO, in cases when the parallel versions introduce some possibly significant difference with respect to the sequential version.
Keywords
nVIDIA CUDA(TM), Particle swarm optimization, Parallel computing, GPUs
Create date
11/03/2011 10:00
Last modification date
01/11/2019 10:28
Usage data