GPU-based asynchronous particle swarm optimization

Luca Mussi; Youssef S G Nashed; Stefano Cagnoni

doi:10.1145/2001576.2001786

GPU-based asynchronous particle swarm optimization

Luca Mussi^*, Youssef S G Nashed, Stefano Cagnoni

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

32 Scopus citations

Abstract

This paper describes our latest implementation of Particle Swarm Optimization (PSO) with simple ring topology for modern Graphic Processing Units (GPUs). To achieve both the fastest execution time and the best performance, we designed a parallel version of the algorithm, as fine-grained as possible, without introducing explicit synchronization mechanisms among the particles' evolution processes. The results we obtained show a significant speed-up with respect to both the sequential version of the algorithm run on an up-to-date CPU and our previously developed parallel implementation within the nVIDIA™ CUDA™ architecture.

Original language	English (US)
Title of host publication	Genetic and Evolutionary Computation Conference, GECCO'11
Pages	1555-1562
Number of pages	8
DOIs	https://doi.org/10.1145/2001576.2001786
State	Published - Aug 24 2011
Event	13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland Duration: Jul 12 2011 → Jul 16 2011

Other

Other	13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
Country	Ireland
City	Dublin
Period	7/12/11 → 7/16/11

Keywords

Implementation
Parallelization
Particle Swarm Optimization
Speed-up technique

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

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title = "GPU-based asynchronous particle swarm optimization",

abstract = "This paper describes our latest implementation of Particle Swarm Optimization (PSO) with simple ring topology for modern Graphic Processing Units (GPUs). To achieve both the fastest execution time and the best performance, we designed a parallel version of the algorithm, as fine-grained as possible, without introducing explicit synchronization mechanisms among the particles' evolution processes. The results we obtained show a significant speed-up with respect to both the sequential version of the algorithm run on an up-to-date CPU and our previously developed parallel implementation within the nVIDIA{\texttrademark} CUDA{\texttrademark} architecture.",

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author = "Luca Mussi and Nashed, {Youssef S G} and Stefano Cagnoni",

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doi = "10.1145/2001576.2001786",

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AU - Mussi, Luca

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AU - Cagnoni, Stefano

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N2 - This paper describes our latest implementation of Particle Swarm Optimization (PSO) with simple ring topology for modern Graphic Processing Units (GPUs). To achieve both the fastest execution time and the best performance, we designed a parallel version of the algorithm, as fine-grained as possible, without introducing explicit synchronization mechanisms among the particles' evolution processes. The results we obtained show a significant speed-up with respect to both the sequential version of the algorithm run on an up-to-date CPU and our previously developed parallel implementation within the nVIDIA™ CUDA™ architecture.

AB - This paper describes our latest implementation of Particle Swarm Optimization (PSO) with simple ring topology for modern Graphic Processing Units (GPUs). To achieve both the fastest execution time and the best performance, we designed a parallel version of the algorithm, as fine-grained as possible, without introducing explicit synchronization mechanisms among the particles' evolution processes. The results we obtained show a significant speed-up with respect to both the sequential version of the algorithm run on an up-to-date CPU and our previously developed parallel implementation within the nVIDIA™ CUDA™ architecture.

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KW - Parallelization

KW - Particle Swarm Optimization

KW - Speed-up technique

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GPU-based asynchronous particle swarm optimization

Abstract

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Keywords

ASJC Scopus subject areas

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