GPU-based asynchronous particle swarm optimization

Luca Mussi*, Youssef S G Nashed, Stefano Cagnoni

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations


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 languageEnglish (US)
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Number of pages8
StatePublished - Aug 24 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: Jul 12 2011Jul 16 2011


Other13th Annual Genetic and Evolutionary Computation Conference, GECCO'11


  • Implementation
  • Parallelization
  • Particle Swarm Optimization
  • Speed-up technique

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science


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