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) |
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Title of host publication | Genetic and Evolutionary Computation Conference, GECCO'11 |
Pages | 1555-1562 |
Number of pages | 8 |
DOIs | |
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 |
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Country/Territory | 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