GPU-based Parallel Implementation of Swarm Intelligence Algorithms

  • ID: 3627122
  • Book
  • 256 Pages
  • Elsevier Science and Technology
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GPU-based Parallel Implementation of Swarm Intelligence Algorithms combines and covers two emerging areas attracting increased attention and applications: graphics processing units (GPUs) for general-purpose computing (GPGPU) and swarm intelligence. This book not only presents GPGPU in adequate detail, but also includes guidance on the appropriate implementation of swarm intelligence algorithms on the GPU platform.

GPU-based implementations of several typical swarm intelligence algorithms such as PSO, FWA, GA, DE, and ACO are presented and having described the implementation details including parallel models, implementation considerations as well as performance metrics are discussed. Finally, several typical applications of GPU-based swarm intelligence algorithms are presented. This valuable reference book provides a unique perspective not possible by studying either GPGPU or swarm intelligence alone.

This book gives a complete and whole picture for interested readers and new comers who will find many implementation algorithms in the book suitable for immediate use in their projects. Additionally, some algorithms can also be used as a starting point for further research.

- Presents a concise but sufficient introduction to general-purpose GPU computing which can help the layman become familiar with this emerging computing technique- Describes implementation details, such as parallel models and performance metrics, so readers can easily utilize the techniques to accelerate their algorithmic programs- Appeals to readers from the domain of high performance computing (HPC) who will find the relatively young research domain of swarm intelligence very interesting- Includes many real-world applications, which can be of great help in deciding whether or not swarm intelligence algorithms or GPGPU is appropriate for the task at hand
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1 Introduction
2 GPGPU: General Purpose Computing on the GPU
3 Parallel Models
4 Performance Measurements
5 Implementation Considerations
6 GPU-based Particle Swarm Optimization
7 GPU-based Fireworks Algorithm
8 Attract-Repulse Fireworks Algorithm Using Dynamic Parallelism
9 Other Typical Swarm Intelligence Algorithms based on GPUs
10 GPU-based Random Number Generators
11 Applications
12 A CUDA-Based Test Suit
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Tan, Ying
Ying Tan is a professor and PhD advisor at the School of Electronics Engineering and Computer Science of Peking University, and director of Computational Intelligence Laboratory at Peking University (PKU). He received his BEng from the EEI, MSc from Xidian Univ., and PhD from Southeast Univ., in 1985, 1988, and 1997, respectively. From 1997, he was a postdoctoral fellow then an associate professor at University of Science and Technology of China (USTC), then served as director of Institute of Intelligent Information Science and a full professor since 2000. He worked with the Chinese University of Hong Kong (CUHK) in 1999 and 2004-2005. He was elected for the 100 talent program of the Chinese Academy of Science (CAS) in 2005
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