GPU-based Parallel Implementation of Swarm Intelligence Algorithms
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GPU-based Parallel Implementation of Swarm Intelligence Algorithms

 Taschenbuch
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ISBN-13:
9780128093627
Einband:
Taschenbuch
Seiten:
256
Autor:
Ying Tan
Gewicht:
570 g
Format:
235x191x mm
Sprache:
Englisch
Beschreibung:

Preface
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
Appendices
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