GPU-based parallel implementation of swarm intelligence algorithms /

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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 deta...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tan, Ying, 1964- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier, [2016]
Temas:
Swarm intelligence.
COMPUTERS > General.
Swarm intelligence
Acceso en línea:Texto completo

MARC

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505 0 |6 880-01  |a Introduction -- GPGPU: general purpose computing on the GPU -- Parallel models -- Performance metrics -- Implementation considerations -- GPU-based particle swarm optimization -- GPU-based fireworks algorithm -- Attract-repulse fireworks algorithm using dynamic parallelism -- Other typical swarm intelligence algorithms based on GPUs -- GPU-based random number generators -- Applications -- A CUDA-based test suit. 
520 |a 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. 
650 0 |a Swarm intelligence. 
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776 0 8 |i Print version:  |a Tan, Ying.  |t GPU-based Parallel Implementation of Swarm Intelligence Algorithms.  |d San Francisco : Elsevier Science, �2016  |z 9780128093627 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128093627  |z Texto completo 
880 0 |6 505-01/(N  |a Front Cover -- GPU-based Parallel Implementation of Swarm Intelligence Algorithms -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Acronyms -- Chapter 1: Introduction -- 1.1 Swarm Intelligence Algorithms (SIAs) -- 1.2 Graphics Processing Units (GPUs) -- 1.3 SIAs and GPUs -- 1.4 Some Perspectives -- 1.5 Organization -- Chapter 2: GPGPU: General-Purpose Computing on the GPU -- 2.1 Introduction -- 2.2 GPGPU Development Platforms -- 2.3 Compute Unified Device Architecture (CUDA) -- 2.4 Open Computing Language (OpenCL) -- 2.5 Programming Techniques -- 2.6 Some Discussions -- 2.7 Summary -- Chapter 3: Parallel Models -- 3.1 Previous Work -- 3.2 Basic Guide for Parallel Programming -- 3.3 GPU-Oriented Parallel Models -- 3.4 Nаїve Parallel Model -- 3.5 Multi-Kernel Parallel Model -- 3.6 All-GPU Parallel Model -- 3.7 Island Parallel Model -- 3.8 Summary -- Chapter 4: Performance Metrics -- 4.1 Parallel Performance Metrics -- 4.2 Algorithm Performance Metrics -- 4.3 Rectified Efficiency -- 4.4 Case Study -- 4.5 Summary -- Chapter 5: Implementation Considerations -- 5.1 Float-Point -- 5.2 Memory Accesses -- 5.3 Random Number Generation -- 5.4 Branch Divergence -- 5.5 Occupancy -- 5.6 Summary -- Chapter 6: GPU-Based Particle Swarm Optimization -- 6.1 Introduction -- 6.2 Particle Swarm Optimization -- 6.3 GPU-Based PSO for Single-Objective Optimization -- 6.4 GPU-Based PSO for Multiple-Objective Optimization -- 6.5 Remarks -- 6.6 Summary -- Chapter 7: GPU-Based Fireworks Algorithm -- 7.1 Introduction -- 7.2 Fireworks Algorithms (FWA) -- 7.3 GPU-Based Fireworks Algorithm -- 7.4 Summary -- Chapter 8: Attract-Repulse Fireworks Algorithm Using Dynamic Parallelism -- 8.1 Introduction -- 8.2 Attract-Repulse Fireworks Algorithm (AR-FWA) -- 8.3 Implementation -- 8.4 Experiments and Analysis -- 8.5 Summary. 

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