000			05966cam a2200481Ii 4500
001			ocn946997805
003			OCoLC
005			20190328114815.0
006			m o d
007			cr cnu---unuuu
008			160420s2016 ne ob 001 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dYDXCP _dN$T _dOCLCF _dOCLCA _dUIU _dOPELS _dEBLCP _dIDEBK _dDEBSZ _dFEM _dIDB _dCNCGM _dVGM _dOCLCQ _dMFS _dB3G _dNRC _dMERUC _dAU@ _dOCLCQ _dLVT _dTKN _dSTF _dDEBBG _dESU
066			_c(Q
019			_a950462235 _a968003164 _a969092685
020			_a9780128093641 _q(electronic bk.)
020			_a0128093641 _q(electronic bk.)
020			_z9780128093627
020			_z0128093625
024	8		_a40026057148
035			_a(OCoLC)946997805 _z(OCoLC)950462235 _z(OCoLC)968003164 _z(OCoLC)969092685
050		4	_aQ337.3
072		7	_aCOM _x000000 _2bisacsh
082	0	4	_a006.3/824 _223
100	1		_aTan, Ying, _d1964- _eauthor.
245	1	0	_aGPU-based parallel implementation of swarm intelligence algorithms / _h[electronic resource] _cYing Tan.
264		1	_aAmsterdam : _bElsevier, _c2016.
264		4	_c�2016
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rda
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed April 25, 2016).
505	0		_aIntroduction -- 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			_aGPU-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	_aSwarm intelligence.
650		7	_aCOMPUTERS _xGeneral. _2bisacsh
650		7	_aSwarm intelligence. _2fast _0(OCoLC)fst01139953
655		4	_aElectronic books.
776	0	8	_iPrint version: _aTan, Ying. _tGPU-based Parallel Implementation of Swarm Intelligence Algorithms. _dSan Francisco : Elsevier Science, �2016 _z9780128093627
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128093627
880	0		_6505-00 _aFront 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 Naїve 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.
999			_c247319 _d247319