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Titlebook: Massively Parallel Evolutionary Computation on GPGPUs; Shigeyoshi Tsutsui,Pierre Collet Book 2013 Springer-Verlag Berlin Heidelberg 2013 A

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書(shū)目名稱(chēng)Massively Parallel Evolutionary Computation on GPGPUs
編輯Shigeyoshi Tsutsui,Pierre Collet
視頻videohttp://file.papertrans.cn/626/625368/625368.mp4
概述First book dedicated to exciting new approach.Content characterized by emphasis on solving practical problems.Valuable for researchers, practitioners, and graduate students.Includes supplementary mate
叢書(shū)名稱(chēng)Natural Computing Series
圖書(shū)封面Titlebook: Massively Parallel Evolutionary Computation on GPGPUs; Shigeyoshi Tsutsui,Pierre Collet Book 2013 Springer-Verlag Berlin Heidelberg 2013 A
描述.Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development..?.The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The 10 chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms
出版日期Book 2013
關(guān)鍵詞Artificial chemistries; CGP; Cartesian genetic programming; Clusters; Differential evolution; Evolutionar
版次1
doihttps://doi.org/10.1007/978-3-642-37959-8
isbn_softcover978-3-662-51345-3
isbn_ebook978-3-642-37959-8Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightSpringer-Verlag Berlin Heidelberg 2013
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GPU-Accelerated High-Accuracy Molecular Docking Using Guided Differential Evolution average speedup of 3.9× on a four-core CPU and 27.4× on a comparable CUDA-enabled GPU when docking 133 ligands of different sizes. Furthermore, the presented parallelisation schemes are generally applicable and can easily be adapted to other flexible docking methods.
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An Analytical Study of Parallel GA with Independent Runs on GPUsison to CPU computations, GPU computation shows a speedup of 7.2× and 13.1× on average using a single GTX 285 GPU and two GTX 285 GPUs, respectively. The parallel independent run model is the simplest of the various parallel evolutionary computation models, and among the models it demonstrates the lower limit performance.
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Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Unitle data (SIMD) mode of parallel computing, even though the GP populations contain different programs. A 448 node nVidia Fermi C2050 Tesla graphics card delivers 8.5 giga GPops per second. In addition to describing our implementation, we survey current GPGPU work in bioinformatics and genetic programming.
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