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標(biāo)題: Titlebook: Massively Parallel Evolutionary Computation on GPGPUs; Shigeyoshi Tsutsui,Pierre Collet Book 2013 Springer-Verlag Berlin Heidelberg 2013 A [打印本頁(yè)]

作者: 作業(yè)    時(shí)間: 2025-3-21 19:55
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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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Generic Local Search (Memetic) Algorithm on a Single GPGPU Chipf the EASEA platform provides an automatic generation and parallelization of an MA for both novice and experienced users. Experiments on a benchmark function and a real-world problem reveal speedups ranging between × 70 and × 120, depending on population size and number of local search iterations.
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Book 2013s 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 par
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Why GPGPUs for Evolutionary Computation?e more powerful chips, manufacturers then started developing multi-core processors, a path that had already been taken by graphics cards manufacturers earlier on. In 2012, NVIDIA came out with GK110 processors boasting 2,880 single precision cores and 960 double precision cores, for a computing powe
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arGA: Adaptive Resolution Micro-genetic Algorithm with Tabu Search to Solve MINLP Problems Using GPUProgramming (MINLP) and non-convex Nonlinear Programming (NLP) problems. The target is to solve MINLP/NLP problems from different domains of research in fewer fitness evaluations as compared to other state-of-the-art stochastic algorithms in the area. The algorithm is GPU compatible and shows remark
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ACO with Tabu Search on GPUs for Fast Solution of the QAPce Architecture (CUDA). In TS on QAPs, there are . neighbors in a candidate solution. These TS moves form two groups based on computing cost. In one group, the computing of the move cost is ., and in the other group the computing of the move cost is .. We compute these groups of moves in parallel by
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New Ideas in Parallel Metaheuristics on GPU: Systolic Genetic Searchnderlying operation relates to systolic computing and is inspired by the systolic contraction of the heart that makes possible blood circulation. The algorithm, called Systolic Genetic Search (SGS), is based on the synchronous circulation of solutions through a grid of processing units and tries to
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Implementation Techniques for Massively Parallel Multi-objective Optimization as multi-objective optimization (MOO). It has been a challenge for researchers and practitioners to find solutions for MOO problems. Many techniques have been developed in operations research and other related disciplines, but the complexity of MOO problems such as large search spaces, uncertainty,
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Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Unitso a company under resource constraints. In this chapter, we first formulate this learning problem as a constrained optimization problem and then convert it to an unconstrained multi-objective optimization problem (MOP), which can be handled by some multi-objective evolutionary algorithms (MOEAs). Ho
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Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Unitiple genetic programming (GP) runs on a graphics processing unit (GPU) hardware, each with a population of five million programs both winnows (selects) useful variables from the chaff and evolves small (three inputs) data models. The SPMD CUDA interpreter exploits the GPU’s single instruction multip
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Ogier Maitreent limitations of traditional surgery and insensitivity to radiation and chemotherapy. However, in recent years, nanotechnology-based advanced techniques have been widely used for the early diagnosis and treatment of cancer. Thus, theranostic nanoparticle-based novel approaches are under extensive
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ological diseases and its treatment using targeted nano-base.This book gives a complete overview of current developments in the nano drug delivery technology in the management of neurological disorders and brain diseases. The book is divided into three main sub-sections: A) Fundamental study on nano
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Frédéric Krüger,Ogier Maitre,Santiago Jiménez,Laurent A. Baumes,Pierre Colletrugs. Several barriers present in the human body limit the overall efficiency of directly administrated therapeutics. The heterogeneous nature of these barriers across patient populations presents another crucial obstacle to designing a universal solution. The concept of precision therapeutics, whic
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Shigeyoshi Tsutsui,Noriyuki Fujimotocon is the dominant material in the production of commercial solar cells. More than 80% of world production is based on monocrystalline and polycrystalline silicon. Efficiency record commercial silicon solar panels convert about 25% of the sunlight into energy while the vast majority of conventional
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William B. Langdonfor a broad readership, including professionals, students, and researchers from materials science/engineering, polymer science, composite technology, nanotechnology, and biotechnology whose work involves nanoma978-981-13-9835-3978-981-13-9833-9Series ISSN 1869-8433 Series E-ISSN 1869-8441
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1619-7127 PGPUs. 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 algorithms978-3-662-51345-3978-3-642-37959-8Series ISSN 1619-7127 Series E-ISSN 2627-6461
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ACO with Tabu Search on GPUs for Fast Solution of the QAP are the island model and the master/slave model. As?for the island model, we used four types of topologies. Although the results of speedup depend greatly on the instances which we use, we show that the island model IM_ELMR has a good speedup feature. As for the master/slave model, we observe reaso
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Simon Harding,Julian F. Milleresource for researchers and engineers in environmental science, mechanical engineering, and chemical engineering fields, as well as for advanced undergraduate and postgraduate students..978-3-030-54710-3978-3-030-54708-0Series ISSN 1865-3529 Series E-ISSN 1865-3537
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Massively Parallel Evolutionary Computation on GPGPUs




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