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Titlebook: Parallel Scientific Computing and Optimization; Advances and Applica Raimondas ?iegis,David Henty,Julius ?ilinskas Book 2009 Springer-Verla

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書(shū)目名稱(chēng)Parallel Scientific Computing and Optimization
副標(biāo)題Advances and Applica
編輯Raimondas ?iegis,David Henty,Julius ?ilinskas
視頻videohttp://file.papertrans.cn/742/741054/741054.mp4
概述Includes 23 survey articles written by distinguished mathematicians.Presents new developments in the construction, analysis, and implementation of parallel computing algorithms.Construction and analys
叢書(shū)名稱(chēng)Springer Optimization and Its Applications
圖書(shū)封面Titlebook: Parallel Scientific Computing and Optimization; Advances and Applica Raimondas ?iegis,David Henty,Julius ?ilinskas Book 2009 Springer-Verla
描述.Parallel Scientific Computing and Optimization. introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters,?including surveys,?written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject: parallel algorithms for matrix computations, parallel optimization, management of parallel programming models and data, with the largest focus on parallel scientific computing in industrial applications...Key features include:..* construction and analysis of parallel algorithms for linear algebra and optimization problems;..* different aspects of parallel architectures, including distributed memory computers with multicore processors;..* a wide range of industrial applications: parallel simulation of flows through oil filters as well as in porous and gas media, jet aerodynamics, heat conduction in electrical cables, nonlinear optics processes in tapered lasers, and molecular and cell dynamics....This volume is intended for scientists and graduate students specializing in computer science and applied mathematics who are engaged in pa
出版日期Book 2009
關(guān)鍵詞Analysis; Matrix; algorithm; algorithms; computer science; global optimization; model; modeling; modelling; m
版次1
doihttps://doi.org/10.1007/978-0-387-09707-7
isbn_softcover978-1-4419-1884-0
isbn_ebook978-0-387-09707-7Series ISSN 1931-6828 Series E-ISSN 1931-6836
issn_series 1931-6828
copyrightSpringer-Verlag New York 2009
The information of publication is updating

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High-Performance Parallel Support Vector Machine Traininga is pre-processed in parallel to generate an approximate low-rank Cholesky decomposition. Our optimization solver then exploits the problem’s structure to perform many linear algebra operations in parallel, with relatively low data transfer between processors, resulting in excellent parallel efficiency for very-large-scale problems.
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DL_POLY_3 I/O: Analysis, Alternatives, and Future Strategiesgly more important when simulations scale to many thousands of processors and system sizes increase to many millions of particles. This study outlines the problems associated with I/O when performing large classic MD runs and shows that it is necessary to use parallel I/O methods when studying large systems.
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Mixed Mode Programming on HPCxthree mixed mode versions: Master-Only, Funneled, and Multiple. None of the mixed mode versions managed to outperform the pure MPI, mainly due to longer MPI point-to-point communication times. Results will be presented and reasons behind the performance losses discussed.
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