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Intro to Parallel Vector Sci Comput

Intro to Parallel Vector Sci Comput

Intro to Parallel Vector Sci Comput Paperback - 2006

by Ronald W. Shonkwiler

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; In this introductory text, the fundamental algorithms of numerical linear algebra are developed in a parallel context. Topics include direct and iterative methods for solving linear systems, numerical methods for the eigenvector/eigenva
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Details

  • Title Intro to Parallel Vector Sci Comput
  • Author Ronald W. Shonkwiler
  • Binding Paperback
  • Condition New
  • Pages 300
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press, New York
  • Publication date 2006-08-14
  • Features Bibliography, Index, Table of Contents
  • Bookseller's Inventory # ria9780521683371_inp
  • ISBN 9780521683371 / 0521683378
  • Weight 0.96 lbs (0.44 kg)
  • Dimensions 9.02 x 6.04 x 0.68 in (22.91 x 15.34 x 1.73 cm)
  • Category Mathematics
  • Library of Congress subjects Parallel processing (Electronic computers), Vector processing (Computer science)
  • Library of Congress Catalogue Number 2006007798
  • Dewey Decimal Code 004.35
  • Quantity available 431

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From the publisher

In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo methods.

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