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Multicore and GPU Programming: An Integrated Approach

Multicore and GPU Programming: An Integrated Approach

Multicore and GPU Programming: An Integrated Approach
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Multicore and GPU Programming: An Integrated Approach Paperback -

by Gerassimos Barlas

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Morgan Kaufmann. New.
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Details

  • Title Multicore and GPU Programming: An Integrated Approach
  • Author Gerassimos Barlas
  • Binding Paperback
  • Condition New
  • Pages 1024
  • Volumes 1
  • Language ENG
  • Publisher Morgan Kaufmann
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # BAY20-00305
  • ISBN 9780128141205 / 0128141204
  • Weight 3.44 lbs (1.56 kg)
  • Dimensions 9.2 x 7.6 x 1.6 in (23.37 x 19.30 x 4.06 cm)
  • Category Computers - General Information
  • Library of Congress subjects Parallel programming (Computer science), Graphics processing units - Programming
  • Library of Congress Catalogue Number 2022301338
  • Dewey Decimal Code 005.275
  • Quantity available 1

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Reader reviews for Multicore and GPU Programming: An Integrated Approach

From the publisher

Multicore and GPU Programming: An Integrated Approach, Second Edition offers broad coverage of key parallel computing tools, essential for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools, the book teaches the design and development of software capable of taking advantage of modern computing platforms that incorporate CPUs, GPUs and other accelerators.

Presenting material refined over more than two decades of teaching parallel computing, author Gerassimos Barlas minimizes the challenge of transitioning from sequential programming to mastering parallel platforms with multiple examples, extensive case studies, and full source code. By using this book, readers will better understand how to develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting parallel machines.

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