Cover for Programming Massively Parallel Processors

Programming Massively Parallel Processors

A Hands-on Approach

Book • Third Edition2017

Authors:

David B. Kirk and Wen-mei W. Hwu

Programming Massively Parallel Processors

A Hands-on Approach

Book • Third Edition2017

 

Cover for Programming Massively Parallel Processors

Authors:

David B. Kirk and Wen-mei W. Hwu

About the book

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Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architect ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Introduction

    Pages 1-18

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    Chapter 2 - Data parallel computing

    Pages 19-41

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    Chapter 3 - Scalable parallel execution

    Pages 43-69

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    Chapter 4 - Memory and data locality

    Pages 71-101

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    Chapter 5 - Performance considerations

    Pages 103-130

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    Chapter 6 - Numerical considerations

    Pages 131-147

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    Chapter 7 - Parallel patterns: convolution: An introduction to stencil computation

    Pages 149-174

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    Chapter 8 - Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms

    Pages 175-197

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    Chapter 9 - Parallel patterns—parallel histogram computation: An introduction to atomic operations and privatization

    Pages 199-214

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    Chapter 10 - Parallel patterns: sparse matrix computation: An introduction to data compression and regularization

    Pages 215-230

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    Chapter 11 - Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification

    Pages 231-256

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    Chapter 12 - Parallel patterns: graph search

    Pages 257-274

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    Chapter 13 - CUDA dynamic parallelism

    Pages 275-304

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    Chapter 14 - Application case study—non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods

    Pages 305-329

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    Chapter 15 - Application case study—molecular visualization and analysis

    Pages 331-344

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    Chapter 16 - Application case study—machine learning

    Pages 345-367

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    Chapter 17 - Parallel programming and computational thinking

    Pages 369-386

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    Chapter 18 - Programming a heterogeneous computing cluster

    Pages 387-411

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    Chapter 19 - Parallel programming with OpenACC

    Pages 413-441

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    Chapter 20 - More on CUDA and graphics processing unit computing

    Pages 443-456

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    Chapter 21 - Conclusion and outlook

    Pages 457-459

  23. Book chapterNo access

    Appendix A - An introduction to OpenCL

    Pages 461-474

  24. Book chapterNo access

    Appendix B - THRUST: a productivity-oriented library for CUDA

    Pages 475-491

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    Appendix C - CUDA Fortran

    Pages 493-513

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    Appendix D - An introduction to C++ AMP

    Pages 515-534

  27. Book chapterNo access

    Index

    Pages 535-550

About the book

Description

Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.

Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.

For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.

Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.

Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.

For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.

Key Features

  • Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing
  • Utilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environments
  • Contains new and updated case studies
  • Includes coverage of newer libraries, such as CuDNN for Deep Learning
  • Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing
  • Utilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environments
  • Contains new and updated case studies
  • Includes coverage of newer libraries, such as CuDNN for Deep Learning

Details

ISBN

978-0-12-811986-0

Language

English

Published

2017

Copyright

Copyright © 2017 Elsevier Inc. All rights reserved.

Imprint

Morgan Kaufmann

Authors

David B. Kirk

Wen-mei W. Hwu