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Programming Massively Parallel Processors : A Hands-On Approach by Wen-mei W. Hwu and David B. Kirk (2016, Trade Paperback)

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Programming Massively Parallel Processors - Third Edition.

About this product

Product Identifiers

PublisherElsevier Science & Technology

ISBN-100128119861

ISBN-139780128119860

eBay Product ID (ePID)228608949

Product Key Features

Number of Pages576 Pages

Publication NameProgramming Massively Parallel Processors : a Hands-On Approach

LanguageEnglish

Publication Year2016

SubjectSystems Architecture / General, Systems Architecture / Distributed Systems & Computing, Programming / General, Programming / Parallel

TypeTextbook

AuthorWen-Mei W. Hwu, David B. Kirk

Subject AreaComputers

FormatTrade Paperback

Dimensions

Item Length9.2 in

Item Width7.5 in

Additional Product Features

Edition Number3

Intended AudienceCollege Audience

LCCN2016-957270

Dewey Edition23

IllustratedYes

Dewey Decimal004.35

Table Of Content1. Introduction2. Data parallel computing3. Scalable parallel execution4. Memory and data locality5. Performance considerations6. Numerical considerations7. Parallel patterns: convolution: An introduction to stencil computation8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms9. Parallel patterns--parallel histogram computation: An introduction to atomic operations and privatization10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification12. Parallel patterns: graph search13. CUDA dynamic parallelism14. Application case study--non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods15. Application case study--molecular visualization and analysis16. Application case study--machine learning17. Parallel programming and computational thinking18. Programming a heterogeneous computing cluster19. Parallel programming with OpenACC20. More on CUDA and graphics processing unit computing21. Conclusion and outlook

SynopsisProgramming 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. 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, 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.

LC Classification NumberQA76.642.K57 2016