Programming Massively Parallel Processors: A Hands-on Approach (Applications of GPU Computing Series) [Paperback]

David B. Kirk (Author), Wen-mei W. Hwu (Author)

Book Description

Publication Date: February 5, 2010 | ISBN-10: 0123814723 | ISBN-13: 978-0123814722 | Edition: 1

Multi-core processors are no longer the future of computing-they are the present day reality. A typical mass-produced CPU features multiple processor cores, while a GPU (Graphics Processing Unit) may have hundreds or even thousands of cores. With the rise of multi-core architectures has come the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs.

• Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing.
• Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments.
• Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.

Editorial Reviews

Review

"For those interested in the GPU path to parallel enlightenment, this new book from David Kirk and Wen-mei Hwu is a godsend, as it introduces CUDA (tm), a C-like data parallel language, and Tesla(tm), the architecture of the current generation of NVIDIA GPUs. In addition to explaining the language and the architecture, they define the nature of data parallel problems that run well on the heterogeneous CPU-GPU hardware ... This book is a valuable addition to the recently reinvigorated parallel computing literature." - David Patterson, Director of The Parallel Computing Research Laboratory and the Pardee Professor of Computer Science, U.C. Berkeley. Co-author of Computer Architecture: A Quantitative Approach

"Written by two teaching pioneers, this book is the definitive practical reference on programming massively parallel processors--a true technological gold mine. The hands-on learning included is cutting-edge, yet very readable. This is a most rewarding read for students, engineers, and scientists interested in supercharging computational resources to solve today's and tomorrow's hardest problems." - Nicolas Pinto, MIT, NVIDIA Fellow, 2009

"I have always admired Wen-mei Hwu's and David Kirk's ability to turn complex problems into easy-to-comprehend concepts. They have done it again in this book. This joint venture of a passionate teacher and a GPU evangelizer tackles the trade-off between the simple explanation of the concepts and the in-depth analysis of the programming techniques. This is a great book to learn both massive parallel programming and CUDA." - Mateo Valero, Director, Barcelona Supercomputing Center

"The use of GPUs is having a big impact in scientific computing. David Kirk and Wen-mei Hwu's new book is an important contribution towards educating our students on the ideas and techniques of programming for massively parallel processors." - Mike Giles, Professor of Scientific Computing, University of Oxford

"This book is the most comprehensive and authoritative introduction to GPU computing yet. David Kirk and Wen-mei Hwu are the pioneers in this increasingly important field, and their insights are invaluable and fascinating. This book will be the standard reference for years to come." - Hanspeter Pfister, Harvard University

"This is a vital and much-needed text. GPU programming is growing by leaps and bounds. This new book will be very welcomed and highly useful across inter-disciplinary fields." - Shannon Steinfadt, Kent State University

"GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors." - CNNMoney.com

"This book is a valuable resource for all students from science and engineering disciplines where parallel programming skills are needed to allow solving compute-intensive problems."--BCS: The British Computer Society's online journal

From the Back Cover

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs.

See all Editorial Reviews

Product Details

• Paperback: 280 pages
• Publisher: Morgan Kaufmann; 1 edition (February 5, 2010)
• Language: English
• ISBN-10: 0123814723
• ISBN-13: 978-0123814722
• Product Dimensions: 7.5 x 0.7 x 9.2 inches
• Shipping Weight: 1.2 pounds
• Average Customer Review: 3.8 out of 5 stars  See all reviews (27 customer reviews)
• Amazon Best Sellers Rank: #501,991 in Books (See Top 100 in Books)
  • #99 in Books > Computers & Technology > Hardware > Microprocessors & System Design > Computer Design

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Most Helpful Customer Reviews

25 of 25 people found the following review helpful

4.0 out of 5 stars a little odd but good enough for first pass March 20, 2010

By Sergei Morozov

Format:Paperback|Amazon Verified Purchase

This book is a much better introduction to programming GPUs via CUDA than CUDA manual, or some presentation floating on the web. It is a little odd in coverage and language. You can tell it is written by two people with different command of English as well as passion. One co-author seems to be trying very hard to be colorful and looking for idiot-proof analogies but is prone to repetition. The other co-author sounds like a dry marketing droid sometimes. There are some mistakes in the codes in the book, but not too many since they don't dwell too long on code listings. In terms of coverage, I wish they'd cover texture memories, profiling tools, examples beyond simple matrix multiplication, and advice on computational thinking for codes with random access patterns. Chapters 6, 8, 9, and 10 are worth reading several times as they are full of practical tricks to use to trade one performance limiter for another in the quest for higher performance.

15 of 15 people found the following review helpful

4.0 out of 5 stars Great for beginners February 21, 2010

By Raymond Tay

Format:Paperback|Amazon Verified Purchase

I think this book was written with the beginner in mind - if you're new to CUDA and having issues with understanding NVIDIA's documentation on the subject then this is the book to get. The author(s) took time to clarify and solidify some of the more difficult terms to understand e.g. memory bandwidth utilization, optimizing strategies but there are shortcomings in the book and two i could think of are typos (this really an issue cos it happens to every other book i've read) and the other would be using more examples to solidify concepts and illustrating them.

In a nutshell, a great beginner's book but not a handbook sort of book.

15 of 16 people found the following review helpful

3.0 out of 5 stars A fine introductory text February 22, 2010

By Lars Bergstrom VINE™ VOICE

Format:Paperback|Amazon Verified Purchase

This book fills a nice gap between the SDK samples, technical specifications, and online course content. If you are just getting started with GPGPU computing, this book leads you smoothly through the computation model, hardware architecture, and the programming model required to take advantage of the hardware.

As others have pointed out, this is not a large book and fairly expensive. But, for the first book on the market it's surprisingly useful, effective, and readable. Definitely recommended for newcomers to the platform. Experienced GPGPU developers should only pick it up as a "hand out" for the people you need to train up, though.