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Programming Massively Parallel Processors: A Hands-on Approach
Get Free Ebook Programming Massively Parallel Processors: A Hands-on Approach
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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
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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.
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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 0.6 x 9.2 inches
Shipping Weight: 1.2 pounds
Average Customer Review:
3.7 out of 5 stars
32 customer reviews
Amazon Best Sellers Rank:
#714,451 in Books (See Top 100 in Books)
The book contains everything a C programmer needs to learn to be able to program NVIDIA GPUs using CUDA: the device architecture, the memory model, the execution model, and optimization techniques. Unfortunately, the book is also often infuriatingly repetitive and wordy. The authors belong to the tell-them-what-you-are-going-to-say-then-say-it-then-tell-them-what-you-said school of writing --- at the paragraph granularity. As a result, the last one or two sentences of a very great number of paragraphs are a mindless repetition of the first sentence of the same paragraph. And explanations are often far longer than they need to be, given that their target reader is a C programmer. For example, their multi-paragraph explanation of "row-major" order can be replaced with the single sentence "Just as in C; look at the picture." In another case, they provide an example of a topic using the "max" function, and then they actually go on to provide another (isomorphic) example using the "min" function! If you want to learn CUDA, you will learn it from this book, but be prepared to do a lot of skimming. Incidentally, the fact that CUDA requires a syntactic extension to C is a horrible botch.
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.
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.
The reason why I bought this book is because it has MRI example in Chapter 8. Although this specific example helps me at last, I was frustrated because when I first copy and paste the code, it didn't work. It took some time to figure out what went wrong, and there were errors in the code, not just one. I really hope this has been addressed and fixed in 2012 version.
a very interesting book written in such a manner that can readily be understood about this very complicated topic and leaving the reader with many interesting ideas to think about and many ideas to imagine to create his own computer and go beyond
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.
This book provides a very good introduction into the topic of massive multiprocessing. I didn't follow the examples because utlimately I haven't been able to use this technique in my projects, but reading the book gave me the feeling I understand the topic and would be able to put it to good use if I decided to actually use it.
I had used the "CUDA by Example: An Introduction to General-Purpose GPU Programming" book as a primer to CUDA work. Using the information from that book, my first CUDA implementation achieved some improvement in performance, but was not what I had expected. It was only after reading the information in this book that my GPU implementation became what I had hoped it would be. The information allowed me to achieve approximately a 60x improvement in the algorithm, dropping a 7 second implementation in CPU space to less than 1/8 of a second in GPU space.
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