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CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of Gpu Computing) Paperback – November 27, 2012

ISBN-13: 978-0124159334 ISBN-10: 0124159338 Edition: 1st

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CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of Gpu Computing) + CUDA by Example: An Introduction to General-Purpose GPU Programming + CUDA Handbook: A Comprehensive Guide to GPU Programming, The
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Product Details

  • Series: Applications of Gpu Computing
  • Paperback: 600 pages
  • Publisher: Morgan Kaufmann; 1 edition (November 27, 2012)
  • Language: English
  • ISBN-10: 0124159338
  • ISBN-13: 978-0124159334
  • Product Dimensions: 1 x 7.5 x 9.1 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 3.8 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #282,599 in Books (See Top 100 in Books)

Editorial Reviews

Review

"I must mention chapters 7, which deals with the practicalities of using the SDK, and 9, which offers advice and a detailed breakdown of areas that can limit the performance of a CUDA application. Together, these chapters transform this good book into the kind of excellent text that all CUDA developers can find useful, regardless of their relative experience."--ComputingReviews.com, July 12, 2013 "This book is one of the most comprehensive on the subject published to date.it will guide those acquainted with GPU/CUDA from other books or from NVIDIA product documentation through the optimization maze to efficient CUDA/GPU coding."--ComputingReviews.com, April 25, 2013

About the Author

Shane Cook is Technical Director at CUDA Developer, a consultancy company that helps companies exploit the power of GPUs by re-engineering code to make the optimal use of the hardware available. He formed CUDA Developer upon realizing the potential of heterogeneous systems and CUDA to disrupt existing serial and parallel programming technologies. He has a degree in Applied Software Engineering, specializing in the embedded software field. He has worked in senior roles with blue chip companies over the past twenty years, always seeking to help to develop the engineers in his team. He has worked on C programming standards including the MISRA Safer C used by widely in the automotive software community, and previously developed code for companies in the Germany automotive and defense contracting industries as well as Nortel and Ford Motor Company.

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Customer Reviews

3.8 out of 5 stars
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Most Helpful Customer Reviews

33 of 34 people found the following review helpful By Alex on December 21, 2012
Format: Paperback Verified Purchase
For me that I have read a lot about GPU Programming I regard it as a necessary companion to cover a lot of aspects that Rob Farber mentions briefly. This is for the initiated and aware people. No need to say more to these people. It will be a nice addition in their collection.
For the people who are not initiated this is not the best book to start with but you should definately acquire it after you become more experienced.
The natural way to start with CUDA if you plan to be self taught is :

1. CUDA by Example: An Introduction to General-Purpose GPU Programming by Jason Sanders and Edward Kandrot
Nice introduction. It is more like playing with your GPU and admire its capabilities.

2. Programming Massively Parallel Processors, Second Edition: A Hands-on Approach by David B. Kirk and Wen-mei W. Hwu
It explains a lot of things in GPU Programming. You simply can't go without it.

3. CUDA Application Design and Development by Rob Farber
I would recommend a nice look at it. Grasp some concepts and then move to

4. CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series) by Shane Cook
I would say it will explain a lot of aspects that Farber cover with examples.

For me this is the natural way to go for a self taught. All the best of luck if you are, it is a really nice area which is becoming mature.
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11 of 12 people found the following review helpful By Armon Dadgar on December 18, 2012
Format: Paperback Verified Purchase
To put this review into context, I come from a background of formal training in computer science, and my day-to-day job is systems engineering. With that said, I'm very comfortable with both systems architecture and the C programming language. It seemed that this book was perfectly targeted for somebody like me, as it assumes an substantial existing knowledge base and doesn't slow down to cover much of the basics.

However, I do find that the manner in which the author covers things to be rather tedious. The writing style itself could use some work, and it frequently makes use of analogies that confuse rather than illuminate. The author will continuously repeat himself on several concepts, and although I agree repetition is critical to learning, it can be done so much at times that it is a hindrance. The book also doesn't necessarily follow a progression of increasing "detail". So while introducing a new concept, the author often goes down the rabbit hole of unnecessary detail and device specific optimizations, and other tangents that are not strictly critical at that time. Another slight complaint is that the CUDA spec has changed dramatically, and thus there are significant changes between CUDA 1/2/3. As a beginner, the complexity of learning something across multiple versions is also frustrating at times. I would've greatly preferred a focus on CUDA 3, and then added notes about backward compatibility in a later chapter, as opposed to covering all three simultaneously.

On the plus side, I do like that the book doesn't spend much time teaching C or basic systems architecture. There is not a lot of introductory material to get through before you get to the meat of CUDA.
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9 of 11 people found the following review helpful By P.J.Schaafsma on May 12, 2013
Format: Paperback
Do not buy this item!

My background is Master level CS, some CUDA experience, but none in a professional capacity.

The best part of these 600 pages of text is their promise: learning how to program with CUDA as an already experienced developer. But that's where the good parts end, sadly.

What stands out first and foremost is the incoherent stream of arbitrary facts and figures related to CUDA programming, nVidia GPUs, parallel programming, and profiling metrics of the author's test programs. It's all over the place! Rarely (if ever) are basic or advanced concepts clearly or concisely explained, the index is insufficient at best (best just Google it or read StackOverflow threads), and what information can be distilled has to be extracted piece by piece by the reader. A term will often be used a fair number of times before the author gets around to any sort of definition or comprehensive description. And when he does, he resorts to car analogies, supermarket queues and whatnot like his audience is completely new to programming ('imagine computer memory like shelf storage, and you're the manager of the warehouse' could very well be a phrase used in this text). The author's idea of explaining complex concepts seems to be to just use a different analogy every time a particular aspect of that concept is relevant to the current subject.

I could go on in some detail about other noticeably poor parts of this item, like its wordiness, lack of vision and structure, quality of its figures and tables ("Descriptions are for suckers!"), endless source code listings in huge font ("Got to get that page count up, or we won't be able to charge $45"), lack of overview, and much, much more, but I'll leave that to others.
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6 of 8 people found the following review helpful By Robin T. Wernick on January 20, 2013
Format: Paperback Verified Purchase
I have been looking over almost all of the books on GPGPU programming for three months now and IMHO this book is presently the best one to select for Nvidia hardware understanding and program development. "CUDA Programming" meets high standards for in depth hardware exploration and program approaches. There are almost 100 pages of code in this 550 page book that help the reader get acquainted with CUDA programming differences from standard C++.

Although this is a fairly deep read, it delivers a host of understanding about GPU hardware architectures and how they create a demand for programming a certain way that supports the high throughput potential that the new GPU cards can deliver. There are new terms to learn in this arena: Grids, Blocks, Threads, Warps,and Kernels that are related to the parallel programming paradigm. The author does a good job of explaining what they are and the how and why of their proper use. I'm sure that there will be less erratic introductions of this material in the future, but for 2012, this is the best there is in print. No doubt, you will need to reread the material three times to get it all down correctly. That is why you will need many program examples to make it all clear.

The book also covers the various programming libraries that surround the CUDA dialect of C++ and is helpful in understanding their usage. There is also an introduction to CUDA debugging tools and several program timing comparisons. The book devotes chapter 10 to the various support libraries and describes their area of support briefly. The current release release of the CUDA SDK.is version 5. This SDK contains a few dozen example programs and I believe that you will develop most of your understanding of CUDA from it.
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