Download Ebook CUDA by Example: An Introduction to General-Purpose GPU Programming

Download Ebook CUDA by Example: An Introduction to General-Purpose GPU Programming

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CUDA by Example: An Introduction to General-Purpose GPU Programming

Download Ebook CUDA by Example: An Introduction to General-Purpose GPU Programming

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From the Back Cover

""This book is required reading for anyone working with accelerator-based computing systems.""-From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National LaboratoryCUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required-just the ability to program in a modestly extended version of C. "CUDA by Example, " written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You'll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. Major topics covered includeParallel programmingThread cooperationConstant memory and eventsTexture memoryGraphics interoperabilityAtomicsStreamsCUDA C on multiple GPUsAdvanced atomicsAdditional CUDA resourcesAll the CUDA software tools you'll need are freely available for download from NVIDIA.http: //developer.nvidia.com/object/cuda-by-example.html

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About the Author

Jason Sanders is a senior software engineer in the CUDA Platform group at NVIDIA. While at NVIDIA, he helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. Jason received his master’s degree in computer science from the University of California Berkeley where he published research in GPU computing, and he holds a bachelor’s degree in electrical engineering from Princeton University. Prior to joining NVIDIA, he previously held positions at ATI Technologies, Apple, and Novell. When he’s not writing books, Jason is typically working out, playing soccer, or shooting photos.   Edward Kandrot is a senior software engineer on the CUDA Algorithms team at NVIDIA. He has more than twenty years of industry experience focused on optimizing code and improving performance, including for Photoshop and Mozilla. Kandrot has worked for Adobe, Microsoft, and Google, and he has been a consultant at many companies, including Apple and Autodesk. When not coding, he can be found playing World of Warcraft or visiting Las Vegas for the amazing food.

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Product details

Paperback: 320 pages

Publisher: Addison-Wesley Professional; 1 edition (July 29, 2010)

Language: English

ISBN-10: 9780131387683

ISBN-13: 978-0131387683

ASIN: 0131387685

Product Dimensions:

7.4 x 0.9 x 9.1 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review:

4.2 out of 5 stars

64 customer reviews

Amazon Best Sellers Rank:

#86,545 in Books (See Top 100 in Books)

CUDA programming is often recommended as the best place to start out when learning about programming GPU's. The learning curve concerning the framework is less steep than say in OpenCL, and then you can learn about OpenCL quite easily because the concepts transfer quite easily.This book is aimed at a beginner in CUDA and the level of the explanations clearly shows that the authors are aware that it is so new to the reader that he/she will need a lot of explanations. However there are times in which I feel the style of this book is insufferable. For example there is a joke of a program which prints Hello World. This can be annoying to someone keen to see the first real example of parallel programming. The authors dont give you much help with regards to installation.Yet, I cant think of any book that can really replace this book. If you are starting out, you pretty much have to have it. It covers certain subjects that Wen-mei Hwu does not cover in his book. In fact in the latter's Coursera course he suggests that you read Sander's book for certain topics.

One of the best resources to start with CUDA.Positive aspects from a starting-out perspective: -- a small, very readable book focusing on the important parts to start with; comprehensiveness has it's merits but not when we are just starting out -- brevity does not mean superficialness; the book is very methodical, has examples when you start doing some technique, but alas, it does not improve performance; then it explains more background and help to fix the issue; sticks much better than feeding the conclusion up front -- complete working samples -- enjoyable style; one of the very very small number of books where I am able to appreciate all of the humor (and I'm fussy about this topic; have other reviews triggered by the contrived tiresome attempts to be humorous, which is the case in most tech books I've seen)Will need to read other book(s) after this one but a very good one to start with. Anyone criticizing because of the size is either missing the point or did not get the right book for his own need (through no fault of his own).

This is a very well done introductory textbook for CUDA programming. The examples are very well explained, and are general enough that you really learn the broader concepts, not just how to do the what the example does. It is written in the standard C CUDA, but does mention that there are other language implementations of CUDA. It does *not* explain how to do anything in them, or the names of any of the libraries are anything, so if you are looking to do pyCuda or MatCuda, this is not the book for you.It does a good enough job of explaining that it is easy to understand even if you have no background in parallel computing or in C. Each example is built modularly, so you can see how each section works and why they do it that way. This really is a fantastic book to begin your journey into GPU programming with NVIDIA.

Great introduction to CUDA programming. The book has fantastic, yet simple and easy to understand code examples. The authors walk you through the code in the book and explain the basics of the architecture. I'd recommend this to anyone getting their feet wet but if you're looking for a more extensive explanation and a deeper understanding I'd either look elsewhere or use this as a stepping stone to that point.

This book covers almost every subject you have to know about starting developing CUDA C applications. Together with the content provided at NVIDIA CUDA zone you can be confident using the CUDA technology. It is not a programmers guide to every function or property in the API, which you get anyways with the API installation. This book is more like a course, teaching you the basics with very good examples.

This book has been helpful for me as a first CUDA book. Never having done CUDA programming before, I like this book. The code examples are well explained and neatly set off (some syntax is even highlighted). The paper is nicely made. This does not feel like a cheap book even though it is paperback. I am about 1/3 of the way through this book so far, and my only complaint is that I have not seen how to write a Makefile to compile any example. The information on compiling is available online. The book and a Google search work well together.

A good book to begin learning about GPU programming. It nicely complements courses on Coursera (Heterogeneous Parallel ProgrammingUniversity of Illinois at Urbana-Champaign) and Udacity (Intro to Parallel Programming).I received the (c) 2011 version of the book, which fixed many typos and errors that had been present in the prior version.

As other reviewers have said, this book could have been about 1/10th the size and probably not lost a beat. Although, I can appreciate the authors desire to "go slow". I think the main problem with this book was a weak editor. I feel like if this book would have been published under OReily it would have been much better. The explanations and examples could have been cleaned up a lot. Even basic things like making sure you name your CUDA files with a ".cu" extension were omitted. I spent an entire day failing to compile one of the first examples in the book b/c I named the file "add.c" rather than "add.cu"Anyway, that said, it's still worth purchasing. There is good stuff in the book if you're patient. I'm sure future editions of this book will be much improved.Make sure to purchase "Programming Massively Parallel Processors" also. PMPP is considerably more rigorous and fills in many of the technical details. Together with the Sanford University iTunes course of the same name, "Programing Massively Parallel Processors", these two books are all you need to really get started in CUDA.

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CUDA by Example: An Introduction to General-Purpose GPU Programming PDF

CUDA by Example: An Introduction to General-Purpose GPU Programming PDF

CUDA by Example: An Introduction to General-Purpose GPU Programming PDF

CUDA by Example: An Introduction to General-Purpose GPU Programming PDF