CUDA by Example: An Introduction to General-Purpose GPU Programming [ペーパーバック]

"CUDA by example: an introduction to general-purpose GPU programming" is a brand new text by Jason Sanders and Edward Kandrot, senior members of NVIDIA's CUDA development team. This is basically the second introductory text to hit the market on general-purpose GPU programming, the first one being "Programming Massively Parallel Processors: A Hands-On Approach" by David Kirk and Wen-Mei Hwu.

The Good: it is not very common to find a technical book in this price range that is not simply in greyscale. Perhaps unsurprisingly for an NVIDIA book there's quite a bit of green, and this definitely enhances the reading experience. On a more substantive note: the authors really mean the "by example" part of "CUDA by example". From chapter 3 onward, all the main concepts are fleshed out by showing and dissecting lots of code -- probably more so than in Kirk & Hwu's text, which includes application case studies, but also more extensive treatments of the CUDA architecture. As with any example-based book, it is important to run and modify the programs while reading through the text. Right now there are a few hiccups with the files Sanders & Kandrot were kind enough to provide (e.g. as of this writing README.txt and license.txt do not have the appropriate permissions set), but I'm pretty sure these are just teething troubles which will disappear soon enough. The writing is cheerful (e.g. "For those readers who are more familiar with Star Trek than with weaving, a warp in this context has nothing to do with the speed of travel through space.", p. 106) and the explanations are for the most part clear, the language being pretty lucid -- once again, probably more so than in the Kirk & Hwu volume. This fact, along with the availability of lecture slides and lab materials for the latter book, points to the main difference between the two texts: Sanders & Kandrot are better-suited to a self-study of CUDA C, while the Kirk & Hwu book is more of a class textbook (and thus broader). Finally, I was pleased to see Sanders & Kandrot include a whole chapter (chapter 11) on working with multiple GPUs, a topic Kirk & Hwu relegate to a short section.

The Bad: having color is a welcome addition, but I could not understand why the authors chose to simply follow the text editor's default highlighting of keywords when they could have used color to highlight specific portions of the code. Similarly, a number of figures (e.g. Figs. 5.5 and 8.1) are described in the text as containing green, but they show up in greyscale. The book also contains quite a few minor typos, but that's normal; what's not normal is that every single section cross-reference outside the appendix is wrong (I counted 16 in total). Moving on to more consequential matters: Kirk & Hwu have a chapter on floating point topics; given that numerical computations are certainly part of general-purpose GPU programming, Sanders & Kandrot could have said more about them. On a different note, Kirk & Hwu have a chapter on the competing programming model OpenCL, while Sanders & Kandrot do not even have an index entry on it -- one might counter-argue here that they have knowingly put CUDA in the title. This brings me to my main gripe with this book: why didn't the authors just call it "CUDA C by example"? I believe the answer is connected to their ambivalence toward C++. An illustrative example: new and delete are used in host code only once in the entire volume (on p. 82 and p. 84, respectively), but when the code snippets are shown again (on pp. 86-87) new and delete have been silently replaced by malloc and free! In the case of device code, the authors do not discuss CUDA-supported C++ constructs like default parameters, namespaces, function templates, not to mention compute capability 2.0 things like function objects. (Structures with member functions do not beget C++). In a nutshell, the book contains too much C++ for people who only know C, and not enough C++ for those who actually use that language.

Despite these misgivings, I cannot ignore this book's low selling price (especially on the Kindle), its practical focus on a multitude of code listings, and the fact that its explanations are generally clear. Thus, I think it is an appropriate buy for those interested in learning about CUDA C.

Alex Gezerlis

I downloaded CUDA by Example on the Kindle and starting reading it. Sanders and Kandrot provide a nice step by step walk through of how to program with CUDA and the examples are really straight forward. It begins with the basic hello world introduction to the programming model, then dives deeper into the different API features with examples in each chapter.
I would recommend this book to anyone who wants to get started using CUDA.
(Found the source code online, not sure what the other review is about.)

This is an excellent introduction to CUDA. The prose and content are excellent: I read it cover-to-cover in a single sitting and enjoyed every page.

The authors clearly explain the basic CUDA paradigm starting with very simple code and working up to progressively more complex examples. The authors spend a considerable amount of time discussing different memory types and memory access styles, motivating when each style is appropriate. The code snippets are clean, clear and concise, providing a minimal yet complete introduction to each new language feature.

Highly recommended!

The book does not provide an HTML pointer to the source code used in the book. Edward Kandrot writes: "The Kindle version shipped a week too soon, it was supposed to ship next week when the physical book ships. Because of this, the website at NVIDIA wasn't done yet. Jason just spent the day making the website happen!

[...] is where the source code is currently located. I hope this helps. I wrote the examples to be specific for what is being covered, putting extras in the header files so as not to distract from the topic at hand. Only really works if the reader has the header files as well..."