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Showing 21-30 of 57 reviews(Verified Purchases). See all 66 reviews
on July 22, 2010
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.)
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on August 19, 2010
A great deal has been written about the various visions regarding parallel processing. In this dazzle it is often lost to the uninitiated, that parallel computing is not some distant promise, but a revolution that is well in the progress of happening. NVIDIA's CUDA platform is one, and perhaps the easiest and most affordable, of the ways to catch up and to join the front wave of this revolution.

CUDA has several components from a hardware architecture for graphics processors to a high level programming interface, implemented as a few extensions to the C language, called CUDA C. One of the main features of the CUDA project is that it makes a systematic effort to separate the programming layer from the chip architecture.

"CUDA by Example" by Sanders and Kandrot is the first book to make full use of this abstraction and to concentrate solely on the software side. As a result, it is the first text eminently suitable as a basis for an introductory course on CUDA C for students of software engineering or scientific computing. Working through the book the student, or reader, get acquinted step-by-step with most important distinguishing features of parallel programming, like need for memory sharing, event sychronization, atomic operations and isolated processing streams. All this is taught through sofware examples without the need to dwelve into the details of chip architecture. All that is required is some experience in basic C programming and an optional $200-300 "gamer" graphics board to demonstrate the real-life performance gains (no graphics programming experience is required).

The authors put a lot of thought into designing each chapter and the corresponding illustrating exaples in such a way that the reader can concentrate on just one new feature at any one time. Every chapter has typically two examples: A very basic one focusing on the newly introduced feature and a more exciting one illustrating some of the power of the new feature. E.g. in the chapter on synchronization, the basic example is provided by the scalar product of two vectors and the need to complete all termwise multiplications before adding up their products. The more dazzling example is a little graphics program, where the lack of proper synchronization manifests itself in the same scrambled screen patterns as what old CRT TV's displayed, when they had synchronization problems. My personal favorite is the very simple heat conduction example, that visually demonstrates the potential of the use of texture memory for spatially related data. It is a gem of simplicity and clarity. (It also captured my fascination because previously I was not able to figure out how to benefit from texture memory in general-purpose applications.)

The conscientious choice by the authors to write a software book for software developers implied that hardware dependent tricks had to be omitted. Specifically, code optimizations, that depend on specifics of various generations of chip designs, are not covered. This, as every compromise, has two sides. The positive one is that the knowledge acquired by working through the book will not become outdated, when a new generation of graphics boards hits the market. It also leaves the door open for other potential authors to write a second book on CUDA optimizations.

In summary, "CUDA by Example" is an excellent and very welcome introductory text to parallel programming for non-ECE majors. It is very systematic, well tought-out and gradual. It goes beyond demonstrating the ease-of-use and the power of CUDA C; it also introduces the reader to the features and benefits of parallel computing in general. Perhaps a more fitting title could have been "An Introduction to Parallel Programming through CUDA-C Examples". I plan to use this book as the text for the first half of a graduate course on parallel computing for data analysis and quantitative finance.
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on October 29, 2013
This is how programming books should be. Anyone familiar with "Learning Python the Hard Way" will understand the benefits of walking you through lots of code and explaining what each piece does. The repetition of rewriting code, making mistakes and digging in to find your errors is a great way to learn and this book does a fantastic job of it.
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on June 24, 2017
Very good and helpful.
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on November 19, 2016
Quite well done by people from the factory.
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on September 2, 2014
I read it cover-to-cover. I enjoyed it all.

I gained enough understanding that I think I could organize a continuous work queue for the GPU to work from and a few tricks to make it faster when I finish.
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on June 17, 2016
Looks useful once I get started with a compatible C or C++ compiler.
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on July 18, 2011
Great book, the examples are clear and easy to follow, and they address many major topics in efficient parallel code and memory management as well as familiarizing the reader with the conventions of the CUDA API. If you have been frustrated with the online tutorials that currently are available for CUDA, this is a much more rewarding place to start.
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on July 2, 2015
helpful to understand the basiscs of CUDA but the code is really outdated, you will have a hard time making it work, specially in VS.
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on November 4, 2011
This book bypasses the hardware orientation of the Nvidia litterature. You get into software quickly and directly. At the end of the book you actually know something about the subject material. I used Cuda by Example as my introduction to GPU programming. It was excellent!
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