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资源说明：CUDA programming: a developer's guide to parallel computing with GPUs. by Shane Cook.
Over the past five years there has been a revolution in computing brought about by a company that for
successive years has emerged as one of the premier gaming hardware manufacturersdNVIDIA. With
the introduction of the CUDA (Compute Unified Device Architecture) programming language, for the
first time these hugely powerful graphics coprocessors could be used by everyday C programmers to
offload computationally expensive work. From the embedded device industry, to home users, to
supercomputers, everything has changed as a result of this.
One of the major changes in the computer software industry has been the move from serial
programming to parallel programming. Here, CUDA has produced great advances. The graphics
processor unit (GPU) by its very nature is designed for high-speed graphics, which are inherently
parallel. CUDA takes a simple model of data parallelism and incorporates it into a programming
model without the need for graphics primitives.
In fact, CUDA, unlike its predecessors, does not require any understanding or knowledge of
graphics or graphics primitives. You do not have to be a games programmer either. The CUDA
language makes the GPU look just like another programmable device.
Throughout this book I will assume readers have no prior knowledge of CUDA, or of parallel
programming. I assume they have only an existing knowledge of the C/C++ programming language.
As we progress and you become more competent with CUDA, we’ll cover more advanced topics,
taking you from a parallel unaware programmer to one who can exploit the full potential of CUDA.
For programmers already familiar with parallel programming concepts and CUDA, we’ll be
discussing in detail the architecture of the GPUs and how to get the most from each, including the latest
Fermi and Kepler hardware. Literally anyone who can program in C or C++ can program with CUDA
in a few hours given a little training. Getting from novice CUDA programmer, with a several times
speedup to 10 times–plus speedup is what you should be capable of by the end of this book.
The book is very much aimed at learning CUDA, but with a focus on performance, having first
achieved correctness. Your level of skill and understanding of writing high-performance code, especially for GPUs, will hugely benefit from this text.
This book is a practical guide to using CUDA in real applications, by real practitioners. At the same
time, however, we cover the necessary theory and background so everyone, no matter what their
background, can follow along and learn how to program in CUDA, making this book ideal for both
professionals and those studying GPUs or parallel programming.
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