前言
This book shows how, by harnessing the power of your computer’s graphics process unit (GPU), you can write high-performance software for a wide rangeof applications.Although originally designed to render computer graphics ona monitor (and still used for this purpose), GPUs are increasingly being calledupon for equally demanding programs in science, engineering, and finance,among other domains.We refer collectively to GPU programs that address problems in nongraphics domains as general-purpose. Happily, although you need to have some experience working in C or C++ to benefit from this book,you need not have any knowledge of computer graphics. None whatsoever! GPU programming simply offers you an opportunity to build-and to build mightily-on your existing programming skills.
To program NVlDlA GPUs to perform general-purpose computing tasks, you will want to know what CUDA is. NVlDlA GPUs are built on what’s known as the CUDA Architecture. You can think of the CUDA Architecture as the scheme by which NVlDlA has built GPUs that can perform both traditional graphics-rendering tasks and general-purpose tasks. To program CUDA GPUs, we will be using a language known as CUDA C. As you will see very early in this book.
CUDA C is essentially C with a handful of extensions to allow programming of massively parallel machines like NVIDIA GPUs.
We’ve geared CUDA by Example toward experienced C or C++ programmers who have enough familiarity with C such that they are comfortable reading and writing code in C. This book builds on your experience with C and intends to serve as an example-driven, “quick-start" guide to using NVIDlA’s CUDA C program-
ming language. By no means do you need to have done large-scale software architecture, to have written a C compiler or an operating system kernel, or to know all the ins and outs of the ANSlC standards. However, we do not spend time reviewing C syntax or common C library routines such as malloc() or memcpy ( ) , so we will assume that you are already reasonably familiar with these topics.
You will encounter some techniques that can be considered general parallel programming paradigms, although this book does not aim to teach general parallel programming techniques. Also, while we will look at nearly every part of the CUDA APl, this book does not serve as an extensive APl reference nor will it go into gory detail about every tool that you can use to help develop your CUDA C software. Consequently, we highly recommend that this book be used in conjunction with NVlDlA’s freely available documentation, in particular the NVIDIA CUDA Programming Guide and the NVIDlA CUDA Best Practices Guide. But don’t stress out about collecting all these documents because we’ll walk you through every-thing you need to do.
Without further ado, the world of programming NVlDlA GPUs with CUDA C awaits!
emmmm,这是这本书的前言，大家应该能看明白吧，我看网上推荐这本书，去网上搜了一下就下单了，然后收到发现是一本原版的，也挺好，可以收藏
标签：伟达,及英,general,will,CUDA,graphics,GPU,purpose From： https://blog.csdn.net/suy123/article/details/144870496