CUDA工程师指南：高性能并行计算入门

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"《CUDA for Engineers：入门高性能并行计算》是一本专为工程领域设计的指南，旨在帮助读者理解并掌握CUDA技术，这是一种由NVIDIA公司开发的并行计算平台，特别适合在GPU（图形处理器）上进行高性能计算。本书涵盖了CUDA编程的基础概念，包括但不限于CUDA架构、线程组织、内存管理和优化技巧等内容。该电子书采用EPUB格式，这是一种开放且广泛接受的电子书籍标准，但不同阅读设备和应用程序对EPUB的支持可能存在差异。用户可以根据自己的偏好调整显示设置，如字体、字号、单列或双列模式，以及是否支持横屏阅读（landscape mode）。图片通常可以通过点击放大，这对于代码示例和配置展示尤为重要。书中包含大量的编程代码和配置实例，为了最大化这些元素的呈现效果，建议在单列布局、横屏模式下阅读，并将字体大小调整到最小值。此外，为了保持代码列表的打印质量，对于那些在可流动文本格式中可能影响视觉效果的代码，书中会提供“点击此处查看代码图像”的链接。通过点击，读者可以直接查看与印刷版一致的代码图片。在阅读过程中，如果需要返回到之前的页面，只需点击相应的链接或者使用电子书应用提供的导航功能。这本书不仅适合有一定计算机科学背景的工程师，也适合希望通过CUDA技术提升其应用程序性能的专业人士，无论是在游戏开发、科学计算还是机器学习等领域，都能从中获益匪浅。通过学习和实践本书内容，读者将能够理解和利用CUDA的力量，实现硬件加速，显著提高计算效率。"

Acknowledgments

The authors wish to express their thanks to a variety of people without whom

this book would never have come into existence.

Thank you to all the family members who received a bit less attention while

we were consumed by the writing of this book. Thank you to Laura Lewin and

everyone at Pearson who contributed to the editing, production, and marketing

efforts. Thank you to Nicholas Wilt (formerly at NVIDIA and currently at

Amazon), who first put us in contact with Laura and really got the ball rolling.

Thanks also to our technical reviewers Thomas Bradley of NVIDIA, Mark

Staveley of Microsoft, and Richard Rand of Cornell University, all of whom

provided helpful comments, corrections, and insights.

Thank you to the many colleagues here at the University of Washington–Seattle

Department of Mechanical Engineering who contributed via discussions

ranging from big-picture perspective down to the finest technical details. That

list includes but is not limited to Mark Ganter, Di Zhang, and Ben Weiss (who

helped create several of the figures and also provided us with some lifesaving

software to support logical tags and automated code formatting). We would

also like to thank Mechanical Engineering Department Chair Per Reinhall for

his approval for us to offer the class that helped to inspire the creation of much

of the book’s content. Additional thanks go to our colleagues David Haynor of

University of Washington Department of Radiology and William Ledoux of the

Seattle VA Hospital, whose research initiatives continue to motivate

meaningful journeys into CUDA territory.

We wish to say a special thank you to the good folks at NVIDIA, including

CEO Jen-Hsun Huang who not only had, but also acted on, a vision of what

could be accomplished by enhancing access to GPU-based parallel computing;

Chandra Cheij, Academic Programs Manager; Kimberly Powell, Director of

Higher Education and Healthcare Industries; Jon Saposhnik and Bob Crovella,

helpful and inspirational CUDA gurus; and last, but definitely not least, Jay

White, Director of Strategic Marketing, who sustains the Seattle-area GPU-

computing meet-up group and serves as our local go-to guy.

We would also like to thank all the students who had the sense of adventure to

participate in the initial CUDA-based class offerings at the University of

Washington, especially former ME graduate student Grant Marchelli (now

Download from finelybook www.finelybook.com

to know about GPU-based parallel computing to keep up with developments in

fields related to applied computation, engineering design and analysis,

simulation, machine learning, vision and imaging systems, or any number of

other computing-intensive fields. GPU-based parallel computing reduces the

time for some computing tasks by orders of magnitude, so big computations

(like training a machine learning system on a large data set) that took weeks

can now happen in hours, and moderate-sized computations (like producing a

3D contour plot) that took minutes can happen interactively in real time. And

these gains can be achieved at very reasonable costs in terms of both

developer effort and the hardware required. You need to know about CUDA

because CUDA is currently the best-supported and most accessible platform

for tapping into the power of GPU-based parallel computing.

We will also do our best to provide you with everything you need to know (and

as little as possible of what you do not need to know!) to get you to your first

direct experiences with CUDA. This book is not intended to be an

encyclopedic guide to CUDA; good books of that sort are already available.

We will provide links to such reference resources, and we hope you will

explore them as you develop a sophisticated working knowledge of CUDA.

The challenge with such guides, however, is that they assume that the reader

already knows enough about parallel computing and CUDA to make sense of

their lingo and context.

Our goal here is to present the essentials of CUDA in a clear, concise manner

without assuming specialized background or getting lost in a jungle of details.

We aim for the most direct path to meaningful hands-on experiences. You will

not need to read 100 pages of background material before actively engaging

with CUDA. We provide short appendices if you need to set up a basic CUDA-

enabled system or catch up on the essentials of C programming. Your first

hands-on engagement with CUDA that involves running some standard sample

programs comes in Chapter 1, “First Steps,” and by the end of Chapter 3,

“From Loops to Grids,” you should be running small CUDA programs you

have created yourself. The ensuing chapters present numerous complete

examples that you can build, run, and modify, as well as suggested projects to

provide additional hands-on CUDA experience. Get ready for a fast and

exciting ride, focused on what you really need to know to get up to speed with

CUDA.

Download from finelybook www.finelybook.com

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