CUDA入门指南：GPU并行编程实战与Tesla架构详解

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"《大规模并行处理器程序设计》是一本深入介绍并行编程实践的指南，特别针对初学者和希望探索高性能计算的读者。该书聚焦于图形处理器（GPUs）作为并行处理的核心，以英伟达（NVIDIA）的CUDA编程语言和Tesla架构为核心内容。 CUDA是一种类似C的并行编程语言，专为GPU设计，旨在利用其众多核心的优势，提供巨大的性能提升。CUDA语言允许程序员在GPU上编写高效的并行代码，这对于那些在传统CPU上难以获得显著速度提升的问题尤其适用。书中详细讲解了CUDA的工作原理，包括如何编写、管理和调度任务，以及如何利用GPU的并行特性来加速计算密集型任务。 NVIDIA的Tesla架构代表了当前GPU技术的前沿，它拥有大量的核心，能够在诸如游戏、科学计算、深度学习等领域展现强大的并行处理能力。通过这本书，读者不仅可以学习CUDA语言，还能理解如何与CPU协同工作，实现硬件资源的有效利用。作者大卫·柯克（David Kirk）和温美惠（Wen-mei Hwu）通过两个详尽的案例研究，展示了CUDA在实际应用中的性能优势。对于初级开发者，他们提供的CUDA代码实例可以实现CPU执行速度的10到15倍提升；而对于那些对性能调优有深入理解的专家，优化后的版本甚至能带来高达105倍的加速效果。最后，书中的未来展望部分为读者揭示了并行处理器技术的潜力和发展趋势，让读者了解到如何跟上技术的步伐，并为未来的并行计算世界做好准备。这是一本既有理论深度又具有实践指导价值的书籍，对于任何希望在GPU并行编程领域取得突破的读者来说，都是一份宝贵的资源。"

The Design Document

Once the students decide on a project and form a team, they are required to

submit a design document for the project. This helps them think through the

project steps before they jump into it. The ability to do such planning will

be important to their later career success. The design document should

discuss the background and motivation for the project, application-level

objectives and potential impact, main features of the end application, an

overview of their design, an implementation plan, their performance goals,

a verification plan and acceptance test, and a project schedule.

The teaching assistants hold a project clinic for final project teams

during the week before the class symposium. This clinic helps ensure that

students are on-track and that they have identified the potential roadblocks

early in the process. Student teams are asked to come to the clinic with an

initial draft of the following three versions of their application: (1) The best

CPU sequential code in terms of performance, with SSE2 and other optimi-

zations that establish a strong serial base of the code for their speedup

comparisons; (2) The best CUDA parallel code in terms of performance.

This version is the main output of the project; (3) A version of CPU sequen-

tial code that is based on the same algorithm as version 3, using single

precision. This version is used by the students to characterize the parallel

algorithm overhead in terms of extra computations involved.

Student teams are asked to be prepared to discuss the key ideas used in

each version of the code, any floating-point precision issues, any compari-

son against previous results on the application, and the potential impact

on the field if they achieve tremendous speedup. From our experience,

the optimal schedule for the clinic is 1 week before the class symposium.

An earlier time typically results in less mature projects and less meaningful

sessions. A later time will not give students sufficient time to revise their

projects according to the feedback.

The Project Report

Students are required to submit a project report on their team’s key find-

ings. Six lecture slots are combined into a whole-day class symposium.

During the symposium, students use presentation slots proportional to the

size of the teams. During the presentation, the students highlight the best

parts of their project report for the benefit of the whole class. The presenta-

tion accounts for a significant part of students’ grades. Each student must

answer questions directed to him/her as individuals, so that different grades

can be assigned to individuals in the same team. The symposium is a major

opportunity for students to learn to produce a concise presentation that

xvPreface

Acknowledgments

We especially acknowledge Ian Buck, the father of CUDA and John

Nickolls, the lead architect of Tesla GPU Computing Architecture. Their

teams created an excellent infrastructure for this course. Ashutosh Rege and

the NVIDIA DevTech team contributed to the original slides and contents

used in ECE498AL course. Bill Bean, Simon Green, Mark Harris, Manju

Hedge, Nadeem Mohammad, Brent Oster, Peter Shirley, Eric Young, and

Cyril Zeller provided review comments and corrections to the manuscripts.

Nadeem Mohammad organized the NVIDIA review efforts and also helped

to plan Chapter 11 and Appendix B. Calisa Cole helped with cover.

Nadeem’s heroic efforts have been critical to the completion of this book.

We also thank Jensen Huang for providing a great amount of financial

and human resources for developing the course. Tony Tamasi’s team con-

tributed heavily to the review and revision of the book chapters. Jensen also

took the time to read the early drafts of the chapters and gave us valuable

feedback. David Luebke has facilitated the GPU computing resources for

the course. Jonah Alben has provided valuable insight. Michael Shebanow

and Michael Garland have given guest lectures and contributed materials.

John Stone and Sam Stone in Illinois contributed much of the base

material for the case study and OpenCL chapters. John Stratton and Chris

Rodrigues contributed some of the base material for the computational

thinking chapter. I-Jui “Ray” Sung, John Stratton, Xiao-Long Wu, Nady

Obeid contributed to the lab material and helped to revise the course material

as they volunteered to serve as teaching assistants on top of their research.

Laurie Talkington and James Hutchinson helped to dictate early lectures that

served as the base for the first five chapters. Mike Showerman helped build

two generations of GPU computing clusters for the course. Jeremy Enos

worked tirelessly to ensure that students have a stable, user-friendly GPU

computing cluster to work on their lab assignments and projects.

We acknowledge Dick Blahut who challenged us to create the course in

Illinois. His constant reminder that we needed to write the book helped

keep us going. Beth Katsinas arranged a meeting between Dick Blahut

and NVIDIA Vice President Dan Vivoli. Through that gathering, Blahut was

introduced to David and challenged David to come to Illinois and create the

course with Wen-mei.

We also thank Thom Dunning of the University of Illinois and Sharon

Glotzer of the University of Michigan, Co-Directors of the multiuniversity

Virtual School of Computational Science and Engineering, for graciously

xvii

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