大规模并行处理器程序设计(英文影印版)

书籍
计算机书籍
　　本书介绍了并行程序设计与gpu体系结构的基本概念，并详细探讨了用于构建并行程序的各种技术，用案例演示了并行程序设计的整个开发过程，即从并行计算的思想开始，直到最终实现实际且高效的并行程序。
　　 本书特点
　　 介绍了并行计算的思想，使得读者可以把这种问题的思考方式渗透到高性能并行计算中去。
　　 介绍了cuda的使用，cuda是nvidia公司专门为大规模并行环境创建的一种软件开发工具。
　　 介绍如何使用cuda编程模式和opencl来获得高性能和高可靠性。

preface
acknowledgments
dedication
chapter 1 introduction
1.1 gpus as parallel computers
1.2 architecture of a modern gpu
1.3 why more speed or parallelism?
1.4 parallel programming languages and models
1.50verarching goals
1.6 organization of the book
chapter 2 history of gpu computing
2.1 evolution of graphics pipelines
2.1.1 the era of fixed-function graphics pipelines
2.1.2 evolution of programmable real-time graphics
2.1.3 unified graphics and computing processors
2.1.4 gpgpu: an intermediate step
9.9 gpu computing
2.2.1 scalable gpus
2.2.2 recent developments
2.3 future trends

　　WHY WE WROTE THIS BOOK
　　Mass-market computing systems that combine multicore CPUs and manycore GPUs have brought terascale computing to the laptop and petascale computing to clusters. Armed with such computing power, we are at the dawn of pervasive use of computational experiments for science, engineering, health, and business disciplines. Many will be able to achieve breakthroughs in their disciplines using computational experiments that are of unprecedented level of scale, controllability, and observability. This book provides a critical ingredient for the vision: teaching parallel programming to millions of graduate and undergraduate students so that computational thinking and parallel programming skills will be as pervasive as calculus.
　　We started with a course now known as ECE498AL. During the Christmas holiday of 2006, we were frantically working on the lecture slides and lab assignments. David was working the system trying to pull the early GeForce 8800 GTX GPU cards from customer shipments to Illinois, which would not succeed until a few weeks after the semester began. It also became clear that CUDA would not become public until a few weeks after the start of the semester. We had to work out the legal agreements so that we can offer the course to students under NDA for the first few weeks.We also needed to get the words out so that students would sign up since the course was not announced until after the preenrollment period.
　　We gave our first lecture on January 16, 2007. Everything fell into place. David commuted weekly to Urbana for the class. We had 52 students, a couple more than our capacity. We had draft slides for most of the first 10 lectures. Wen-mei's graduate student, John Stratton, graciously volunteered as the teaching assistant and set up the lab. All students signed NDA so that we can proceed with the first several 'lectures until CUDA became public. We recorded the lectures but did not release them on the Web until February. We had graduate students from physics, astronomy, chemistry, electrical engineering, mechanical engineering as well as computer science and computer engineering. The enthusiasm in the room made it all worthwhile.
　　Since then, we have taught the course three times in one-semester format and two times in one-week intensive format. The ECE498AL course has become a permanent course known as ECE408 of the University of Illinois, Urbana-Champaign. We started to write up some early chapters of this book when we offered ECE498AL the second time. We tested these chapters in our spring 2009 class and our 2009 Summer School. The first four chapters were also tested in an MIT class taught by Nicolas Pinto in spring 2009. We also shared these early chapters on the web and received valuable feedback from numerous individuals. We were encouraged by the feedback we received and decided to go for a full book. Here, we humbly present our first edition to you.
　　TARGET AUDIENCE
　　The target audience of this book is graduate and undergraduate students from all science and engineering disciplines where computational thinking and parallel programming skills are needed to use pervasive terascale computing hardware to achieve breakthroughs. We assume that the reader has at least some basic C programming experience and thus are more advanced programmers, both within and outside of the field of Computer Science.We especially target computational scientists in fields such as mechanical engineering, civil engineering, electrical engineering, bioengineering, physics, and chemistry, who use computation to further their field of research.As such, these scientists are both experts in their domain as well as advanced programmers. The book takes the approach of building on basic C programming skills, to teach parallel programming in C. We use C for CUDATM, a parallel programming environment that is supported on NVIDIA GPUs, and emulated on less parallel CPUs. There are approximately 200 million of these processors in the hands of consumers and professionals, and more than 40,000 programmers actively using CUDA. The applications that you develop as part of the learning experience will be able to be run by a very large user community.
　　HOW TO USE THE BOOK
　　We would like to offer some of our experience in teaching ECE498AL using the material detailed in this book.A Three,Phased Approach
　　In ECE498AL the lectures and programming assignments are balanced with each other and organized into three phases:
　　Phase 1: One lecture based on Chapter 3 is dedicated to teaching thebasic CUDA memory/threading model, the CUDA extensions to the C language, and the basic programming/debugging tools. After the lecture, students can write a naYve parallel matrix multiplication code in a couple of hours.
　　Phase 2: The next phase is a series of 10 lectures that give students the conceptual understanding of the CUDA memory model, the CUDA threading model, GPU hardware performance features, modem computer system architecture, and the common data-parallel programming patterns needed to develop a high-performance parallel application. These lectures are based on Chapters 4 through 7. The performance of their matrix multiplication codes increases by about 10 times through this period. The students also complete assignments on convolution, vector reduction, and prefix scan through this period.
　　Phase 3: Once the students have established solid CUDA programming skills, the remaining lectures cover computational thinking, a broader range of parallel execution models, and parallel programming principles. These lectures are based on Chapters 8 through 11. (The voice and video recordings of these lectures are available on-line (http://courses.ece. illinois.edu/ece498/al).)
　　Tying It All Together:The Final Project
　　While the lectures, labs, and chapters of this book help lay the intellectual foundation for the students, what brings the learning experience together is the final project. The final project is so important to the course that it is prominently positioned in the course and commands nearly 2 months' focus. It incorporates five innovative aspects: mentoring, workshop, clinic, final report, and symposium. (While much of the information about final project is available at the ECE498AL web site (http://courses.ece.illinois. edu/ece498/al), we would like to offer the thinking that was behind the design of these aspects.)
　　Students are encouraged to base their final projects on problems that represent current challenges in the research community. To seed the process, the instructors recruit several major computational science research groups to propose problems anffserve as mentors. The mentors are asked to contribute a one-to-two-page project specification sheet that briefly describes the significance of the application, what the mentor would like to accomplish with the student teams on the application, the technical skills (particular type of Math, Physics, Chemistry courses) required to understand and work on the application, and a list of web and traditional resources that students can draw upon for technical background, general information, and building blocks, along with specific URLs or ftp paths to particular implementations and coding examples. These project specification sheets also provide students with learning experiences in defining their own research projects later in their careers. (Several examples are available at the ECE498AL course web site.)
　　Students are also encouraged to contact their potential mentors during their project selection process. Once the students and the mentors agree on a project, they enter into a close relationship, featuring frequent consultation and project reporting. We the instructors attempt to facilitate the collaborative relationship between students and their mentors, making it a very valuable experience for both mentors and students.
　　The Project Workshop
　　The main vehicle for the whole class to contribute to each other's final project ideas is the project workshop. We usually dedicate six of the lecture slots to project workshops. The workshops are designed for students'benefit. For example, if a student has identified a project, the workshop serves as a venue to present preliminary thinking, get feedback, and recruit teammates. If a student has not identified a project, he/she can simply attend the presentations, participate in the discussions, and join one of the project teams. Students are not graded during the workshops, in order to keep the atmosphere nonthreatening and enable them to focus on a meaningful dialog with the instructor(s), teaching assistants, and the rest of the class.
　　The workshop schedule is designed so the instructor(s) and teaching assistants can take some time to provide feedback to the project teams and so that students can ask questions. Presentations are limited to 10 min so there is time for feedback and questions during the class period. This limits the class size to about 36 presenters, assuming 90-min lecture slots.All presentations are preloaded into a PC in order to control the schedule strictly and maximize feedback time. Since not all students present at the'workshop, we have been able to accommodate up to 50 students in each class, with extra workshop time available as needed.