柯克編著的《大規模並行處理器程序設計(英文版第2版)》簡介：We would like to offer some of our experience in teaching courses with this book. Since 2006, we have taught multiple types of courses: in one-semester for mat and in one-week intensive format. The original ECE498AL course has become a permanent course known as ECE408 or CS483 of the University of Illinois at Urbana-Champaign. We started to write up some early chapters of this book when we offered ECE498AL the second time. The first four chapters were also tested in an MIT class taught by Nicolas Pinto in the spring of 2009. Since then, we have used the book for numerous offerings of ECE408 as well as the VSCSE and PUMPS summer schools.

作者（柯克）結合自己多年從事並行計算課程教 學的經驗，以簡潔、直觀和實用的方式，詳細剖析了 編寫 並行程序所需的各種技術，並用豐富的案例說明了並 行程序設計的整個開發過程，即從計算機思想開 始，直到最終實現高效可行的並行程序。 與上一版相比，《大規模並行處理器程序設計( 英文版第2版)》對書中內容進行全面修訂和更新，更 加繫統地闡述並行程序設計，既介紹了 基本並行算法模式，又補充了更多的背景資料，而且 還介紹了一些新的實用編程技術和工具。具體更 新情況如下： 並行模式：新增3章並行模式方面的內容，詳細 說明了並行應用中涉及的諸多算法。 CUDA Fortran：這一章簡要介紹了針對CUDA體 繫結構的編程接口，並通過豐富的實例闡釋CUDA編程 。 OpenACC：這一章介紹了使用指令表示並行性的 開放標準，以簡化並行編程任務。 Thrust：Thrust．~CUDA C／C++之上的一個抽 像層。《大規模並行處理器程序設計(英文版第2版) 》用一章的篇幅說明了如何利用Thrust並行 模板庫以最少的編程工作來實現高性能應用。 C++AMP：微軟開發的一種編程接口，用於簡化 Windows環境中大規模並行處理編程。 NVIDlA的KepIer架構：探討了NVIDlA高性能、 節能的GPU架構的編程特性。

Preface
Acknowledgements
CHAPTER 1 Introduction
CHAPTER 2 History of GPU Computing.
CHAPTER 3 Introduction to Data Parallelism and CUDA C..
CHAPTER 4 Data-Parallel Execution Model
CHAPTER 5 CUDA Memories
CHAPTER 6 Performance Considerations
CHAPTER 7 Floating-Point Considerations
CHAPTER 8 Parallel Patterns: Convolution
CHAPTER 9 Parallel Patterns: Prefix Sum
CHAPTER 10 Parallel Patterns: Sparse Matrix-Vector Multiplication
CHAPTER 11 Application Case Study: Advanced MRI Reconstruction
CHAPTER 12 Application Case Study: Molecular Visualization and Analysis
CHAPTER 13 Parallel Programming and Computational Thinking
CHAPTER 14 An Introduction to OpenCLTM...
CHAPTER 15 Parallel Programming with OpenACC
CHAPTER 16 Thrust: A Productivity-Oriented Library for CUDA
CHAPTER 17 CUDA FORTRAN
CHAPTER 18 An Introduction to C++ AMP
CHAPTER 19 Programming a Heterogeneous Computing Cluster
CHAPTER 20 CUDA Dynamic Parallelism
CHAPTER 21 Conclusion and Future Outlook