Programming Massively Parallel Processors: A Hands-on Approach shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing and optimizing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This new edition has been updated for the latest trends, incorporating the latest techniques and technologies to ensure that readers are learning the most current and relevant practices in GPU programming.
1. Introduction
Part I. Fundamental Concepts
2. Heterogeneous data parallel computing
3. Multidimensional grids and data
4. Compute architecture and scheduling
5. Memory architecture and data locality
6. Performance considerations
Part II. Parallel Patterns
7. Convolution
8. Stencil
9. Parallel histogram
10. Reduction
11. Prefix sum (scan)
12. Merge
Part III. Advanced Patterns and Applications
13. Sorting
14. Filtering (new)
15. Sparse matrix computation
16. Wavefront Algorithms (new)
17. Graph traversal
18. Deep learning
19. Multi-GPU API (new)
20. Electrostatic potential map
21. Parallel programming and computational thinking
Part IV. Advanced Practices
22. Programming a heterogeneous computing cluster
23. Advanced Optimizations for Matrix Multiplication (new)
24. Advanced practices and future evolution
25. Conclusion and outlook
Wen-mei W. Hwu is a Senior Director of Research of NVIDIA and the Sanders-AMD Endowed Chair Professor Emeritus of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His work focuses on parallel computingcovering architecture, implementation, compilers, and algorithms. Dr. Hwu has received numerous honors, including the ACM/ IEEE Eckert-Mauchly Award, ACM Grace Murray Hopper Award, IEEE B.R. Rau Award. He is an IEEE and ACM Fellow. He earned his Ph.D. in Computer Science from UC Berkele David B. Kirk is known for major contributions to graphics, hardware, and algorithms. Before pursuing his Ph.D. at Caltech, he earned B.S. and M.S. degrees in mechanical engineering from MIT and worked at Raster Technologies and Hewlett-Packards Apollo Systems Division. After completing his doctorate, he served as chief scientist and head of technology at Crystal Dynamics. In 1997, he became Chief Scientist at NVIDIA. Dr. Kirk has received numerous honors including the IEEE Seymour Cray Computer Engineering Award and ACM SIGGRAPH Computer Graphics Achievement Award. He is a member of the U.S. National Academy of Engineering. Izzat El Hajj is an Assistant Professor of Computer Science at the American University of Beirut. His research focuses on leveraging accelerator architectures to tackle challenging computations, with a focus on GPU computing, processing-in-memory, and performance modeling. He earned his Ph.D. in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He has received the Dan Vivoli Endowed Fellowship (UIUC) and the Distinguished Graduate Award from the American University of Beirut.
