Table of Contents

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     1.5 Overarching 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     2.2 GPU Computing          2.2.1 Scalable GPUs          2.2.2 Recent Developments     2.3 Future Trends Chapter 3 Introduction to CUDA     3.1 Data Parallelism     3.2 CUDA Program Structure     3.3 A Matrix–Matrix Multiplication Example     3.4 Device Memories and Data Transfer     3.5 Kernel Functions and Threading     3.6 Summary          3.6.1 Function declarations          3.6.2 Kernel launch          3.6.3 Predefined variables          3.6.4 Runtime API Chapter 4 CUDA Threads     4.1 CUDA Thread Organization     4.2 Using blockIdx and threadIdx     4.3 Synchronization and Transparent Scalability     4.4 Thread Assignment     4.5 Thread Scheduling and Latency Tolerance     4.6 Summary     4.7 Exercises Chapter 5 CUDA™ Memories     5.1 Importance of Memory Access Efficiency     5.2 CUDA Device Memory Types     5.3 A Strategy for Reducing Global Memory Traffic     5.4 Memory as a Limiting Factor to Parallelism     5.5 Summary     5.6 Exercises Chapter 6 Performance Considerations     6.1 More on Thread Execution     6.2 Global Memory Bandwidth     6.3 Dynamic Partitioning of SM Resources     6.4 Data Prefetching     6.5 Instruction Mix     6.6 Thread Granularity     6.7 Measured Performance and Summary     6.8 Exercises Chapter 7 Floating Point Considerations     7.1 Floating-Point Format          7.1.1 Normalized Representation of M       

Details

Language:
English
Imprint:
Morgan Kaufmann
Electronic ISBN:
9780123814739

About the authors

David Kirk

David B. Kirk is well recognized for his contributions to graphics hardware and algorithm research. By the time he began his studies at Caltech, he had already earned B.S. and M.S. degrees in mechanical engineering from MIT and worked as an engineer for Raster Technologies and Hewlett-Packard's Apollo Systems Division, and after receiving his doctorate, he joined Crystal Dynamics, a video-game manufacturing company, as chief scientist and head of technology. In 1997, he took the position of Chief Scientist at NVIDIA, a leader in visual computing technologies, and he is currently an NVIDIA Fellow. At NVIDIA, Kirk led graphics-technology development for some of today's most popular consumer-entertainment platforms, playing a key role in providing mass-market graphics capabilities previously available only on workstations costing hundreds of thousands of dollars. For his role in bringing high-performance graphics to personal computers, Kirk received the 2002 Computer Graphics Achievement Award from the Association for Computing Machinery and the Special Interest Group on Graphics and Interactive Technology (ACM SIGGRAPH) and, in 2006, was elected to the National Academy of Engineering, one of the highest professional distinctions for engineers. Kirk holds 50 patents and patent applications relating to graphics design and has published more than 50 articles on graphics technology, won several best-paper awards, and edited the book Graphics Gems III. A technological "evangelist" who cares deeply about education, he has supported new curriculum initiatives at Caltech and has been a frequent university lecturer and conference keynote speaker worldwide.

Wen-mei Hwu

Wen-mei W. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests are in the area of architecture, implementation, compilation, and algorithms for parallel computing. He is the chief scientist of Parallel Computing Institute and director of the IMPACT research group (www.impact.crhc.illinois.edu). He is a co-founder and CTO of MulticoreWare. For his contributions in research and teaching, he received the ACM SigArch Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the Tau Beta Pi Daniel C. Drucker Eminent Faculty Award, the ISCA Influential Paper Award, the IEEE Computer Society B. R. Rau Award and the Distinguished Alumni Award in Computer Science of the University of California, Berkeley. He is a fellow of IEEE and ACM. He directs the UIUC CUDA Center of Excellence and serves as one of the principal investigators of the NSF Blue Waters Petascale computer project. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.

Reviews

"For those interested in the GPU path to parallel enlightenment, this new book from David Kirk and Wen-mei Hwu is a godsend, as it introduces CUDA (tm), a C-like data parallel language, and Tesla(tm), the architecture of the current generation of NVIDIA GPUs. In addition to explaining the language and the architecture, they define the nature of data parallel problems that run well on the heterogeneous CPU-GPU hardware ... This book is a valuable addition to the recently reinvigorated parallel computing literature."
- David Patterson, Director of The Parallel Computing Research Laboratory and the Pardee Professor of Computer Science, U.C. Berkeley. Co-author of Computer Architecture: A Quantitative Approach

"Written by two teaching pioneers, this book is the definitive practical reference on programming massively parallel processors--a true technological gold mine. The hands-on learning included is cutting-edge, yet very readable. This is a most rewarding read for students, engineers, and scientists interested in supercharging computational resources to solve today's and tomorrow's hardest problems."
- Nicolas Pinto, MIT, NVIDIA Fellow, 2009

"I have always admired Wen-mei Hwu's and David Kirk's ability to turn complex problems into easy-to-comprehend concepts. They have done it again in this book. This joint venture of a passionate teacher and a GPU evangelizer tackles the trade-off between the simple explanation of the concepts and the in-depth analysis of the programming techniques. This is a great book to learn both massive parallel programming and CUDA."
- Mateo Valero, Director, Barcelona Supercomputing Center

"The use of GPUs is having a big impact in scientific computing. David Kirk and Wen-mei Hwu's new book is an important contribution towards educating our students on the ideas and techniques of programming for massively parallel processors.