Designed to facilitate the development of parallel programs In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required - just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You'll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Major topics covered include
- Parallel programming
- Thread cooperation
- Constant memory and events
- Texture memory
- Graphics interoperability
- Atomics
- Streams
- CUDA C on multiple GPUs
- Advanced atomics
Table of Contents:
- Chapter 1: Why CUDA? Why Now?
- Chapter 2: Getting Started
- Chapter 3: Introduction to CUDA C
- Chapter 4: Parallel Programming in CUDA C
- Chapter 5: Thread Cooperation
- Chapter 6: Constant Memory and Events
- Chapter 7: Texture Memory
- Chapter 8: Graphics Interoperability
- Chapter 9: Atomics
- Chapter 10: Streams
- Chapter 11: CUDA C on Multiple GPUs
- Chapter 12: The Final Countdown
- Appendix A: Advanced Atomics
- Index
About the Author :
Jason Sanders is a senior software engineer in the CUDA Platform group at NVIDIA. While at NVIDIA, he helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. Jason received his master's degree in computer science from the University of California Berkeley where he published research in GPU computing, and he holds a bachelor's degree in electrical engineering from Princeton University. Prior to joining NVIDIA, he previously held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on the CUDA Algorithms team at NVIDIA. He has more than twenty years of industry experience focused on optimising code and improving performance, including for Photoshop and Mozilla. Kandrot has worked for Adobe, Microsoft, and Google, and he has been a consultant at many companies, including Apple and Autodesk.
