

Jason Sanders is Senior Software Engineer in the CUDA Algorithms group at NVIDIA Corporation, Inc.
Meer over de auteursCUDA by Example
An introduction to General-Purpose GPU Programming
Samenvatting
CUDA is a computing architecture 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
- Additional CUDA resources
All the CUDA software tools you'll need are freely available for download from NVIDIA.
Specificaties
Over Edward Kandrot
Inhoudsopgave
Preface
Acknowledgments
About the Authors
1. Why CUDA? Why Now?
1.1 Chapter Objectives
1.2 The Age of Parallel Processing
1.3 The Rise of GPU Computing
1.4 CUDA
1.5 Applications of CUDA
1.6 Chapter Review
2. Getting Started
2.1 Chapter Objectives
2.2 Development Environment
2.3 Chapter Review
3. Introduction to CUDA C
3.1 Chapter Objectives
3.2 A First Program
3.3 Querying Devices
3.4 Using Device Properties
3.5 Chapter Review
4. Parallel Programming in CUDA C
4.1 Chapter Objectives
4.2 CUDA Parallel Programming
4.3 Chapter Review
5. Thread Cooperation
5.1 Chapter Objectives
5.2 Splitting Parallel Blocks
5.3 Shared Memory and Synchronization
5.4 Chapter Review
6. Constant Memory and Events
6.1 Chapter Objectives
6.2 Constant Memory
6.3 Measuring Performance with Events
6.4 Chapter Review
7. Texture Memory
7.1 Chapter Objectives
7.2 Texture Memory Overview
7.3 Simulating Heat Transfer
7.4 Chapter Review
8. Graphics Interoperability
8.1 Chapter Objectives
8.2 Graphics Interoperation
8.3 GPU Ripple with Graphics Interoperability
8.4 Heat Transfer with Graphics Interop
8.5 DirectX Interoperability
8.6 Chapter Review
9. Atomics
9.1 Chapter Objectives
9.2 Compute Capability
9.3 Atomic Operations Overview
9.4 Computing Histograms
9.5 Chapter Review
10. Streams
10.1 Chapter Objectives
10.2 Page-Locked Host Memory
10.3 CUDA Streams
10.4 Using a Single CUDA Stream
10.5 Using Multiple CUDA Streams
10.6 GPU Work Scheduling
10.7 Using Multiple CUDA Streams Effectively
10.8 Chapter Review
11. CUDA C on Multiple GPUs
11.1 Chapter Objectives
11.2 Zero-Copy Host Memory
11.3 Using Multiple GPUs
11.4 Portable Pinned Memory
11.5 Chapter Review
12. The Final Countdown
12.1 Chapter Objectives
12.2 CUDA Tools
12.3 Written Resources
12.4 Code Resources
12.5 Chapter Review
Appendix A: Advanced Atomics
A.1: Dot Product Revisited
A.2: Implementing a Hash Table
A.3: Appendix Review
Index
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