- Format
- Häftad (Paperback)
- Språk
- Engelska
- Antal sidor
- 312
- Utgivningsdatum
- 2010-08-04
- Upplaga
- 1
- Förlag
- ADDISON-WESLEY
- Medarbetare
- Kandrot, Edward
- Illustrationer
- Illustrations
- Dimensioner
- 230 x 188 x 17 mm
- Vikt
- Antal komponenter
- 1
- ISBN
- 9780131387683
- 522 g
CUDA by Example: An Introduction to General-Purpose GPU Programming
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Övrig information
Jason Sanders is Senior Software Engineer in the CUDA Algorithms group at NVIDIA Corporation, Inc. He received his M.S. in Computer Science from the University of California Berkeley, researching applications for GPU computing, and holds a B.S. in Electrical Engineering from Princeton University. He previously worked at ATI Technologies, Apple and Novell. Edward Kandrot is Senior Software Engineer on the CUDA Algorithms team at NVIDIA. He has more than 20 years of industry experience focused on code performance and optimization. Kandrot has consulted at many companies including Apple and Autodesk, and has worked for Google, Microsoft, Adobe, and other leading companies.
Innehållsförteckning
Foreword xiii
Preface xv
Acknowledgments xvii
About the Authors xix
Chapter 1: Why CUDA? Why Now? 1
1.1 Chapter Objectives 2
1.2 The Age of Parallel Processing 2
1.3 The Rise of GPU Computing 4
1.4 CUDA 6
1.5 Applications of CUDA 8
1.6 Chapter Review 11
Chapter 2: Getting Started 13
2.1 Chapter Objectives 14
2.2 Development Environment 14
2.3 Chapter Review 19
Chapter 3: Introduction to CUDA C 21
3.1 Chapter Objectives 22
3.2 A First Program 22
3.3 Querying Devices 27
3.4 Using Device Properties 33
3.5 Chapter Review 35
Chapter 4: Parallel Programming in CUDA C 37
4.1 Chapter Objectives 38
4.2 CUDA Parallel Programming 38
4.3 Chapter Review 57
Chapter 5: Thread Cooperation 59
5.1 Chapter Objectives 60
5.2 Splitting Parallel Blocks 60
5.3 Shared Memory and Synchronization 75
5.4 Chapter Review 94
Chapter 6: Constant Memory and Events 95
6.1 Chapter Objectives 96
6.2 Constant Memory 96
6.3 Measuring Performance with Events 108
6.4 Chapter Review 114
Chapter 7: Texture Memory 115
7.1 Chapter Objectives 116
7.2 Texture Memory Overview 116
7.3 Simulating Heat Transfer 117
7.4 Chapter Review 137
Chapter 8: Graphics Interoperability 139
8.1 Chapter Objectives 140
8.2 Graphics Interoperation 140
8.3 GPU Ripple with Graphics Interoperability 147
8.4 Heat Transfer with Graphics Interop 154
8.5 DirectX Interoperability 160
8.6 Chapter Review 161
Chapter 9: Atomics 163
9.1 Chapter Objectives 164
9.2 Compute Capability 164
9.3 Atomic Operations Overview 168
9.4 Computing Histograms 170
9.5 Chapter Review 183
Chapter 10: Streams 185
10.1 Chapter Objectives 186
10.2 Page-Locked Host Memory 186
10.3 CUDA Streams 192
10.4 Using a Single CUDA Stream 192
10.5 Using Multiple CUDA Streams 198
10.6 GPU Work Scheduling 205
10.7 Using Multiple CUDA Streams Effectively 208
10.8 Chapter Review 211
Chapter 11: CUDA C on Multiple GPUs 213
11.1 Chapter Objectives 214
11.2 Zero-Copy Host Memory 214
11.3 Using Multiple GPUs 224
11.4 Portable Pinned Memory 230
11.5 Chapter Review 235
Chapter 12: The Final Countdown 237
12.1 Chapter Objectives 238
12.2 CUDA Tools 238
12.3 Written Resources 244
12.4 Code Resources 246
12.5 Chapter Review 248
Appendix A: Advanced Atomics 249
A.1 Dot Product Revisited 250
A.2 Implementing a Hash Table 258
A.3 Appendix Review 277