CUDA by Example An Introduction to General-Purpose GPU Programming

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. When he’s not writing books, Jason is typically working out, playing soccer, or shooting photos.

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 optimizing 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. When not coding, he can be found playing World of Warcraft or visiting Las Vegas for the amazing food.

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

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