"This book is required reading for anyone working with accelerator-based computing systems." -From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory 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…mehr
"This book is required reading for anyone working with accelerator-based computing systems."
-From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
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.
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.
Inhaltsangabe
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 Index 279
