Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide

Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming.

Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including:

• CUDA Programming Model
• GPU Execution Model
• GPU Memory model
• Streams, Event and Concurrency
• Multi-GPU Programming
• CUDA Domain-Specific Libraries
• Profiling and Performance Tuning

The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.

Table of Contents

Chapter 1 Heterogeneous Parallel Computing with CUDA

Chapter 2 CUDA Programming Model

Chapter 3 CUDA Execution Model

Chapter 4 Global Memory

Chapter 5 Shared Memory and Constant Memory

Chapter 6 Streams and Concurrency

Chapter 7 Tuning Instruction-l evel Primitives

Chapter 8 GPU-Accelerated CUDA libraries and OpenACC

Chapter 9 Multi-GPU Programming

Chapter 1 0 Implementation Considerations

About the Authors

John Cheng, PHD, is a Research Scientist at BGP International in Houston. He has developed seismic imaging products with GPU technology and many high-performance parallel production applications on heterogeneous computing-platforms.

Max Grossman is an expert in GPU computing with experience applying CUDA to problems in medical imaging, machine learning, geophysics, and more.

Ty McKercher has been helping customers adopt GPU acceleration technologies while he has been employed at NVIDIA since 2008.