The explosion of the parallel computing on heterogeneous machines has significant challenges: 1) how to best manage all available resources to ensure the most efficient use of the peak performance provided by hardware; and 2) how the enormous potential of these systems can be effectively utilized by a wide spectrum of professionals, scientists and engineers who are not necessarily parallel programming experts. CUDA is a parallel computing platform and programming model that is designed to ease the development of GPU programming with a minimum learning effort for programmers familiar with standard programming languages such as C.  Professional CUDA Programming in C is geared specifically towards this audience – it is an entry-level programming book for professionals from different industries, with minimum requirements of knowledge and experiences for computer sciences, parallel computing, and C programming. The book turns complex subjects into easy-to-comprehend concepts and easy-to-follows steps. It not only teaches readers the fundamentals of parallel programming with GPUs, but also helps readers think in parallel. The book covers major CUDA features with the main focus on GPU programming skills and best practices that delivers outstanding performance, such as: CUDA Programming model GPU Execution Model GPU Memory Architecture Stream, Event and Asynchronization Multiple GPU Programing Performance Tuning Each chapter is organized around one central topic with a concise explanation, followed by workable examples to demonstrate the development process beginning with parallel computational thinking and ending with effective parallel programs.  Each chapter contains two parts: the main section and exercise section. In the main section, readers can learn the basic feature of the topic; while in the exercise section, readers have a chance to probe the limitation of hard and soft aspects of each topic, so as to let readers gain much insight on that topic from many different and practical perspectives.

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.