Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. For this new edition, the authors are updating their coverage of CUDA, including the concept of unified memory, and expanding content in areas such as threads, while still retaining its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses. We are now ready to learn how to write a CUDA C program to exploit data parallelism for faster execution. CUDA C extends the popular ANSI C programming language with minimal new syntax and library functions to let programmers target heterogeneous computing systems containing both CPU cores and massively parallel GPUs.