Thinking and Coding in Parallel
Richard E. Ansorge · Cambridge University Press eBooks · 2022
Chapter 2 gives a more formal account of the ideas introduced in Chapter 1. We discuss the requirements for writing CUDA kernel code and explain the syntax in detail. We encourage the reader to start thinking in parallel by introducing some key coding ideas including methods for summing a large number of values in parallel for so-called reduction operations. This chapter also introduces GPU shared memory, illustrated with a tiled matrix multiplication example. We demonstrate how the __restrict keyword applied to kernel pointer arguments can speed up your code. In some sense this is our most conventional chapter for a book on CUDA, and the reduction operation is revisited in a number of later chapters to help introduce new CUDA features. However, many of our other examples go well beyond what you can find elsewhere.