David B. Kirk - Programming massively parallel processors: A hands-on approach

We are proud to introduce the second edition of Programming Massively Parallel Processors: A Hands-on Approach. Mass-market computing systems that combine multicore computer processing units (CPUs) and many-thread GPUs have brought terascale computing to laptops and petascale computing to clusters. Armed with such computing power, we are at the dawn of pervasive use of computational experiments for science, engineering, health, and business disciplines. Many will be able to achieve breakthroughs in their disciplines using computational experiments that are of an unprecedented level of scale, accuracy, controllability, and observability. This book provides a critical ingredient for the vision: teaching parallel programming to millions of graduate and undergraduate students so that computational thinking and parallel programming skills will be as pervasive as calculus.

Since the first edition came out in 2010, we have received numerous comments from our readers and instructors. Many told us about the existing features they value. Others gave us ideas about how we should expand its contents to make the book even more valuable. Furthermore, the hardware and software technology for heterogeneous parallel computing has advanced tremendously. In the hardware arena, two more generations of graphics processing unit (GPU) computing architectures, Fermi and Kepler, have been introduced since the first edition. In the software domain, CUDA 4.0 and CUDA 5.0 have allowed programmers to access the new hardware features of Fermi and Kepler. Accordingly, we added eight new chapters and completely rewrote five existing chapters.

Broadly speaking, we aim for three major improvements in the second edition while preserving the most valued features of the first edition. The first improvement is to introduce parallel programming in a more systematic way. This is done by (1) adding new . These additions are designed to remove the assumption that students are already familiar with basic parallel programming concepts. They also help to address the desire for more examples by our readers.

The second improvement is to cover practical techniques for using joint MPI-CUDA programming in a heterogeneous computing cluster. This has been a frequently requested addition by our readers. Due to the cost-effectiveness and high throughput per watt of GPUs, many high-performance computing systems now provision GPUs in each node. The new explains the conceptual framework behind the programming interfaces of these systems.

The third improvement is an introduction of new parallel programming interfaces and tools that can significantly improve the productivity of data-parallel programming. The new introduce OpenACC, Thrust, CUDA FORTRAN, and C++AMP. Instead of replicating the detailed descriptions of these tools from their user guides, we focus on the conceptual understanding of the programming problems that these tools are designed to solve.

While we made all these improvements, we also preserved the first edition features that seem to contribute to its popularity. First, we kept the book as concise as possible. While it is very tempting to keep adding material, we want to minimize the number of pages readers need to go through to learn all the key concepts. Second, we kept our explanations as intuitive as possible. While it is extremely tempting to formalize some of the concepts, especially when we cover the basic parallel algorithms, we strive to keep all our explanations intuitive and practical.