Programming Massively Parallel Processors

Programming Massively Parallel Processors

Programming Massively Parallel Processors

Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Programming Massively Parallel Processors

This groundbreaking textbook teaches readers how to program massively parallel processors to achieve high performance, and the approach does not require a great deal of hardware expertise. The presentation focuses on computational thinking techniques that enable readers to think about problems in ways that are amenable to parallel computing. Students will learn to complete a suite of API programming tools and techniques at least once, so that they will be able to apply the experience to other APIs and other tools in the future. This book teaches parallel programming for correct functionality and dependability, which constitute a subtle issue in parallel computing. Those who have worked on parallel systems in the past know that achieving initial performance is not enough. The challenge is to achieve it in such a way that you can later debug the code, reproduce the bugs when they reappear, and support the code. This book shows that with the CUDA programming model, which focuses on data parallelism, one can achieve both high-performance and high-reliability in their applications.