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This page contains a list of books that might be used as textbooks for a course on GPU Computing


'''Heterogeneous Computing with OpenCL[http://www.elsevierdirect.com/product.jsp?isbn=9780123877666]<br>
By Benedict Gaster, Lee Howes, David R. Kaeli, Perhaad Mistry, Dana Schaa <br>
Expected Release Date: Aug 2011'''

Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future.

Written by leaders in the parallel computing and OpenCL communities, this book will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms. The authors explore memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. Intended to support a parallel programming course, Heterogeneous Computing with OpenCL includes detailed examples throughout, plus additional online exercises and other supporting materials.



'''Programming Massively Parallel Processors: A Hands-on Approach[http://www.elsevierdirect.com/companion.jsp?ISBN=9780123814722]<br>
By David Kirk and Wen-Mei Hwu <br>
Release Date: Feb 2010'''

Multi-core processors are no longer the future of computing-they are the present day reality. A typical mass-produced CPU features multiple processor cores, while a GPU (Graphics Processing Unit) may have hundreds or even thousands of cores. With the rise of multi-core architectures has come the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

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.



'''CUDA by Example: An Introduction to General-Purpose GPU Programming[http://developer.nvidia.com/cuda-example-introduction-general-purpose-gpu-programming]<br>
By Jason Sanders and Edward Kandrot <br>
Release Date: Jul 2010'''

CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.

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