–Professor Pat Hanrahan, Stanford University
OpenCL Programming Guideby Aaftab Munshi
Using the new OpenCL (Open Computing Language) standard, you can write applications that access all available programming resources: CPUs, GPUs, and other processors such as DSPs and the Cell/B.E. processor. Already implemented by Apple, AMD, Intel, IBM, NVIDIA, and other leaders, OpenCL has outstanding potential for PCs, servers, handheld/embedded devices,
Using the new OpenCL (Open Computing Language) standard, you can write applications that access all available programming resources: CPUs, GPUs, and other processors such as DSPs and the Cell/B.E. processor. Already implemented by Apple, AMD, Intel, IBM, NVIDIA, and other leaders, OpenCL has outstanding potential for PCs, servers, handheld/embedded devices, high performance computing, and even cloud systems. This is the first comprehensive, authoritative, and practical guide to OpenCL 1.1 specifically for working developers and software architects.
Written by five leading OpenCL authorities, OpenCL Programming Guide covers the entire specification. It reviews key use cases, shows how OpenCL can express a wide range of parallel algorithms, and offers complete reference material on both the API and OpenCL C programming language.
Through complete case studies and downloadable code examples, the authors show how to write complex parallel programs that decompose workloads across many different devices. They also present all the essentials of OpenCL software performance optimization, including probing and adapting to hardware. Coverage includes
• Understanding OpenCL’s architecture, concepts, terminology, goals, and rationale • Programming with OpenCL C and the runtime API • Using buffers, sub-buffers, images, samplers, and events • Sharing and synchronizing data with OpenGL and Microsoft’s Direct3D • Simplifying development with the C++ Wrapper API • Using OpenCL Embedded Profiles to support devices ranging from cellphones to supercomputer nodes • Case studies dealing with physics simulation; image and signal processing, such as image histograms, edge detection filters, Fast Fourier Transforms, and optical flow; math libraries, such as matrix multiplication and high-performance sparse matrix multiplication; and more • Source code for this book is available
Meet the Author
Aaftab Munshi is the spec editor for the OpenGL ES 1.1, OpenGL ES 2.0, and OpenCL specifications and coauthor of the book OpenGL ES 2.0 Programming Guide (with Dan Ginsburg and Dave Shreiner, published by Addison-Wesley, 2008). He currently works at Apple.
Benedict R. Gaster is a software architect working on programming models for next-generation heterogeneous processors, in particular looking at high-level abstractions for parallel programming on the emerging class of processors that contain both CPUs and accelerators such as GPUs. Benedict has contributed extensively to the OpenCL’s design and has represented AMD at the Khronos Group open standard consortium. Benedict has a Ph.D. in computer science for his work on type systems for extensible records and variants. He has been working at AMD since 2008.
Timothy G. Mattson is an old-fashioned parallel programmer, having started in the mid-eighties with the Caltech Cosmic Cube and continuing to the present. Along the way, he has worked with most classes of parallel computers (vector supercomputers, SMP, VLIW, NUMA, MPP, clusters, and many-core processors). Tim has published extensively, including the books Patterns for Parallel Programming (with Beverly Sanders and Berna Massingill, published by Addison-Wesley, 2004) and An Introduction to Concurrency in Programming Languages (with Matthew J. Sottile and Craig E. Rasmussen, published by CRC Press, 2009). Tim has a Ph.D. in chemistry for his work on molecular scattering theory. He has been working at Intel since 1993.
James Fung has been developing computer vision on the GPU as it progressed from graphics to general-purpose computation. James has a Ph.D. in electrical and computer engineering from the University of Toronto and numerous IEEE and ACM publications in the areas of parallel GPU Computer Vision and Mediated Reality. He is currently a Developer Technology Engineer at NVIDIA, where he examines computer vision and image processing on graphics hardware.
Dan Ginsburg currently works at Children’s Hospital Boston as a Principal Software Architect in the Fetal-Neonatal Neuroimaging and Development Science Center, where he uses OpenCL for accelerating neuroimaging algorithms. Previously, he worked for Still River Systems developing GPU-accelerated image registration software for the Monarch 250 proton beam radiotherapy system. Dan was also Senior Member of Technical Staff at AMD, where he worked for over eight years in a variety of roles, including developing OpenGL drivers, creating desktop and hand-held 3D demos, and leading the development of handheld GPU developer tools. Dan holds a B.S. in computer science from Worcester Polytechnic Institute and an M.B.A. from Bentley University.
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OpenCL Programming Guide is the 2nd book (to my awareness) being published, which deals with the new and exciting standard by the KHRONOS Group: OpenCL. The goal of this book is to provide the reader with an extensive walkthrough of the standard, providing explanations to complement the standard's specs. The authors of the book dim it "a pragmatic guide for people interested in writing code", and that it is. The book is at its first edition, and it shows. Throughout the book there are typos, and what can only be explained as 'copy & paste' originated mistakes. Some of the code samples contain generic errors such as memory leaks or incorrect remarks, and some of the figures simply do not convey the intended concept, or are erroneous. The majority of errata I personally reported dealt with these types of errors, which are arguably acceptable (for a first edition) as they are not regarding the focus of the book, however, the book also contains some errata that does touch the actual focus, like an incorrect explanation (e.g. reported issue #14 on pg.132, and reported issue #4 on pg.65), or incorrect usage of returned information (e.g. reported issue #8 on page 88). On the other hand, the book does provide good insight on a vast portion of the standard. Although it claims to cover the entire spec, the level of this coverage is inconsistent and in some aspects completely lacking (e.g. the explanation of clEnqueueTask() could have been accompanied with a concise example, but in turn ended up as a short sub-section). On the portions with most interest, i.e., OpenCL's support for data-parallel algorithms, the book does provide extended information, and adds to the OpenCL specs, by clarifying the concepts. The 2nd part of the book, which was added rather close to the final release of the book (from the eyes of a SafariBooksOnline RoughCuts reader), provides 9 case studies of OpenCL usage. Some of these are purely pedagogic (e.g. chapter 15), but some provide more real-world examples of how OpenCL can be used, and optimized (especially for a GPU). These add another dimension to the book, and contribute to its relevancy. On a closing note, I do think that the book is worth the while. It is currently the best option besides reading the specs, to learn the OpenCL APIs and OpenCL C programming language, and despite the shortcomings I've mentioned, it does manage to provide the gist of OpenCL, and add insight to the standard. ----------------------------- Proper disclosure: OpenCL and GPU programming is what I do for a living.
Chloe sat up, getting her things together so she could leave.