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Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 Edition von [Gaster, Benedict, Howes, Lee, Kaeli, David R., Mistry, Perhaad, Schaa, Dana]
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Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 Edition 2 , Kindle Edition

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"With parallel computing now in the mainstream, this book provides an excellent reference on the state-of-the-art techniques in accelerating applications on CPU-GPU systems. "

"-"David A. Bader, Georgia Institute of Technology

With parallel computing now in the mainstream, this book provides an excellent reference on the state-of-the-art techniques in accelerating applications on CPU-GPU systems.

-David A. Bader, Georgia Institute of Technology


Heterogeneous Computing with OpenCL, Second Edition 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. It is the first textbook that presents OpenCL programming appropriate for the classroom and is intended to support a parallel programming course. Students will come away from this text with hands-on experience and significant knowledge of the syntax and use of OpenCL to address a range of fundamental parallel algorithms.

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, Heterogeneous Computing with OpenCL explores memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. It includes detailed examples throughout, plus additional online exercises and other supporting materials that can be downloaded at

This book will appeal to software engineers, programmers, hardware engineers, and students/advanced students.

  • Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications.
  • Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more.
  • Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architectures
  • Addresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms


  • Format: Kindle Edition
  • Dateigröße: 3340 KB
  • Seitenzahl der Print-Ausgabe: 309 Seiten
  • ISBN-Quelle für Seitenzahl: 0124058949
  • Verlag: Morgan Kaufmann; Auflage: 2 (31. Dezember 2012)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00AKFSM14
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: 2.5 von 5 Sternen 2 Kundenrezensionen
  • Amazon Bestseller-Rang: #188.149 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?


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Format: Taschenbuch Verifizierter Kauf
This book represents a good step into OpenCL's way to use hardware acceleration.
Due the fact that OpenCL is not an easy library, this is my short review of what i like/dislike of this book.

What I like mostly from this book is the extensive Hardware description of GPU and CPU architectures and thread management. Probably one of the best piece of literature concerning modern hardware architectures.

What I dislike is the quality of code examples: you can find the complete list of operations at the end of each chapter without good comments. In addiction, the source code written is related to easy-to-do tasks (everything is called in main(), which is trivial), but it would be better to include more complicated and structured projects. For example, how to deal with OpenCL functions included in C++ classes?

Anyway, learning OpenCL requires a lot of extra work. So, be prepared to sweat!
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Von abear am 10. Juni 2014
Format: Taschenbuch
The book goes into describing low-level stuff without , repeatingly touching on a lot of subjects, loosing the reader between little details and repetitive code blocks, focusing on theoretical perfection instead of guiding the reader along solution-driven path. Pain to read; the same added value can be gathered by watching a few youtube presentations. What a shame when highly skilled academicians try to be 100% formal precise and forget about usefulness of the stuff they prouduce :-(. Highly disappointed.
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Die hilfreichsten Kundenrezensionen auf (beta) (Kann Kundenrezensionen aus dem "Early Reviewer Rewards"-Programm beinhalten) 3.0 von 5 Sternen 7 Rezensionen
7 von 7 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Disappointing 14. Oktober 2013
Von 4ureyz - Veröffentlicht auf
Format: Kindle Edition Verifizierter Kauf
Format of the text in Kindle Edition is sloppy. The book does not introduce concepts properly, for example it makes analogy to map-reduce withouth defining it. The sections that are supposed to introduce core concepts like execution model, context, queue, etc. at best are summary of the online reference manuals.
3.0 von 5 Sternen Looks useful but not latest version 17. Juni 2016
Von milesrf - Veröffentlicht auf
Format: Taschenbuch Verifizierter Kauf
Not for the latest OpenCL version. Otherwise, looks like it will be useful after I get started with a compatible C++ compiler.
3.0 von 5 Sternen Three Stars 8. Juli 2016
Von ROUDY44 - Veröffentlicht auf
Format: Taschenbuch Verifizierter Kauf
19 von 23 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen One Stop Overview of OpenCL at the end of 2012 11. Januar 2013
Von Robin T. Wernick - Veröffentlicht auf
Format: Taschenbuch Verifizierter Kauf
I have several books on programming GPGPUs now. I admit to being torn between OpenGL and CUDA. The two camps are quite underlapped now with little hope of seeing the languages applied evenly over the Ati and NVidia hardware. The former gap in performance has been reduced to about 10% and CUDA is not always leading; the type of parallelism brought to the computer by the task is much more defining of the overall performance than the tools at this time. Only one thing remains, how well can a programmer become efficient in writing code for either OpenCL or CUDA.

"Heterogenous Computing with OpenCL" covers all the bases well in helping a programmer become comfortable with OpenCL programming. If I have any reservations, its that the book is a short 284 page read. I wanted to see a lot more code, but the writer gives a thorough introduction and steps the reader carefully into each step of the process. My other references are not as detailed or as complete.

If you are entering this new world of parallel computing the list of new concepts and processes can be bewildering. Furthermore, the field is too new to have attracted the usual authors who know how to make the learning process seamless. The five authors of this book are PHd's in the field of parallel computing and bring their refined, and fairly narrow focus, to the text. That is why I skipped my usual breakdown of the text. This book covers too many subject areas for the in too few pages to deliver seamless development of the subject. There is a jumpy sense to the code sections perhaps because applied researchers are not the people who do the best teaching. My goal is to apply a practical bent to this largely theoretical viewpoint. I want to develop methodologies that will make it easy to divide up parallel programming seamlessly across the diverse range of GPU hardware and allow me to build a GPU library in C++ that I can apply conveniently to my parallel computing needs without having to concern myself greatly with the nature of the hardware. At this point in time that is not quite possible. All the current references divide evenly between using CUDA Thrush on NVidia products and OpenCL on AMD Ati products. Each reference takes a firm stand for one camp or the other and barely mentions the other hardware design issues. The cross platform winner is OpenCL. NVidia SDKs contain an OpenCL library for their products. Translaters such as Ocelot can convert CUDA output to OpenCL formats, but add several more computing steps. Both camps have C++ headers that allow their code to Compile through Visual Studio, however I haven't had the time to determine all the gotchas there. You will have to be your own pioneer in that area. Don't forget, Microsoft has weighed in here as well. I have sucessfully run a Microsoft AMP simulation off a trial Visual Studio 2012 IDE that produced a staggering increase of computing performance that amplified my CPU's abilities by 112 times on an MSI gaming portable with an NVidia Geforce 470M GPU. When your portable hits 355 GFlops it tends to make your hair stand on end. However, AMP and/or C++ C11 is only available on the very new and very expensive Visual Studio 2012. I'm saving my pennies up for a premium version and hitting on the tooth fairy for a very large gift. In the mean time and for work I need a much more portable solution. That drives me far into the OpenCL camp.

I have work to be used on Linux and servers. That eliminates most of my attraction for AMP. My professional access to machines shows me that NVidia cards abound in my personal programming world. My internet research shows me that NVidia has more attractive qualities( more compute units and more streaming processers per unit ) with NVidia hardware than on Ati for the dollar. For the last two years new and better GPU hardware has been delivered about three or more times each year. Despite a small influence that shows that CUDA has a somewhat higher level of programming abstraction than OpenCL, it's not enough to be a decision point. All the current GPGPU references make it easier to learn to program in OpenCL and none of them are helpful in applying their code to C++ class design. So, the world around me indicates that I should optimize my parallel computing efforts on the use of OpenCL for the near future. There won't be a better CUDA reference produced until June. Thus this reference now delivers the best approach to GPGPU computing at this time.

This is where I deliver my summary. GPGPU programming has become a computing performance solution that is driving both harware design and performance computing. There are two basic camps of thinking on this; NVidia is exclusively pushing CUDA, but doesn't travel well to other GPU cards, and OpenCL works on everything, but the references only give code and case studies that apply to Ati GPU cards. So, I'm going to remain an independent until one camp can give me a compelling reason to give up on the other. I'm going to keep on learning how to program code for both camps. But this book gives OpenCL the advantage in giving all of us the edge in learning how to benefit from this performance revolution. So, I am recommending that you either decide to start here or focus on OpenCL using this reference. If a better reference shows up, I'll review it for you.
8 von 8 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Confused and confusing 13. Oktober 2013
Von mpatter - Veröffentlicht auf
Format: Kindle Edition
This book needs to be rewritten by someone who has at least some teaching ability.

A typical passage: "When a kernel is executed, the programmer specifies the number of work-items that should be created as an
n-dimensional range (NDRange). An NDRange is a one-, two-, or three-dimensional index space of work-items that will often map to the dimensions of either the input or the output data." While this passage could serve as a summary of concepts discussed in a section, it appears before any reasonable explanation of the meaning of the keywords in the passage.

The amounting editing involved to make this book readable is well beyond what it takes to write it from scratch.

What is the most surprising is that the authors are under the misguided impression that they have written a good book.
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