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  • Gebundene Ausgabe: 886 Seiten
  • Verlag: Morgan Kaufmann (9. März 2011)
  • Sprache: Englisch
  • ISBN-10: 0123849888
  • ISBN-13: 978-0123849885
  • Größe und/oder Gewicht: 3,2 x 19,7 x 24,8 cm
  • Durchschnittliche Kundenbewertung: 4.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 257.903 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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Praise for GPU Computing Gems: Emerald Edition: "GPU computing is becoming an outstanding field in high performance computing. Due to its easiness, the CUDA approach enables programmers to take advantage of GPU-acceleration very quickly. My research in complex science as well as applications in high frequency trading benefited significantly from GPU computing. --Dr. Tobias Preis, ETH Zurich, Switzerland "This book is an important reference for everyone working on GPU/CUDA, and contains definitive work in a selection of fields. The patterns of CUDA parallelization it describes can often be adapted to applications in other fields. --Dr. Ming Ouyang, Assistant Professor - Director Visualization and Intensive Graphics Lab, University of Louisville "Diving into the world of GPU computing has never been more important these days. GPU Computing Gems: Emerald Edition takes you through the looking glass into this fascinating world. --Martin Eisemann, Computer Graphics Lab, TU Braunschweig ".an outstanding collection of vignettes of how to program GPUs for a breathtaking range of applications. --Dr. Amitabh Varshney, Director, Institute for Advanced Computer Studies, University of Maryland "The book features a useful index that might help readers mine the gems in search of a solution to a specific algorithmic problem. The index is accompanied by online resources containing source code samples-and further information-for some of the chapters. A second volume with another 30 chapters of GPGPU application reports, somewhat more focused on generic algorithms and programming techniques, is currently in the pipeline and scheduled to appear as the "Jade Edition" sometime this month."--Computing in Science and Engineering "The book is an excellent selection of important papers describing various applications of GPUs. As such, I believe it would be a valuable addition to the bookshelf of any researcher in modeling and simulation.This is not a substitute for a more detailed text on massively parallel programming...Instead, it is a nice practical addition to that text."--Computing Reviews, August 2012

Über den Autor und weitere Mitwirkende

Wen-mei Hwu: CTO of MulticoreWare, and is a professor at University of Illinois at Urbana-Champaign specializing in compiler design, computer architecture, computer microarchitecture, and parallel processing. He currently holds the Walter J. ("Jerry") Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory. He is a PI for the petascale Blue Waters system, is co-director of the Intel and Microsoft funded Universal Parallel Computing Research Center (UPCRC), and PI for the world's first NVIDIA CUDA Center of Excellence. At the Illinois Coordinated Science Lab, Dr. Hwu leads the IMPACT Research Group and is director of the OpenIMPACT project - which has delivered new compiler and computer architecture technologies to the computer industry since 1987. He previously edited GPU Computing Gems, a similar work focusing on NVIDIA CUDA.

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Format: Gebundene Ausgabe Verifizierter Kauf
Dieses Buch ist eine Sammlung von 50 wissenschaftlichen Artikeln über Erfahrungen bei der Verwendung des GPU-Computing in verschiedenen Fachgebieten.

Alle Artikel haben einen ähnlichen Aufbau: nach dem Abstract folgen die theoretischen Grundlagen, die teilweise sehr mathematisch sind. Anschließend werden die Kernel vorgestellt, die dann wiederum optimiert werden. Letztlich wird die Performance mit der CPU verglichen.

Die Autoren stellen hier Techniken vor, mit denen sie erhebliche Performance-Gewinne erreichen konnten. Für mich als CUDA-Entwickler war das an vielen Stellen interessant.

Ich kann dieses Buch aber nicht uneingeschränkt empfehlen. Eine Begeisterung für CUDA und für die naturwissenschaftlichen Algorithmen muss beim Leser vorhanden sein. Wenn man einfach nur die Optimierung mit CUDA lernen will, ist das hier nicht das richtige Buch.

P.S. Das Buch ist schon 2011 erschienen, daher sind ein paar Stellen schon veraltet. Ich bewerte es aber jetzt erst, weil ich erst jetzt alle 50 Artikel gelesen habe.
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Die hilfreichsten Kundenrezensionen auf (beta) 17 Rezensionen
21 von 25 Kunden fanden die folgende Rezension hilfreich
A missed opportunity 22. Februar 2011
Von Sean - Veröffentlicht auf
Format: Gebundene Ausgabe Verifizierter Kauf
I have to agree with H. Nguyen. This book is a missed opportunity. GPGPU computing is new for programmers and barely even known by scientists. The entries in this book don't really show sophisticated GPGPU philosophy or idioms. You won't read this and have "aha" moments. It would be nice if the text focused on advanced uses of segmented scan (the central trick in GPGPU computing) for load balancing and allocation, and helped the reader develop a toolbox for writing their own kernels. What's really needed is a GPU replacement for basic computer science texts like Sedgewick et. al. Just learning how to add up numbers, write a sort, write a sparse matrix code, etc, near peak efficiency of the device, is a great learning experience, because you learn to think with cooperative thread array logic rather than imperative logic. Until you master that, it's not possible to write efficient GPU code. I give the contributors credit for the articles, but I think the editorship made a mistake by not giving the book a clearer and more narrow focus. Hopefully there will soon be a book that tackles ten can't-live-without algorithms and covers them in very fine detail, addressing all performance aspects of the code and showing how coupled it is to device architecture.

On the other hand I'm giving the book a second star because it does let the reader know there are others using GPGPU to solve science problems, and the topics are pretty interesting, even if the implementations are not in the GPU idiom.

The best references are still the technical docs from NVIDIA and ATI (you should read both vendor's docs even if you only deal with CUDA, as extra perspective helps), the CUDA technical forum, and the handful of research papers written by good GPGPU coders (many who work at NV now).
5 von 5 Kunden fanden die folgende Rezension hilfreich
wide survey but not deep 19. April 2011
Von E. Baxter - Veröffentlicht auf
Format: Gebundene Ausgabe Vine Kundenrezension eines kostenfreien Produkts ( Was ist das? )
I use GPU computing in my own research and so was eager to get my hands on this book. The authors' introduction states that they observed that while GPUs are now used in extremely diverse circumstances, many fundamental operations easily cross disciplines. Their goal therefore is to help disseminate knowledge from one area of science to others who can learn from what has already been done. This is an admirable goal with uncertain execution in this book. The text consists of 50 chapters, each chapter written by experts in their field. I can testify to the top quality of the experts contributing here from my own field of medical imaging. The chapters are well written and their variety do give a good understanding of the breathe of applications in which GPUs are finding themselves. Unfortunately, I did not learn anything new or useful that I could apply. If you are using GPUs in your field, you probably know more than this book presents. If you don't know anything about GPUs, then this book is not a good introduction. The book's audience is unclear. If you are looking for details for graphics applications this is not your book as this focuses on scientific application. I agree with several of my colleagues when they say this book should have been a GPU programming cookbook with code examples for fundamental and common operations.
4 von 4 Kunden fanden die folgende Rezension hilfreich
It was ok but... 21. Juni 2011
Von K.Waggner - Veröffentlicht auf
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I found previous books in the GPU series really helpful, this one, not so much.
The graphics were great but not very helpful. With such a broad array of topics, I
think readers will probably benefit from only a small portion of the book.

I think GPU pro was much better. I also agree with others that this book should
have been a GPU programming cookbook with code examples for fundamental
and common operations.
2 von 2 Kunden fanden die folgende Rezension hilfreich
Good research paper overview 26. September 2011
Von Mike - Veröffentlicht auf
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This book is not for someone seeking guidance into algorithms for parallel programs or an introduction to GPU programming. The target audience is either a researcher seeking a literature survey snapshot of the use of GPUs in some high-performance computing areas or a engineering professional looking to see which universities are working in an area of interest.

The papers are very academic in style and followed a basic pattern:
1) problem outline,
2) GPU solution overview,
3) comparison of performance and
4) conclusions.

There is little coverage of openCL (chapter 34), an alternate non-proprietary CPU+GPU computing language which was a little disappointing - probably because of NVIDIA heaviliy managed content; editors, reviewers and authors. The content will age quickly as platforms (GPUs) and languages develop and university departments change. Given this I think the book would have been better published on the web where the content would keep up with that pace.
a lot of the book is still about graphics 1. Mai 2011
Von W Boudville - Veröffentlicht auf
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Hwu has assembled a grab bag of intentionally diverse numerical applications where Graphics Processing Units are used, mostly as pure computational engines. The book is intended as outreach to a broader audience that might hitherto have been disinclined to use GPUs; regarding them as relegated to mostly graphical work.

Yet somewhat ironically, about half the chapters concern graphical uses after all! Section 6 on ray tracing and rendering, Section 7 on computer vision, Section 8 on video and image processing and Section 10 on medical imaging. 23 of the 50 chapters belong to those sections. Still, even in the context of graphics, the book can be helpful. Section 10 has 11 chapters on ways to perform medical image processing by leveraging the massively parallel nature of GPUs. Since the latter was historically focused on games; certainly if you look at other texts on GPUs, this is the general impression. Even a broadening of GPU usage to the biomedical field can be salutary. Partly because a developer in that field might not be as aware of how game oriented hardware can be germane.

The other sections of the book do take us much further outside graphics. One chapter on quantum chemistry jumped straight into molecular dynamics simulations. Something that once started in physics and thanks in part to computational hardware advances, now has migrated to chemistry. I certainly did not expect to see mention of the Born-Oppenheimer approximation and Hamiltonian matrices in this book! That particular chapter reported impressive results with GPUs, but only in some situations.

Overall, that is something to keep in mind when reading the book. Even given that the editor undoubtedly selected contributed chapters that were overall positive for GPUs, you should be aware that gains can be nuanced and are not automatic. Specific analysis is required of an application.
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