"...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." (Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010). Graphics Processing Units (GPUs) are designed to be parallel - having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for many computationally-intense applications - not just for graphics. If you're facing the challenge of programming systems to effectively use these massively parallel processors to achieve efficiency and performance goals, "GPU Computing Gems" provides a wealth of tested, proven GPU techniques. Different application domains often pose similar algorithm problems, and researchers from diverse application domains often develop similar algorithmic strategies. "GPU Computing Gems" offers developers a window into diverse application areas, and the opportunity to gain insights from others' algorithm work that they may apply to their own projects. Learn from the leading researchers in parallel programming, who have gathered their solutions and experience in one volume under the guidance of expert area editors. Each chapter is written to be accessible to researchers from other domains, allowing knowledge to cross-pollinate across the GPU spectrum. "GPU Computing Gems: Emerald Edition" is the first volume in Morgan Kaufmann's "Applications of GPU Computing Series", offering the latest insights and research in computer vision, electronic design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, video and image processing. It covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more. It includes many examples that leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution. It offers insights and ideas as well as practical 'hands-on' skills you can immediately put to use.
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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
About the Author
Wen-mei W. Hwu is the Walter J. ("Jerry") Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign. From 1997 to 1999, Dr. Hwu served as the chairman of the Computer Engineering Program at the University of Illinois. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley. His research interests are in the areas of architecture, implementation, and software for high-performance computer systems. He is the director of the OpenIMPACT project, which has delivered new compiler and computer architecture technologies to the computer industry since 1987. He also serves as the Soft Systems Theme leader of the MARCO/DARPA Gigascale Silicon Research Center (GSRC) and on the Executive Committees of both the GSRC and the MARCO/DARPA Center for Circuit and System Solutions. For his contributions to the areas of compiler optimization and computer architecture, he received the 1993 Eta Kappa Nu Outstanding Young Electrical Engineer Award, the 1994 Xerox Award for Faculty Research, the 1994 University Scholar Award of the University of Illinois, the 1997 Eta Kappa Nu Holmes MacDonald Outstanding Teaching Award, the 1998 ACM SigArch Maurice Wilkes Award, the 1999 ACM Grace Murray Hopper Award, the 2001 Tau Beta Pi Daniel C. Drucker Eminent Faculty Award. He served as the Franklin Woeltge Distinguished Professor of Electrical and Computer Engineering from 2000 to 2004. He is a fellow of IEEE and ACM.
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3.4 out of 5 stars
16 reviews
20 of 24 people found the following review helpful
2.0 out of 5 stars
A missed opportunity
22 Feb 2011
By Sean
- Published on Amazon.com
Format:Hardcover|Amazon Verified Purchase
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).
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 of 5 people found the following review helpful
2.0 out of 5 stars
wide survey but not deep
19 April 2011
By E. Baxter
- Published on Amazon.com
Format:Hardcover|Amazon Vine™ Review
(What's this?)
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 of 4 people found the following review helpful
3.0 out of 5 stars
It was ok but...
21 Jun 2011
By K.Waggner
- Published on Amazon.com
Format:Hardcover|Amazon Vine™ Review
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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.
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.
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3.4 out of 5 stars
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