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GPU Computing Gems Emerald Edition (Applications of GPU Computing Series)
Download PDF GPU Computing Gems Emerald Edition (Applications of GPU Computing Series)
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Review
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 "...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010
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From the Back Cover
Practical techniques straight from the leading minds in general purpose GPU research. Graphics Processing Units (GPUs) are designed to be parallel – having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for any computationally-intense operation – 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. Learn from the leading researchers in concurrent programming, who have gathered their insights and experience in one volume under the guidance of NVIDIA and GPU expert Wen-mei Hwu. Features Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more Many examples utilize NVIDIA Compute Unified Device Architecture (CUDA), the most widely-adopted GPU programming tool Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use About the Editor-in-Chief Wen-Mei Hwu Co-author of Programming Massively Parallel Processors and 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
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Product details
Series: Applications of GPU Computing Series
Hardcover: 886 pages
Publisher: Morgan Kaufmann; 1 edition (February 7, 2011)
Language: English
ISBN-10: 0123849888
ISBN-13: 978-0123849885
Product Dimensions:
7.8 x 1.2 x 9.8 inches
Shipping Weight: 3.1 pounds (View shipping rates and policies)
Average Customer Review:
3.2 out of 5 stars
15 customer reviews
Amazon Best Sellers Rank:
#933,803 in Books (See Top 100 in Books)
Interesting topics are covered, but the variety and coverage are over reaching. This is more like a book full of articles than a useful reference. Also, I believe that you can now get all of the books on the Nvidia website for free.
Bought on the author's recommendation. It appears to contain little that I currently have a use for, though. Many items I don't have a use for, though.
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).
This was the rumored successor to the GPU Gems series. I don't know if that's true. I hope not.I spend a lot of time tinkering with the GPU and sometimes I even end up using the results of these tinkerings in my day to day. The GPU Gems series have been a wonderful series that helped me understand what was possible on the GPU and where graphics is advancing. This book doesn't do any of that.I'm not really sure what the intent of this book was. There's a lot of technical information in this book, but not much of it is useful. There's a lot of complex math that I think is relevant to a small portion of people who use the GPU for GPGPU stuff. The "graphics" examples in this book are pretty a bit over the top with very little useful information. The Random Walks bit is semi interesting - but it goes into too many tangents that aren't at all relevant to why you would want to use the GPU to calculate random walks.If you do games or anything real time - this probably isn't the book for you. I think the GPU Gems and GPU Pro is where it's at.
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.
I have a long programming background with everything from micro-controller programming to SAAS. I was interested in this book as way to get an introduction to programming techniques and what problem domains GPU-based computing was helpful in.The book was not what I expected it to be. It is a large collection of graduate level papers on problems that GPU-based computing has been employed to help solve. The information about what is GPU-based programming and what approaches / techniques are used to do GPU-based computing you will have to figure out yourself from trying to understanding the underlying principles that are rarely directly explained. The book does give you a very wide range of what problems and problem domains are good to approach from a GPU-based approach.If you want to find out about parallel algorithm development/design, then this book is not a good resource.Each chapter has:1) Introduction, problem statement and context2) Core Methods3) Algorthims, implementations and evaluations4) Final evaluation5) Future directionThe chapter contents sound good but the level of detail in each part is generally not good for initial learning of concepts. Rather, if you have some background in the problem domain or GPU-based computing, then the information would be rather helpful to you.I am going to keep this book on my reference bookcase as I think it provides some useful starting points for research as well as examples of where GPU-based computing works well.
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