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GPU Computing Gems Emerald Edition
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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
Kurzbeschreibung
Graphics processing units (GPUs) have long been a means to shift graphic-intensive computation away from the CPU. GPUs are designed to be parallel having hundreds of cores vs. 2-4 cores in traditional CPUs. Software developers are increasingly looking to GPUs for non-graphical computational-heavy operations to achieve improvements in efficiency and power consumption. This is known as general purpose computing on GPUs, called GPGPU. The challenge is learning how to program systems that effectively use these concurrent processors to achieve efficiency and performance goals. Programming professionals and advanced computer architecture and programming students need to know how to program these processors. GPU Computing Gems includes tested, proven GPGPU and CUDA techniques from the leading minds of concurrent programming, offering insights unavailable in any one volume to date. The contents cover the breadth of industry from scientific computing and engineering to artificial intelligence, with techniques including statistical and financial modeling, rendering, computer-aided design, and more.
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19 von 22 Kunden fanden die folgende Rezension hilfreich
A missed opportunity
22. Februar 2011
Format:Gebundene Ausgabe|Von Amazon bestätigter 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).
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).
4 von 4 Kunden fanden die folgende Rezension hilfreich
wide survey but not deep
19. April 2011
Format:Gebundene Ausgabe|Amazon Vine™ Rezension
(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.
3 von 3 Kunden fanden die folgende Rezension hilfreich
It was ok but...
21. Juni 2011
Format:Gebundene Ausgabe|Amazon Vine™ Rezension
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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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