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Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap.
Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/
- Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge
- Explains the related background on hardware, architecture and programming for ease of use
- Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects
- Sales Rank: #1490967 in Books
- Published on: 2013-12-16
- Released on: 2013-12-02
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .61" w x 6.00" l, .90 pounds
- Binding: Paperback
- 258 pages
Review
"This truly is a practical primer. It is well written and delivers what it promises. Its main contribution is that it will assist “naive” programmers in advancing their code optimization capabilities for graphics processing units (GPUs) without any agonizing pain."--Computing Reviews,July 2 2014
"Suh and Kim show graduate students and researchers in engineering, science, and technology how to use a graphics processing unit (GPU) and the NVIDIA company's Compute Unified Device Architecture (CUDA) to process huge amounts of data without losing the many benefits of MATLAB. Readers are assumed to have at least some experience programming MATLAB, but not sufficient background in programming or computer architecture for parallelization."--ProtoView.com, February 2014
From the Back Cover
Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap.
Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/
About the Author
Jung W. Suh is a senior algorithm engineer and research scientist at KLA-Tencor. Dr. Suh received his Ph.D. from Virginia Tech in 2007 for his 3D medical image processing work. He was involved in the development of MPEG-4 and Digital Mobile Broadcasting (DMB) systems in Samsung Electronics. He was a senior scientist at HeartFlow, Inc., prior to joining KLA-Tencor. His research interests are in the fields of biomedical image processing, pattern recognition, machine learning and image/video compression. He has more than 30 journal and conference papers and 6 patents.
Youngmin Kim is a staff software engineer at Life Technologies where he has been programming in the area that requires real-time image acquisition and high-throughput image analysis. His previous works involved designing and developing software for automated microscopy and integrating imaging algorithms for real time analysis. He received his BS and MS from the University of Illinois at Urbana-Champaign in electrical engineering. Since then he developed 3D medical software at Samsung and led a software team at the startup company, prior to joining Life Technologies.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Poor follow through by author.
By Chicago Cyclist
As an experienced matlab user who is starting out in CUDA I have mixed / poor feelings about the book.
Initially I thought it was very useful but after starting to go through the examples things quickly come to a stand still. The first two examples contain errors or are lacking particular descriptions that are really required. The author had a good thought to write a blog to address errors however the blog is not maintained (last entry in 2014) and the feedback provided to users there is unsatisfactory to assist.
An example of the author's unsatisfactory reply and or lack of explanation regarding errors that are extremely difficult to overcome are found in the compile error requiring an "fPIC" flag. No mention of how to resolve this is ever made in the book and the blog simply refers to the book.
A good book would better / more comprehensive explanation of the cuda setup required to actually perform cuda in the matlab environment, and the blog would elaborate this when new cuda toolkits are released.
3 of 3 people found the following review helpful.
Good primer
By Björn Skatt
The book is just what it says - a primer with Matlab-mex-examples. If you are a Matlab programmer with some experience in C/C++, then this book takes you past the practical hurdles of downloading, setting up the system, linking your first few mex-files to CUDA (+ some open GPU-libs) and profiling the results. It helped me do this in a limited time and with very little effort. It also gets you started on the mindset and the tricks of GPU-data-crunching but for this, I'm sure there are much better books. There are some language issues (in parts of the book) and even a couple of bugs in the example code (suggesting the need for an editor?). But for the time it saves, and the information-gap it fills (Mathworks excellent documentation is focused on the Parallell Toolbox) the book is so worth the money.
1 of 1 people found the following review helpful.
Five Stars
By Vladimir Gorokhovsky
Good book
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