Accelerating MATLAB with GPU Computing

A Primer with Examples

Häftad, Engelska, 2013

713 kr

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Beskrivning

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

Produktinformation

Utgivningsdatum:2013-12-17
Mått:152 x 226 x 20 mm
Vikt:409 g
Format:Häftad
Språk:Engelska
Antal sidor:258
Förlag:Elsevier Science
ISBN:9780124080805

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Nätverk och kommunikation inom Data och IT
Informationsteknik: allmänt inom Data och IT

Mer om författaren

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.

Recensioner i media

"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

Innehållsförteckning

Preface1. Accelerating MATLAB without GPU 2. Configurations for MATLAB and CUDA 3. Optimization Planning through Profiling4. CUDA coding with C-MEX5. MATLAB with Parallel Computing Toolbox6. Using CUDA-Accelerated Libraries 7. Example in Computer Graphics: 3D Surface Reconstruction using Marching Cubes 8. Example in 3D Image Processing: Atlas-based SegmentationAPPENDIX A.1 Download and install CUDA library A.2 Installing NVIDIA Nsight into Visual Studio