Description
Accelerating MATLAB with GPU Computing
A Primer with Examples
Authors: Suh Jung W., Kim Youngmin
Language: EnglishSubjects for Accelerating MATLAB with GPU Computing:
Keywords
Atlas-based object segmentation; Block; C++ template library; C-mex configuration; C-mex debugging; C-mex programming; C/C++ compiler; CUDA; CUDA configuration; CUDA kernel; Codistributed array; Column-major order; Computer graphics algorithm; Distributed array; Elementwise operation; FFT; GPU; Grid; Image registration; Isosurface; Linear algebra; MATLAB profiling; Marching cubes algorithm; Matlabpool; Memory preallocation; Microsoft Visual Studio; Parallel Computing Toolbox; Parfor; Planning CU
258 p. · 15x22.8 cm · Paperback
Description
/li>Contents
/li>Readership
/li>Biography
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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/
Graduate students and researchers in a variety of fields, who need huge data processing without losing the many benefits of Matlab.
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.
- 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