000			02997nam a22003257a 4500
003			OSt
005			20231024231553.0
008			210318b ||||| |||| 00| 0 eng d
020			_a9780124080805
040			_aCvSU-CCAT Campus Library. _bEnglish. _cCvSU-CCAT Campus Library. _erda.
050			_aCIR QA 297 _bS84 2014
100			_aSuh, Jung, author. _92648
245			_aAccelerating MATLAB with GPU computing : _ba primer with examples / _cJung W. Suh, Youngmin Kim.
250			_aFirst edition.
260			_aAmsterdam : _bMorgan Kaufmann/Elsevier, _cc2014.
300			_ax, 248 pages : _billustrations ; _c23 cm
504			_aIncludes bibliographical references (pages 243-244) and index.
505			_aAccelerating MATLAB without GPU -- Configurations for MATLAB and CUDA -- Optimization planning through profiling -- CUDA coding with c-mex -- MATLAB and parallel computing toolbox -- Using CUDA-accelerated libraries -- Example in computer graphics -- CUDA conversion example : 3D image processing.
520			_a"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."--Provided by publisher.
546			_aIn English text.
650			_aMATLAB. _92590
650			_aGraphics processing units. _92649
650			_aNumerical analysis Data processing. _92650
650			_aCOMPUTERS General. _92651
650			_aMathematics. _913
650			_aElectronic books. _92652
700			_aKim, Youngmin, author. _92653
942			_cBK _eFirst edition _hQA 297 S84 2014 _kCIR _2lcc
999			_c914 _d914