By Jung W. Suh,Youngmin Kim
Beyond simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This usually calls for that MATLAB codes run quicker by way of leveraging the allotted parallelism of photographs Processing devices (GPUs). whereas MATLAB effectively presents high-level capabilities as a simulation device for fast prototyping, the underlying info and data wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs deals a primer on bridging this hole.
Starting with the fundamentals, developing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated subject matters corresponding to CUDA libraries. The authors percentage their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program products. during the booklet, they reveal many instance codes that may be used as templates of C-MEX and CUDA codes for readers’ projects. obtain instance codes from the publisher's web site: http://booksite.elsevier.com/9780124080805/
- Shows the right way to speed up MATLAB codes in the course of the GPU for parallel processing, with minimum knowledge
- Explains the similar history on undefined, structure and programming for ease of use
- Provides basic labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world projects
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Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh,Youngmin Kim
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