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 through leveraging the allotted parallelism of images Processing devices (GPUs). whereas MATLAB effectively presents high-level services as a simulation device for quick prototyping, the underlying information and data wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs bargains a primer on bridging this hole.
Starting with the fundamentals, constructing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then publications clients via complicated subject matters equivalent to CUDA libraries. The authors proportion their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, editing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program products. during the ebook, they exhibit 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 site: http://booksite.elsevier.com/9780124080805/
- Shows the right way to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
- Explains the comparable historical past on undefined, structure and programming for ease of use
- Provides uncomplicated 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