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 speedier through leveraging the disbursed parallelism of portraits Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities as a simulation instrument for swift 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 complex issues similar to CUDA libraries. The authors percentage their event 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. through the e-book, 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 tips on how to speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
- Explains the similar history 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