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 frequently calls for that MATLAB codes run speedier by means of leveraging the disbursed parallelism of pix Processing devices (GPUs). whereas MATLAB effectively offers high-level features as a simulation software for speedy prototyping, the underlying information and information 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, establishing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complex issues corresponding to CUDA libraries. The authors percentage their event constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to higher make the most of the computational strength of GPUs, and integrating them into advertisement software program products. through the publication, they display 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 how one can speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
- Explains the comparable history on undefined, structure and programming for ease of use
- Provides easy 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