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 quicker via leveraging the disbursed parallelism of portraits Processing devices (GPUs). whereas MATLAB effectively offers high-level features as a simulation software for fast prototyping, the underlying info 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 courses clients via complicated themes comparable 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. in the course of 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 how you can speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
- Explains the comparable heritage 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