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 swifter by means of leveraging the disbursed parallelism of pics Processing devices (GPUs). whereas MATLAB effectively presents high-level capabilities as a simulation instrument for speedy 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 issues akin to CUDA libraries. The authors proportion their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for big 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 booklet, they show 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 best way to speed up MATLAB codes throughout 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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