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 pictures Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities 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 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 themes corresponding to CUDA libraries. The authors proportion their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to raised make the most of the computational strength of GPUs, and integrating them into advertisement software program products. in the course of 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 tips on how to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
- Explains the similar historical past 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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Extra info for Accelerating MATLAB with GPU Computing: A Primer with Examples
Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh,Youngmin Kim