GPU Computing with MATLAB

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Dan Doherty, MathWorks

Learn how MATLAB users can leverage NVIDIA GPUs to accelerate computationally intensive applications in areas such as image processing, signal processing, and computational finance. We show the GPU-enabled functionality in MATLAB and various add-on toolboxes, and demonstrate how you can integrate your own custom CUDA kernels into MATLAB. We also demonstrate how MATLAB supports CUDA kernel development by providing a high-level language and development environment for prototyping algorithms and incrementally developing and testing CUDA kernels.   

A wave propagation example will be used to demonstrate these capabilities and the speedups achieved through GPU computing. 

About the Presenter: Dan Doherty works as a Partner Manager at MathWorks, focusing on NVIDIA and other partners in the HPC area. Prior to working as Partner Manager, Dan was a Product Manager at MathWorks for over 7 years, focusing on MATLAB and core math and data analysis products. Dan received a B.S.E. and M.S.E. in Mechanical Engineering from the University of New Hampshire, where his research focused on prediction of cutting forces during CNC machining.

Product Focus

  • Parallel Computing Toolbox

Recorded: 22 Jul 2014