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.