In this webinar, you’ll see 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. Product demonstrations will highlight how MATLAB can be used to:
You will see how MATLAB reduces the amount of code required for evaluating and testing kernels compared with lower level languages such as C or Fortran. You will also see how the GPU-enabled functionality in MATLAB lets you take advantage of GPU computing without having to write CUDA kernels or learn low-level GPU computing libraries.
Previous knowledge of MATLAB is not required for this webinar.
About the Presenters:
Dan Doherty, MathWorks
Dan 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 5 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.
Jonathan Bentz, NVIDIA
Jonathan Bentz is a Solution Architect with NVIDIA, focusing on Higher Education and Research customers. Prior to NVIDIA Jonathan worked for Cray as a software engineer in the Scientific Libraries group working on dense linear algebra and FFT software. Jonathan obtained a PhD in physical chemistry and an MS in computer science from Iowa State University.