Do you have data that is too large to fit into available memory? Or perhaps you would like to speed up data analysis tasks using additional hardware such as additional CPUs or GPUs? In this webinar, you will learn techniques for working with large data in MATLAB^® and approaches to speeding up your analyses using parallel computing and GPUs. Through an example seismic analysis case study we will show you how to:

• Work with data that is too large to fit in available memory on a single machine
• Perform large data analysis computations on a computer cluster (we will use a cluster running 64 MATLAB Distributed Computing Server workers)
• Introduce GPU computing for speeding up solutions of the wave equation for seismic analysis

About the Presenter: Stuart Kozola is a product manager at bat365 and focuses on MATLAB^® and add-on products for data analysis, mathematical modeling, and computational finance. Prior to joining bat365 in 2006, Stuart worked at Pratt & Whitney (United Technologies) as a design engineer working on combustion systems for gas turbine engines. Stuart earned a B.S. in Chemical Engineering from the University of Wyoming, M.S. in Chemical Engineering from Arizona State University, M.S. in Electrical Engineering from Rensselaer Polytechnic Institute, and an M.B.A. from Carnegie Mellon University.

Prior to R2019a, MATLAB Parallel Server was called MATLAB Distributed Computing Server.