COMPUTATIONAL RESEARCH in BOSTON and BEYOND (CRIBB)

Date	July 14, 2017
Speaker	Jeremy Kepner MIT-Lincoln Laboratory MIT Lincoln Laboratory Supercomputing Center Head
Topic	Convergence of Machine Learning, Big Data, and Supercomputing
Abstract	Machine learning, big data, and simulation challenges have led to a proliferation of computing hardware and software solutions. Hyperscale data centers, accelerators, and programmable logic can deliver enormous performance via a wide range of analytic environments and data storage technologies. Effectively exploiting these capabilities for science and engineering requires mathematically rigorous interfaces that allow scientists and engineers to focus on their research and avoid rewriting software each time computing technology changes. Mathematically rigorous interfaces are at the core MIT Lincoln Laboratory Supercomputing Center (LLSC) and enable the LLSC to deliver leading edge technologies to thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are the key to exploiting advanced computing capabilities.

Acknowledgements

We thank the generous support of MIT IS&T, CSAIL, and the Department of Mathematics for their support of this series.