The Mathematical Convergence and Architectural Divergence of Machine Learning, Big Data, and Supercomputing

Jeremy Kepner, MIT Lincoln Laboratory

Photo of Jeremy Kepner

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 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 of the MIT Lincoln Laboratory Supercomputing Center and let it deliver leading-edge technologies to thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are key to exploiting advanced computing capabilities.

Abstract Author(s): Jeremy Kepner