- Allen Malony
We outline the “Learning Everywhere" paradigm -- a powerful scientific methodology of coupling learning methods to traditional HPC simulations. We present several examples of “Learning Everywhere” applications, their scientific impact, and effective performance improvements over traditional HPC simulations. Such applications require a fundamental re-examination of scientific programming and systems software. This talk will highlight middleware advances to enable "Learning Everywhere" algorithms and methods as the "natural" extreme-scale programming paradigm. Specifically, we discuss performance and scalability challenges of integrating AI and HPC tasks, and outline how RADICAL middlware building blocks address these challenges. We also discuss how the "Learning Everywhere” paradigm is enabling therapeutics for COVID19.
Shantenu Jha is the Chair of Computation & Data Driven Discovery Department at Brookhaven National Laboratory, and Professor of Computer Engineering at Rutgers University. His research interests are at the intersection of high-performance distributed computing and computational & data science. Shantenu leads the the RADICAL-Cybertools project which are a suite of middleware building blocks used to support large-scale science and engineeringapplications. He was appointed a Rutgers Chancellor's Scholar (2015) and was the recipient of the inaugural Chancellor's Excellence in Research (2016) for his cyberinfrastructure contributions to computational science. He is a recipient of the NSF CAREER Award (2013), winner of IEEE SCALE 2018 award, and the Gordon Bell Award (2020), as well as several other prizes at SC'xy and ISC’xy. More details can be found at: http://radical.rutgers.edu/shantenu.