Abstract:
This talk will present a retrospective on how scientific modeling has evolved at HPC facilities in the Department of Energy. Historically, centers such as Berkeley Lab’s NERSC have primarily supported simulation workloads for over 40 years. These workloads involve running PDE simulations that model complex natural systems (fluid flow, coupled climate model, subsurface flow, etc). Over the past 10 years, data-intensive science has gained prominence: these workloads involve application of statistical or machine learning tools to analyze experimental and observational datasets (from astronomy, cosmology, climate, high-energy physics, etc). We are now witnessing the rise of Deep Learning in Science: domain scientists are augmenting/replacing/enhancing their simulation and analytics models with Deep Learning architectures. This talk will touch upon some leading scientific use cases at NERSC, and conclude with open challenges at the interface of Deep Learning and Scientific Modeling.
Bio:
Prabhat leads the Data and Analytics Services team at NERSC; his group is responsible for supporting over 7000 scientific users on NERSC’s HPC systems. His current research interests include Deep Learning, Machine Learning, Applied Statistics and High Performance Computing. In the past, Prabhat has worked on topics in scientific data management; he co-edited a book on ‘High Performance Parallel I/O’.
Prabhat received a B.Tech in Computer Science and Engineering from IIT-Delhi (1999); ScM in Computer Science from Brown University (2001) and a PhD in Earth and Planetary Sciences from U.C. Berkeley (2020). Prabhat has co-authored over 150 papers spanning several domain sciences and topics in computer science. He has won 5 Best Paper Awards, 3 Industry Innovation Awards, and he was a part of the team that won the 2018 Gordon Bell Prize for their work on ‘Exascale Deep Learning’.