Past Event: Oden Institute Seminar

Towards simulations on the Exascale hardware and beyond

Sivasankaran (Siva) Rajamanickam, Sandia National Laboratories, Albuquerque, NM

Abstract

This talk will cover two paths that we are pursuing to adopt scientific simulations as we march towards building exascale systems. The first path for scientific simulations on exascale systems relies on adopting codes to be performance portable. This talk will cover the Kokkos ecosystem as a programming model. Then we will cover key design principles in developing high performance scientific simulations that are also portable using Kokkos. I will show examples from the exascale computing project. The second path is to pursue novel machine learning techniques that build on recent advances. These advances have helped in tasks such as image classification and board games. The question we are all interested in is whether machine learning can also advance science and how it can help advance science. This talk will cover a portion of this space from the perspective of applications, and computer architectures. I will cover recent advances that enabled us to use machine learning within a material science workflow. Specifically, I will cover our recent ML model that is a surrogate for Density Functional Theory calculations. Finally, I will cover recent advances in computer architectures that is focused on machine learning and the impact of such architectures on future performance. Bio Siva Rajamanickam has a PhD in Computer Science and Engineering from the University of Florida. He is a principal member of technical staff in the Scalable Algorithms department at the Center for Computing Research at Sandia National Laboratories. His focus is in the intersection of high performance computing, combinatorial scientific computing, graph algorithms and machine learning. He is the Sandia PI for the Office of Science Advanced Scientific Computing and Research funded co-design center, ARIAA, that focuses on upcoming machine learning accelerators. Dr. Rajamanickam also leads a Sandia LDRD on accelerating material simulations with machine learning. Dr. Rajamanickam is also part of the Exascale Computing Project’s (ECP) ExaLearn project which addresses the machine learning needs of ECP applications. On the scientific computing side, he leads the linear solver efforts in Sandia's Trilinos framework, and Kokkos Kernels library for performance portability.