siegel-300MICDE Seminar: Andrew Siegel

Andrew Siegel is a Computational Scientist at Argonne National Laboratory with appointments both in the Mathematics/Computer Science and Nuclear Engineering Divisions. For the past eight years, Dr. Siegel has led Argonne’s program in advanced reactor modeling and simulation, where his own research has centered around high-fidelity modeling of advanced reactors, including mixing, neutron/fluid coupling, and innovative computational approaches to stochastic methods for neutron transport.¬† Previously, Dr. Siegel served as the co-founder and lead of the SHARP group (Simulation-based High-Fidelity Advanced Reactor Prototyping), The National Technical Director of Nuclear Energy Advanced Modeling and Simulation Program, The Deputy Director for the Fusion Simulation Program, Lead of the Petaflops Application Group, and the Chief Artchitect of the Flash Code. Dr. Siegel is also a Senior Fellow at the University of Chicago Computation Institute, where he has designed a curriculum and taught over fifty courses in parallel computing, numerical methods, and software design.

Trends in Next Generation HPC Architectures and Their Impact on Computational Methods for Nuclear Reactor Analysis

4 p.m., Friday, Nov. 14
White Auditorium (G906 Cooley Building)

Next-generation HPC platforms in many cases will force application developers to re-formulate¬† fundamental algorithmic and implementation approaches that were adopted over the previous generation. Overall levels of concurrency, the relative cost of FLOP/s compared to data movement, available memory per floating point unit, depth and complexity of the memory hierarchy, awareness of power costs, and overall resilience characteristics are a few broad areas where exascale-type machines are likely to depart signficantly from current practice. While constrained to some degree by the technology, in designing future HPC systems there is still considerable latitude both in a relatively¬† broad range of design tradeoffs and the programming models that are used to optimally express them. At the same time, regardless of specific design choices, most applications will need to evolve considerably to make efficient use of these systems, including developing new algorithmic implementations, formulations, and potentially even new mathematical descriptions of the target physical problem. In this talk I discuss in depth several concrete examples of this “push” and “pull” of co-design directly relevant to the simulation of nuclear energy systems.