The Roles of Sparse Direct Methods in Large-scale Simulations
Xiaoye Sherry Li, LBNL
Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC (NIMROD and M3D-C1 codes, fusion plasma simulation).
The Terascale Optimal PDE Simulations (TOPS) is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations.
Most of these systems are very ill-conditioned, resulting in extremely poor convergence (sometimes no convergence) for many iterative solvers. we have successfully deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains.
These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research.