NGS: Self-Adapting Large-scale Solver Architecture

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

Large-Scale numerical simulations demand scalable solvers that strike a delicate balance between robustness and cost. The proposed project aims to solve the complex problems of algorithm selection and tuning by implementing a heuristic decision-making agent. Through techniques from data mining and machine learning, applied to production runs, the decision-making component will increase its repertoire of heuristics and tune these heuristics. This will enable many areas of computational science and engineering to use sophisticated numerical software with high efficiency, profoundly reducing the execution time of production runs. We will work on solver software in wide use, over which we have source code control to permit necessary monitoring and manipulation. The innovative aspects of this project are in the integration of numerical analysis and techniques from mainstream computer science such as machine learning, data mining, and componentization, creating a software architecture that makes it easy to employ numerical techniques in an application context with expert knowledge, and to codify their benefits for wider community.