Abstract

This project is focused on a critical issue in computational science. As scientists in all fields increasingly rely on high-throughput applications (which combine multiple components into increasingly complex multi-modal workflows on heterogeneous systems), the increasing complexities of those applications hinder the scientists' ability to generate robust results. The project recruits a cross-disciplinary community working together to define, design, implement, and use a set of solutions for robust science. In so doing, the community defines a roadmap that enables high-throughput applications to withstand and overcome adverse conditions such as heterogeneous, unreliable architectures at all scales including extreme scale, rigorous testing under uncertainties, unexplainable algorithms (eg, in machine learning), and black-box methods. The project's novelties are its comprehensive, cross-disciplinary study of high …

Date

2020

Authors

Ewa Deelman

Journal

NSF Award Number 2028930. Directorate for Computer and Information Science and Engineering

Volume

20

Issue

2028930

Pages

28930