User Steering Support in Large-scale Workflows

Abstract

Large-scale workflows that execute on High-Performance Computing machines need to be dynamically steered by users. This means that users analyze big data files, assess key performance indicators, fine-tune parameters, and evaluate the tuning impacts while the workflows generate multiple files, which is challenging. If one does not keep track of such interactions (called user steering actions), it may be impossible to understand the consequences of steering actions and to reproduce the results. This thesis proposes a generic approach to enable tracking user steering actions by characterizing, capturing, relating, and analyzing them by leveraging provenance data management concepts. Experiments with real users show that the approach enabled the understanding of the impact of steering actions while incurring negligible overhead.