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In long-lasting scientific workflow executions in HPC machines, computationalscientists (the users in this work) often need to fine-tune several workflowparameters. These tunings are done through user steering actions that maysignificantly improve performance (e.g., reduce execution time) or improve theoverall results. However, in executions that last for weeks, users can losetrack of what has been adapted if the tunings are not properly registered. Inthis work, we build on provenance data management to address the problem oftracking online parameter fine-tuning in dynamic workflows steered by users. Wepropose a lightweight solution to capture and manage provenance of the steeringactions online with negligible overhead. The resulting provenance databaserelates tuning data with data for domain, dataflow provenance, execution, andperformance, and is available for analysis at runtime. We show how users mayget a detailed view of the execution, providing insights to determine when andhow to tune. We discuss the applicability of our solution in different domainsand validate its ability to allow for online capture and analyses of parameterfine-tunings in a real workflow in the Oil and Gas industry. In thisexperiment, the user could determine which tuned parameters influencedsimulation accuracy and performance. The observed overhead for keeping track ofuser steering actions at runtime is less than 1% of total execution time.

Keeping Track of User Steering Actions in Dynamic Workflows