Science Automation in Practice - Performance Data Farming in Workflows
AUTHORS: D. Król, E. Deelman, R. F. Da Silva, G. Juve, J. Kitowski, M. Rynge and K, Vahi
ABSTRACT
This paper describes an approach to conduct large- scale parameter studies, where each data point in the study requires the execution of a whole scientific workflow. We show how a parameter studies system can be integrated with a work- flow management system to seamlessly execute a large number of workflows, each with different input parameter values using large-scale computing infrastructure. The work is motivated by a need to collect performance-related data to conduct a sensitivity analysis in the context of relation between workflow input parameters and the performance of tasks in the workflow developed for the Spallation Neutron Source facility at the Oak
