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High Performance Computing (HPC) applications are essential for scientistsand engineers to create and understand models and their properties. Theseprofessionals depend on the execution of large sets of computational jobs thatexplore combinations of parameter values. Avoiding the execution of unnecessaryjobs brings not only speed to these experiments, but also reductions ininfrastructure usage---particularly important due to the shift of theseapplications to HPC cloud platforms. Our hypothesis is that data generated bythese experiments can help users in identifying such jobs. To address thishypothesis we need to understand the similarity levels among multipleexperiments necessary for job elimination decisions and the steps required toautomate this process. In this paper we present a study and a machinelearning-based tool called JobPruner to support parameter exploration in HPCexperiments. The tool was evaluated with three real-world use cases fromdifferent domains including seismic analysis and agronomy. We observed the toolreduced 93% of jobs in a single experiment, while improving quality in mostscenarios. In addition, reduction in job executions was possible evenconsidering past experiments with low correlations.

JobPruner: A Machine Learning Assistant for Exploring Parameter Spaces in HPC Applications