  High Performance Computing (HPC) applications are essential for scientists
and engineers to create and understand models and their properties. These
professionals depend on the execution of large sets of computational jobs that
explore combinations of parameter values. Avoiding the execution of unnecessary
jobs brings not only speed to these experiments, but also reductions in
infrastructure usage---particularly important due to the shift of these
applications to HPC cloud platforms. Our hypothesis is that data generated by
these experiments can help users in identifying such jobs. To address this
hypothesis we need to understand the similarity levels among multiple
experiments necessary for job elimination decisions and the steps required to
automate this process. In this paper we present a study and a machine
learning-based tool called JobPruner to support parameter exploration in HPC
experiments. The tool was evaluated with three real-world use cases from
different domains including seismic analysis and agronomy. We observed the tool
reduced 93% of jobs in a single experiment, while improving quality in most
scenarios. In addition, reduction in job executions was possible even
considering past experiments with low correlations.
