Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning

Portable parallel programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of those parameters is non-trivial, so that HPC applications perform well in different system environments and on different input data sets, without the need of time consuming parameter exploration or major algorithmic adjustments.