It is desirable for general productivity that high-performance computing applications be portable to new architectures, or can be optimized for new workflows and input types, without the need for costly code interventions or algorithmic re-writes. Parallel portability 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.

This dissertation maps out a vision for addressing this parallel portability challenge, and then demonstrates this plan through an effective combination of observability, analysis, and in situ machine learning techniques. A platform for general-purpose observation in HPC contexts will be investigated, along with support for its use in human-in-the-loop performance understanding and analysis, finally concluding in a demonstration of lessons learned in order to provide online automated tuning of HPC applications utilizing parallel portability frameworks.