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 parameters, so that HPC applications perform well in different system environments and on different input data sets, is not trivial.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 is investigated, along with support for its use in human-in-the-loop performance understanding and analysis. The dissertation culminates in a demonstration of lessons learned in order to provide automated tuning of HPC applications utilizing parallel portability frameworks
