As HPC progresses toward exascale, writing applications that are highly efficient, portable, and support programmer productivity is becoming more challenging than ever. The growing scale, diversity, and heterogeneity in compute platforms increases the burden on software to efficiently use available distributed parallel resources. This burden has fallen on developers who, increasingly, are experts in application domains rather than traditional computer scientists and engineers. We propose CASPER—Compiler Abstractions Supporting high Performance on Extreme-scale Resources—a novel domain-specific compiler and runtime framework to enable domain scientists to achieve high performance and scalability on complex HPC systems. CASPER extends domain-specific languages with machine learning to map software tasks to distributed, heterogeneous resources, and provides a runtime framework to support a variety of adaptive runtime optimizations for dynamic environments. This paper presents an initial design and analysis of the CASPER framework for synthetic aperture radar and computational fluid dynamics domains.