This paper examines energy management in a heterogeneous processor consisting of an integrated CPU-GPU for high-performance computing (HPC) applications. Energy management for HPC applications is challenged by their uncompromising performance requirements and complicated by the need for coordinating energy management across distinct core types – a new and less understood problem.We examine the intra-node CPU-GPU frequency sensitivity of HPC applications on tightly coupled CPU-GPU architectures as the first step in understanding power and performance optimization for a heterogeneous multi-node HPC system. The insights from this analysis form the basis of a coordinated energy management scheme, called DynaCo, for integrated CPU-GPU architectures. We implement DynaCo on a modern heterogeneous processor and compare its performance to a state-of-the-art power- and performance-management algorithm. DynaCo improves measured average energy-delay squared (ED^2) product by up to 30% with less than 2% average performance loss across several exascale and other HPC workloads.