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To improve energy efficiency processors allow for Dynamic Voltage and Frequency Scaling (DVFS), which enables changing their performance and power consumption on-the-fly. Many-core architectures, such as the Single-chip Cloud Computer (SCC) experimental processor from Intel Labs, have DVFS infrastructures that scale by having many more independent voltage and frequency domains on-die than today's multi-cores. This paper proposes a novel, hierarchical, and transparent client-server power management scheme applicable to such architectures. The scheme tries to minimize energy consumption within a performance window taking into consideration not only the local information for cores within frequency domains but also information that spans multiple frequency and voltage domains. We implement our proposed hierarchical power control using a novel application-driven phase detection and prediction approach for Message Passing Interface (MPI) applications, a natural choice on the SCC with its fast on-chip network and its non-coherent memory hierarchy. This phase predictor operates as the front-end to the hierarchical DVFS controller, providing the necessary DVFS scheduling points. Experimental results with SCC hardware show that our approach provides significant improvement of the Energy Delay Product (EDP) of as much as 27.2%, and 11.4% on average, with an average increase in execution time of 7.7% over a baseline version without DVFS. These improvements come from both improved phase prediction accuracy and more effective DVFS control of the domains, compared to existing approaches.