Abstract—The advance of high-performance computing sys-tems towards exascale will be constrained by the systems ’ energy consumption levels. Large numbers of processing components, memory, interconnects, and storage components must all be considered to achieve exascale performance within a targeted energy bound. While application-aware power allocation schemes for computing resources are well studied, a portable and scalable budget-constrained power management scheme for scientific ap-plications on exascale systems is still required. Execution activities within scientific applications can be categorized as CPU-bound, I/O-bound and communication-bound. Such activities tend to be clustered into ‘phases’, offering opportunities to manage their power consumption separately. Our experiments have demon-strated that their performance and energy consumption are affected differently by CPU frequency, an opportunity to fine tune CPU frequency for a minimal impact on the total execution time but significant savings on the energy consumption. By exploiting this opportunity, we present a phase-aware hierarchical power management framework that can opportunistically deliver good tradeoffs between system power consumption and application performance under a power budget. Our hierarchical power management framework consists of two main techniques: Phase-Aware CPU Frequency Scaling (PAFS) and opportunistic pro-visioning for power-constrained performance optimization. We have performed a systematic evaluation using both simulations and representative scientific applications on real systems. Our results show that our techniques can achieve 4.3%-17 % better energy efficiency for large-scale scientific applications. I.