High-performance computing (HPC) systems have become power limited. For instance, the U.S. Department of Energy set a power envelope of 20MW in 2008 for the first exascale supercomputer now expected to arrive in 2021--22. Toward this end, we seek to improve the greenness of HPC systems by improving their performance per watt at the allocated power budget. In this dissertation, we develop a series of models and techniques to manage power at micro-, meso-, and macro-levels of the system hierarchy, specifically addressing data movement and heterogeneity. We target the chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level. Overall, our goal is to improve the greenness of HPC systems by intelligently managing power. The first part of this dissertation focuses on measurement and modeling problems for power. First, we study how to infer chip-interconnect power by observing the system-wide power consumption. Our proposal is to design a novel micro-benchmarking methodology based on data-movement distance by which we can properly isolate the chip interconnect and measure its power. Next, we study how to develop software power meters to monitor a GPU's power consumption at runtime. Our proposal is to adapt performance counter-based models for their use at runtime via a combination of heuristics, statistical techniques, and application-specific knowledge. In the second part of this dissertation, we focus on managing power. First, we propose to reduce the chip-interconnect power by proactively managing its dynamic voltage and frequency (DVFS) state. Toward this end, we develop a novel phase predictor that uses approximate pattern matching to forecast future requirements and in turn, proactively manage power. Second, we study the problem of applying a power cap to a heterogeneous node. Our proposal proactively manages the GPU power using phase prediction and a DVFS power model but reactively manages the CPU. The resulting hybrid approach can take advantage of the differences in the capabilities of the two devices. Third, we study how in-situ techniques can be applied to improve the greenness of HPC clusters. Overall, in our dissertation, we demonstrate that it is possible to infer power consumption of real hardware components without directly measuring them, using the chip interconnect and GPU as examples. We also demonstrate that it is possible to build models of sufficient accuracy and apply them for intelligently managing power at many levels of the system hierarchy. / Doctor of Philosophy / Past research in green high-performance computing (HPC) mostly focused on managing the power consumed by general-purpose processors, known as central processing units (CPUs) and to a lesser extent, memory. In this dissertation, we study two increasingly important components: interconnects (predominantly focused on those inside a chip, but not limited to them) and graphics processing units (GPUs). Our contributions in this dissertation include a set of innovative measurement techniques to estimate the power consumed by the target components, statistical and analytical approaches to develop power models and their optimizations, and algorithms to manage power statically and at runtime. Experimental results show that it is possible to build models of sufficient accuracy and apply them for intelligently managing power on multiple levels of the system hierarchy: chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level.