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While high performance has always been the primary constraint behind large-scale system design, future systems will be built with increasing energy efficiency in mind. Mechanisms such as fine-grained power scaling and gating will provide tools to system-software and application developers to ensure the most efficient use of tightly constrained power budgets. Such approaches to-date have been focused on nodelevel optimizations to impact overall system energy efficiency. In this work we introduce Dynamic Power Steering, in which power can be dynamically routed across a system to resources where it will be of most benefit and away from other resources to maintain a near-constant overall power budget. This, a higherlevel algorithmic approach to improving energy efficiency, considers the whole extent of a system being used by an application. It can be used for applications in which there is loadimbalance that varies over its execution. Using two classes of applications, namely those that contain a wavefront type processing, and a particle-in-cell, we quantify the benefit of Dynamic Power Steering for a variety of workload characteristics and derive some insight into the ways in which workload behavior affect Power Steering applicability.

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