Compile-time dynamic voltage scaling settings

Abstract

With power-related concerns becoming dominant aspects of hardware and software design, significant research effort has been devoted towards system power minimization. Among run-time power-management techniques, dynamic voltage scaling (DVS) has emerged as an important approach, with the ability to provide significant power savings. DVS exploits the ability to control the power consumption by varying a processor's supply voltage (V) and clock frequency (f). DVS controls energy by scheduling different parts of the computation to different (V, f) pairs; the goal is to minimize energy while meeting performance needs. Although processors like the Intel XScale and Transmeta Crusoe allow software DVS control, such control has thus far largely been used at the process/task level under operating system control. This is mainly because the energy and time overhead for switching DVS modes is considered too large and difficult to manage within a single program.In this paper we explore the opportunities and limits of compile-time DVS scheduling. We derive an analytical model for the maximum energy savings that can be obtained using DVS given a few known program and processor parameters. We use this model to determine scenarios where energy consumption benefits from compile-time DVS and those where there is no benefit. The model helps us extrapolate the benefits of compile-time DVS into the future as processor parameters change. We then examine how much of these predicted benefits can actually be achieved through optimal settings of DVS modes. This is done by extending the existing Mixed-integer Linear Program (MILP) formulation for this problem by accurately accounting for DVS energy switching overhead, by providing finer-grained control on settings and by considering multiple data categories in the optimization. Overall, this research provides a comprehensive view of compile-time DVS management, providing both practical techniques for its immediate deployment as well theoretical bounds for use into the future.