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Future servers will incorporate many active lowpower modes for differentsystem components, such as cores and memory. Though these modes provideflexibility for power management via Dynamic Voltage and Frequency Scaling(DVFS), they must be operated in a coordinated manner. Such coordinated controlcreates a combinatorial space of possible power mode configurations. Given therapid growth of the number of cores, it is becoming increasingly challenging toquickly select the configuration that maximizes the performance under a givenpower budget. Prior power capping techniques do not scale well to large numbersof cores, and none of those works has considered memory DVFS. In this paper, wepresent FastCap, our optimization approach for system-wide power capping, usingboth CPU and memory DVFS. Based on a queuing model, FastCap formulates powercapping as a non-linear optimization problem where we seek to maximize thesystem performance under a power budget, while promoting fairness acrossapplications. Our FastCap algorithm solves the optimization online andefficiently (low complexity on the number of cores), using a small set ofperformance counters as input. To evaluate FastCap, we simulate it for amany-core server running different types of workloads. Our results show thatFastCap caps power draw accurately, while producing better applicationperformance and fairness than many existing CPU power capping methods (evenafter they are extended to use of memory DVFS as well).

FastCap: An Efficient and Fair Algorithm for Power Capping in Many-Core Systems