Three scalable approaches to improving many-core throughput for a given peak power budget

Recently proposed techniques for peak power management involve centralized decision-making and assume quick evaluation of the various power management states. These techniques suffer from two limitations. First, they do not prevent instantaneous power from exceeding the peak power budget, but instead trigger corrective action when the budget has been exceeded. Second, while these techniques may work for multi-core architectures (processors with small number of cores), they are not suitable for many-core architectures (processors with tens or possibly hundreds of cores on the same die) due to an exponential explosion in the number of global power management states. In this paper, we look at three scalable techniques for peak power management for many-core architectures. The proposed techniques (mapping the power management problem to a knapsack problem, mapping it to a genetic search problem, and mapping it to a simple learning problem with confidence counters) prevent power from exceeding the peak power budget and enable the placement of several more cores on a die than what the power budget would normally allow. We show up to 47% (33% on average) improvements in throughput for a given power budget. Our techniques outperform the static oracle by 22%.