Reducing the energy cost of computing through efficient co-scheduling of parallel workloads

Future computing clusters will prevalently run parallel workloads to take advantage of the increasing number of cores on chips. In tandem, there is a growing need to reduce energy consumption of computing. One promising method for improving energy efficiency is co-scheduling applications on compute nodes. Efficient consolidation for parallel workloads is a challenging task as a number of factors, such as scalability, inter-thread communication patterns, or memory access frequency of the applications affect the energy/performance tradeoffs. This paper evaluates the impact of co-scheduling parallel workloads on the energy consumed per useful work done on real-life servers. Based on this analysis, we propose a novel multi-level technique that selects the best policy to co-schedule multiple workloads on a multi-core processor. Our measurements demonstrate that the proposed multi-level co-scheduling method improves the overall energy per work savings of the multi-core system up to 22% compared to state-of-the-art techniques.