Run-DMC: Runtime dynamic heterogeneous multicore performance and power estimation for energy efficiency

Abstract

In this paper we propose Run-DMC, an accurate runtime performance and power estimation scheme for dynamic workloads executing on heterogeneous multicore systems. In contrast to previous works, Run-DMC uses fine grain per-thread metrics that model the Thread Load Contribution (TLC) induced by the native OS scheduling policy to accurately predict performance and power for any possible thread-to-core mapping. This allows the operating system to opportunistically exploit the heterogeneous multicore architecture by dynamically mapping workloads to the most appropriate core type. We have integrated our models into the Linux kernel running on top of a heterogeneous multicore system with 4 different core types. Our experimental results show that Run-DMC models yield up to 97% more energy efficient when compared to the vanilla Linux. When compared to the approach employed by state-of-the-art energy-aware schedulers, Run-DMC yields up-to 44% better energy efficiency.