Online Learning for Dynamic Control of OpenMP Workloads

Optimizing energy-efficiency of modern multicore compute systems through online control is often regarded as both promising and challenging. In this paper, we propose a dynamic control technique for OpenMP workloads that exploits online energy efficiency measures derived from the OpenMP runtime. The proposed strategy relies on an automatic program phase identification which detects workload execution patterns, used by an online learning back-end. We design a synthetic benchmark template that makes it possible to produce benchmarks with controllable characteristics for mimicking a wide range of workload profiles. Experimental results show improvements on a 20-core server when compared to default Linux governors.