DVFS performance prediction for managed multithreaded applications

Making modern computer systems energy-efficient is of paramount importance. Dynamic Voltage and FrequencyScaling (DVFS) is widely used to manage the energy and power consumption in modern processors; however, for DVFS to be effective, we need the ability to accurately predict the performance impact of scaling a processor’s voltage and frequency. No accurate performance predictors exist for multithreaded applications, let alone managed language applications.In this work, we propose DEP+BURST, a new performance predictor for managed multithreaded applications that takes into account synchronization, inter-thread dependencies, and store bursts, which frequently occur in managed language workloads.Our predictor lowers the performance estimation error from 27% for a state-of-the-art predictor to 6% on average, for a set of multithreaded Java applications when the frequency is scaled from 1 to 4 GHz. We also novelly propose an energy management framework that uses DEP+BURST to save energy while meeting performance goals within a user-specified bound.Our proposed energy manager delivers average energy savings of 13% and 19% for a user-specified slowdown of 5% and 10% for memory-intensive Java benchmarks. Accurate performance predictors are key to achieving high performance while keeping energy consumption low for managed language applications usingDVFS