DEP+BURST : online DVFS performance prediction for energy-efficient managed language execution

Making modern computer systems energy-efficient is of paramount importance. Dynamic Voltage and Frequency Scaling (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 percent for a state-of-the-art predictor to 6 percent 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 reduce energy consumption. We first target reducing the processor's energy consumption by lowering its frequency and hence its power consumption, while staying within a user-specified maximum slowdown threshold. For a slowdown of 5 and 10 percent, our energy manager reduces on average 13 and 19 percent of energy consumed by the memory-intensive benchmarks. We then use the energy manager to optimize total system energy, achieving an average reduction of 15.6 percent for a set of Java benchmarks. Accurate performance predictors are key to achieving high performance while keeping energy consumption low for managed language applications using DVFS