DEP+BURST: Online DVFS Performance Prediction for Energy-Efficient Managed Language Execution

Shoaib Akram, Jennifer Sartor, Lieven Eeckhout

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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% 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 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%, our energy manager reduces on average 13% and 19% 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% 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.
Original languageEnglish
Article number7569056
Pages (from-to)601-615
Number of pages15
JournalIEEE Transactions on Computers
Issue number4
Publication statusPublished - Apr 2017


  • Dynamic voltage and frequency scaling
  • dynamic energy management
  • managed runtimes
  • multithreaded performance estimation


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