Live, runtime phase monitoring and prediction on real systems with application to dynamic power management

Canturk Isci, Gilberto Contreras, Margaret Martonosi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

192 Scopus citations

Abstract

Computer architecture has experienced a major paradigm shift from focusing only on raw performance to considering power-performance efficiency as the defining factor of the emerging systems. Along with this shift has come increased interest in workload characterization. This interest fuels two closely related areas of research. First, various studies explore the properties of workload variations and develop methods to identify and track different execution behavior, commonly referred to as "phase analysis". Second, a large complementary set of research studies dynamic, on-the-fly system management techniques that can adaptively respond to these differences in application behavior. Both of these lines of work have produced very interesting and widely useful results. Thus far, however, there exists only a weak link between these conceptually related areas, especially for real-system studies. Our work aims to strengthen this link by demonstrating a real-system implementation of a runtime phase predictor that works cooperatively with on-the-fly dynamic management. We describe a fully-functional deployed system that performs accurate phase predictions on running applications. The key insight of our approach is to draw from prior branch predictor designs to create a phase history table that guides predictions. To demonstrate the value of our approach, we implement a prototype system that uses it to guide dynamic voltage and frequency scaling. Our runtime phase prediction methodology achieves above 90% prediction accuracies for many of the experimented benchmarks. For highly variable applications, our approach can reduce mispredictions by more than 6X over commonly-used statistical approaches. Dynamic frequency and voltage scaling, when guided by our runtime phase predictor, achieves energy-delay product improvements as high as 34% for benchmarks with non-negligible variability, on average 7% better than previous methods and 18% better than a baseline unmanaged system.

Original languageEnglish (US)
Title of host publicationProceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-39
Pages359-370
Number of pages12
DOIs
StatePublished - Dec 1 2006
Event39th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-39 - Orlando, FL, United States
Duration: Dec 9 2006Dec 13 2006

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
ISSN (Print)1072-4451

Other

Other39th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-39
CountryUnited States
CityOrlando, FL
Period12/9/0612/13/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Isci, C., Contreras, G., & Martonosi, M. (2006). Live, runtime phase monitoring and prediction on real systems with application to dynamic power management. In Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-39 (pp. 359-370). [4041860] (Proceedings of the Annual International Symposium on Microarchitecture, MICRO). https://doi.org/10.1109/MICRO.2006.30