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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Isci, C. Contreras, G. Martonosi, M. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. Eng., Princeton Univ., NJ (Isci, C.; Contreras, G.; Martonosi, M.) |
| 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 |
| Sponsorship | IEEE TC-uARCH ACM SIGMICRO |
| Starting Page | 359 |
| Ending Page | 370 |
| File Size | 571903 |
| Page Count | 12 |
| File Format | |
| ISBN | 0769527329 |
| ISSN | 10724451 |
| DOI | 10.1109/MICRO.2006.30 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Runtime Energy management Power system management Dynamic voltage scaling Frequency Computerized monitoring Application software Computer architecture Fuels History |
| Content Type | Text |
| Resource Type | Article |
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