Predictive power management for multi-core processors

Authors:
William Lloyd Bircher;Lizy John
Affiliations:
The University of Texas at Austin;The University of Texas at Austin
Venue:
ISCA'10 Proceedings of the 2010 international conference on Computer Architecture
Year:
2010

Citing 12
Cited 2

Comparing algorithm for dynamic speed-setting of a low-power CPU

MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
The benefits of event: driven energy accounting in power-sensitive systems

EW 9 Proceedings of the 9th workshop on ACM SIGOPS European workshop: beyond the PC: new challenges for the operating system
Phase tracking and prediction

Proceedings of the 30th annual international symposium on Computer architecture
Characterizing and Predicting Program Behavior and its Variability

Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
Runtime Power Monitoring in High-End Processors: Methodology and Empirical Data

Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Locality phase prediction

ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
The Fuzzy Correlation between Code and Performance Predictability

Proceedings of the 37th annual IEEE/ACM International Symposium on Microarchitecture
Long-Term Workload Phases: Duration Predictions and Applications to DVFS

IEEE Micro
Structures for phase classification

ISPASS '04 Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software
Live, Runtime Phase Monitoring and Prediction on Real Systems with Application to Dynamic Power Management

Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture
Analysis of dynamic power management on multi-core processors

Proceedings of the 22nd annual international conference on Supercomputing
Evaluation of the Intel® Core i7 Turbo Boost feature

IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)

Evaluating the effectiveness of model-based power characterization

USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
PGCapping: exploiting power gating for power capping and core lifetime balancing in CMPs

Proceedings of the 21st international conference on Parallel architectures and compilation techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

Predictive power management provides reduced power consumption and increased performance compared to reactive schemes. It effectively reduces the lag between workload phase changes and changes in power adaptations since adaptations can be applied immediately before a program phase change. To this end we present the first analysis of prediction for power management under SYSMark2007. Compared to traditional scientific/computing benchmarks, this workload demonstrates more complex core active and idle behavior. We analyze a table based predictor on a quad-core processor. We present an accurate runtime power model that accounts for fine-grain temperature and voltage variation. By predictively borrowing power from cores, our approach provides an average speedup of 7.3% in SYSMark2007.