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ABSTRACT
Energy efficiency is becoming an increasingly important feature for both mobile and high-performance server systems. Most processors designed today include power management features that provide processor operating points which can be used in power management algorithms. However, existing power management algorithms implicitly assume that lower performance points are more energy efficient than higher performance points. Our empirical observations indicate that for many systems, this assumption is not valid.We introduce a new concept called critical power slope to explain and capture the power-performance characteristics of systems with power management features. We evaluate three systems - a clock throttled Pentium laptop, a frequency scaled PowerPC platform, and a voltage scaled system to demonstrate the benefits of our approach. Our evaluation is based on empirical measurements of the first two systems, and publicly available data for the third. Using critical power slope, we explain why on the Pentium-based system, it is energy efficient to run only at the highest frequency, while on the PowerPC-based system, it is energy efficient to run at the lowest frequency point. We confirm our results by measuring the behavior of a web serving benchmark. Furthermore, we extend the critical power slope concept to understand the benefits of voltage scaling when combined with frequency scaling. We show that in some cases, it may be energy efficient not to reduce voltage below a certain point.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Nevine AbouGhazaleh , Alexandre Ferreira , Cosmin Rusu , Ruibin Xu , Frank Liberato , Bruce Childers , Daniel Mosse , Rami Melhem, Integrated CPU and l2 cache voltage scaling using machine learning, ACM SIGPLAN Notices, v.42 n.7, July 2007
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Vincent W. Freeh , David K. Lowenthal , Feng Pan , Nandini Kappiah , Rob Springer , Barry L. Rountree , Mark E. Femal, Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications, IEEE Transactions on Parallel and Distributed Systems, v.18 n.6, p.835-848, June 2007
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Haijin Yan , Scott A. Watterson , David K. Lowenthal , Kang Li , Rupa Krishnan , Larry L. Peterson, Client-Centered, Energy-Efficient Wireless Communication on IEEE 802.11b Networks, IEEE Transactions on Mobile Computing, v.5 n.11, p.1575-1590, November 2006
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