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Issue No. 05 - May (2010 vol. 21)
ISSN: 1045-9219
pp: 658-671
Rong Ge , Marquette University, Milwaukee
Xizhou Feng , Virginia Tech, Blacksburg
Shuaiwen Song , Virginia Tech, Blacksburg
Hung-Ching Chang , Virginia Tech, Blacksburg
Dong Li , Virginia Tech, Blacksburg
Kirk W. Cameron , Virginia Tech, Blacksburg
Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance.
Distributed system, CMP-based cluster, energy efficiency, power measurement, system tools, power management, dynamic voltage and frequency scaling.

R. Ge, D. Li, H. Chang, K. W. Cameron, X. Feng and S. Song, "PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications," in IEEE Transactions on Parallel & Distributed Systems, vol. 21, no. , pp. 658-671, 2009.
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