Document Type
Article
Language
eng
Format of Original
4 p.
Publication Date
11-2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Source Publication
IEEE Transactions on Parallel and Distributed Systems
Source ISSN
9780769543857
Original Item ID
doi: 10.1109/TPDS.2009.76
Abstract
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.
Recommended Citation
Ge, Rong; Feng, Xizhou; Song, Shuaiwen; Chang, Hung-Ching; Li, Dong; and Cameron, Kirk W., "PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications" (2010). Mathematics, Statistics and Computer Science Faculty Research and Publications. 3.
https://epublications.marquette.edu/mscs_fac/3
Comments
Accepted version. IEEE Transactions on Parallel and Distributed Systems, Vol. 21, No. 5 (May 2010), DOI. © 2010 IEEE. Used with permission.