The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
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.
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Cites
Wattch: a framework for architectural-level power analysis and optimizations
Brooks, D., Tiwari, V., & Martonosi, M. (2000) · Proceedings of the 27th annual international symposium on Computer architecture - ISCA '00 · doi:10.1145/339647.339657 · Cited by 2,578
Bailey, D. H., Barszcz, E., Barton, J. T., Browning, D. S., Carter, R. L., Dagum, L., Fatoohi, R. A., Frederickson, P. O., Lasinski, T. A., Schreiber, R. S., et al. (1991) · The International Journal of Supercomputing Applications · doi:10.1177/109434209100500306 · Cited by 2,395
Wattch: a framework for architectural-level power analysis and optimizations
Brooks, D. M., Tiwari, V., & Martonosi, M. (2002) · doi:10.1109/isca.2000.854380 · Cited by 1,122
Runtime power monitoring in high-end processors: methodology and empirical data
Isci, C., & Martonosi, M. (2003) · doi:10.5555/956417.956567 · Cited by 491
PowerScope: a tool for profiling the energy usage of mobile applications
Flinn, J., & Satyanarayanan, M. (1999) · Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications · doi:10.1109/mcsa.1999.749272 · Cited by 433
A Power-Aware Run-Time System for High-Performance Computing
Hsu, C.-H., & Feng, W.-C. (2005) · ACM/IEEE SC 2005 Conference (SC'05) · doi:10.1109/sc.2005.3 · Cited by 402
Bellosa, F. (2000) · doi:10.1145/566726.566736 · Cited by 324
Ge, R., Feng, X., & Cameron, K. W. (2005) · ACM/IEEE SC 2005 Conference (SC'05) · doi:10.1109/sc.2005.57 · Cited by 315
Self-citationRuntime power monitoring in high-end processors: methodology and empirical data
Isci, C., & Martonosi, M. (2004) · 22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449) · doi:10.1109/micro.2003.1253186 · Cited by 297
CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters
Ge, R., Feng, X., Feng, W.-C., & Cameron, K. W. (2007) · 2007 International Conference on Parallel Processing (ICPP 2007) · doi:10.1109/icpp.2007.29 · Cited by 238
Self-citation
Cited by
Data Center Energy Consumption Modeling: A Survey
Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165
Rapid and accurate energy models through calibration with IPMI and RAPL
Kavanagh, R., & Djemame, K. (2019) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5124 · Cited by 23
Quality Assessment of GPU Power Profiling Mechanisms
Sen, S., Imam, N., & Hsu, C.-H. (2018) · 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · doi:10.1109/ipdpsw.2018.00113 · Cited by 7
Metadata
- Authors
- Ge, R., Feng, X., Song, S., Chang, H.-C., Li, D., & Cameron, K. W.
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2010-05
- Volume
- 21
- Issue
- 5
- Pages
- 658-671
- ISSN
- 1045-9219 (ISSN-L)
- OpenAlex
- W2151587416
- Cited by
- 382
- References
- 17
- FWCI
- 39.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ge, R., Feng, X., Song, S., Chang, H.-C., Li, D., & Cameron, K. W. (2010). PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications. IEEE Transactions on Parallel and Distributed Systems. https://doi.org/10.1109/tpds.2009.76
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