Article

Global Extensible Open Power Manager: A Vehicle for HPC Community Collaboration on Co-Designed Energy Management Solutions

Authors:

Jonathan Eastep,

Steve Sylvester,

Christopher Cantalupo,

Federico Ardanaz,

Kelly Livingston,

Matthias Maiterth,

Siddhartha JanaAuthors Info & Claims

High Performance Computing: 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18–22, 2017, Proceedings

Pages 394 - 412

https://doi.org/10.1007/978-3-319-58667-0_21

Published: 18 June 2017 Publication History

Abstract

The power scaling challenge associated with Exascale systems is a well-known issue. In this work, we introduce the Global Extensible Open Power Manager (GEOPM): a tree-hierarchical, open source runtime framework we are contributing to the HPC community to foster increased collaboration and accelerated progress toward software-hardware co-designed energy management solutions that address Exascale power challenges and improve performance and energy efficiency in current systems. Through its plugin extensible architecture, GEOPM enables rapid prototyping of new energy management strategies. Different plugins can be tailored to the specific performance or energy efficiency priorities of each HPC center. To demonstrate the potential of the framework, this work develops an example plugin for GEOPM. This power rebalancing plugin targets power-capped systems and improves efficiency by minimizing job time-to-solution within a power budget. Our results demonstrate up to 30% improvements in the time-to-solution of CORAL system procurement benchmarks on a Xeon Phi cluster.

References

[1]

Eastep, J., Sylvester, S., Cantalupo, C., et al.: Global extensible open power manager: a vehicle for HPC community collaboration toward co-designed energy management solutions. In: Supercomputing PMBS (2016)

[2]

Schulz, K., Baird, C.R., Brayford, D., et al.: Cluster computing with OpenHPC. In: Supercomputing HPC Systems Professionals (2016)

[3]

Auweter A et al. Kunkel JM, Ludwig T, Meuer HW, et al. A case study of energy aware scheduling on SuperMUC Supercomputing 2014 Cham Springer 394-409

[4]

Marathe A, Bailey PE, Lowenthal DK, Rountree B, Schulz M, and de Supinski BR Kunkel JM and Ludwig T A run-time system for power-constrained HPC applications High Performance Computing 2015 Cham Springer 394-408

[5]

Rountree, B., Lowenthal, D.K., de Supinski, B., Schulz, M., Freeh, V.W.: Adagio: making DVS practical for complex HPC applications. In: ICS (2009)

[6]

Kappiah, N., Freeh, V.W., Lowenthal, D.K.: Just in time dynamic voltage scaling: exploiting inter-node slack to save energy in MPI programs. In: Supercomputing (2005)

[7]

Etinski, M., Corbalan, J., Labarta, J., Valero, M.: Optimizing job performance under a given power constraint in HPC centers. In: IGCC (2010)

[8]

Etinski, M., Corbalan, J., Labarta, J., Valero, M.: Linear programming based parallel job scheduling for power constrained systems. In: HPCS (2011)

[9]

Sarood, O., Langer, A., Gupta, A., Kale, L.: Maximizing throughput of overprovisioned HPC data centers under a strict power budget. In: Supercomputing (2014)

[10]

Global Extensible Open Power Manager Project. Intel Corporation (2016). http://geopm.github.io/geopm/

[11]

Shoga, K., Rountree, B., Schulz, M., Shafer, J.: Whitelisting MSRs with MSR-safe. In: Supercomputing Exascale Systems Programming Tools (2014)

[12]

Rountree, B., Ahn, D.H., de Supinski, B.R., et al.: Beyond DVFS: a first look at performance under a hardware-enforced power bound. In: HPPAC (2012)

[13]

Inadomi, Y., Patki, T., Inoue, K., et al.: Analyzing and mitigating the impact of manufacturing variability in power-constrained supercomputing. In: Supercomputing (2015)

[14]

CORAL Procurement Benchmarks. Livermore National Lab (2016). https://asc.llnl.gov/CORAL-benchmarks/CORALBenchmarksProcedure-v26.pdf

[15]

Mohd-Yusof, J.: Codesign molecular dynamics (CoMD) proxy app. In: ExMatEx All-Hands Meeting (2012)

[16]

Bailey, D., Barszcz, E., Barton, J., Browning, D., Carter, R., Dagum, L., Fatoohi, R., Frederickson, P., Lasinski, T., Schreiber, R., et al.: The NAS parallel benchmarks summary and preliminary results. In: Supercomputing (1991)

[17]

Intel: Intel-64 and IA-32 Architectures Software Developer’s Manual, vols. 3A and 3B. System Programming Guide, Intel Corporation (2011)

[18]

Laros, J., DeBonis, D., Grant, R., et al.: High performance computing - power application programming interface specification, version 1.0. Sandia National Laboratories, Technical report SAND2014-17061 (2014)

[19]

Gschwind, M.: OpenPOWER: reengineering a server ecosystem for large-scale data centers. In: Hot Chips Symposium (HCS) (2014)

[20]

GEOPM Video Tutorials: Intel Corporation (2016). https://www.youtube.com/playlist?list=PLwm-z8c2AbIBU-T7HnMi_Pux7iO3gQQnz

[21]

Rountree, B., Lowenthal, D.K., Funk, S., et al.: Bounding energy consumption in large-scale MPI programs. In: Supercomputing (2007)

[22]

Cameron, K.W., Feng, X., Ge, R.: Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: Supercomputing (2005)

[23]

Ge, R., Feng, X., Feng, W., Cameron, K.W.: CPU MISER: a performance-directed, run-time system for power-aware clusters. In: ICPP (2007)

[24]

Hsu, C.-H., Feng, W.-C.: A power-aware run-time system for high-performance computing. In: Supercomputing (2005)

[25]

Li, D., de Supinski, B., Schulz, M., Cameron, K., Nikolopoulos, D.: Hybrid MPI/OpenMP power-aware computing. In: IPDPS (2010)

[26]

Ellsworth, D., Patki, T., Perarnau, S., et al.: Systemwide power management with Argo. In: Parallel and Distributed Processing Symposium Workshops (2016)

[27]

Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., Zhu, X.: No “power” struggles: coordinated multi-level power management for the data center. In: ASPLOS (2008)

Cited By

Wilson DAcun FJana SArdanaz FEastep JPaschalidis ICoskun A(2023)An End-to-End HPC Framework for Dynamic Power ObjectivesProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624262(1801-1811)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624262
Carpentieri LD'Antonio MFan KCrisci LCosenza BFicarelli FCesarini DAccordi GGadioli DPalermo GThoman PSalzmann PGschwandtner PWippler MMarchetti FGregori DBeccari A(2023)Domain-Specific Energy Modeling for Drug Discovery and Magnetohydrodynamics ApplicationsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624261(1790-1800)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624261
Fan KRountree BPatki TMarathe ABrink SMcfarlane DGreen EShoga K(2023)msr-genie: Navigating Model Specific Registers Across Processor GenerationsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624146(696-703)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624146
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Published In

cover image Guide Proceedings

High Performance Computing: 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18–22, 2017, Proceedings

Jun 2017

425 pages

ISBN:978-3-319-58666-3

DOI:10.1007/978-3-319-58667-0

Editors:
Julian M. Kunkel
Deutsches Klimarechenzentrum (DKRZ), Hamburg, Germany
,
Rio Yokota
Tokyo Institute of Technology, Tokyo, Japan
,
Pavan Balaji
Argonne National Laboratory, Argonne, IL, USA
,
David Keyes
KAUST, Thuwal, Saudi Arabia

© Springer International Publishing AG 2017.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 June 2017

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Cited By

Wilson DAcun FJana SArdanaz FEastep JPaschalidis ICoskun A(2023)An End-to-End HPC Framework for Dynamic Power ObjectivesProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624262(1801-1811)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624262
Carpentieri LD'Antonio MFan KCrisci LCosenza BFicarelli FCesarini DAccordi GGadioli DPalermo GThoman PSalzmann PGschwandtner PWippler MMarchetti FGregori DBeccari A(2023)Domain-Specific Energy Modeling for Drug Discovery and Magnetohydrodynamics ApplicationsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624261(1790-1800)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624261
Fan KRountree BPatki TMarathe ABrink SMcfarlane DGreen EShoga K(2023)msr-genie: Navigating Model Specific Registers Across Processor GenerationsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624146(696-703)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624146
Muralidhar RBorovica-Gajic RBuyya R(2022)Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and StandardsACM Computing Surveys10.1145/351109454:11s(1-37)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3511094
Kruglov ASucci GVasuez X(2021)Incorporating energy efficiency measurement into CI\CD pipelineProceedings of the 2021 European Symposium on Software Engineering10.1145/3501774.3501777(14-20)Online publication date: 19-Nov-2021
https://dl.acm.org/doi/10.1145/3501774.3501777
Kumar SGupta AKumar VBhalachandra Sde Supinski BHall MGamblin T(2021)CuttlefishProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476163(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476163
Bartolini ABeneventi FBorghesi ACesarini DLibri ABenini LCavazzoni C(2019)Paving the Way Toward Energy-Aware and Automated DatacentreWorkshop Proceedings of the 48th International Conference on Parallel Processing10.1145/3339186.3339215(1-8)Online publication date: 5-Aug-2019
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Ozer GGarg SDavoudi NPoerwawinata GMaiterth MNetti ATafani D(2019)Towards a Predictive Energy Model for HPC Runtime Systems Using Supervised LearningEuro-Par 2019: Parallel Processing Workshops10.1007/978-3-030-48340-1_48(626-638)Online publication date: 26-Aug-2019
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Haidar AAbdelfattah AZounon MWu PPranesh STomov SDongarra J(2018)The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement TechniquesComputational Science – ICCS 201810.1007/978-3-319-93698-7_45(586-600)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-93698-7_45

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