research-article

Power-aware dynamic placement of HPC applications

Authors:

Anindya NeogiAuthors Info & Claims

ICS '08: Proceedings of the 22nd annual international conference on Supercomputing

Pages 175 - 184

https://doi.org/10.1145/1375527.1375555

Published: 07 June 2008 Publication History

Abstract

High Performance Computing applications and platforms have been typically designed without regard to power consumption. With increased awareness of energy cost, power management is now an issue even for compute-intensive server clusters. In this work, we investigate the use of power management techniques for high performance applications on modern power-efficient servers with virtualization support. We consider power management techniques such as dynamic consolidation and usage of dynamic power range enabled by low power states on servers. We identify application performance isolation and virtualization overhead with multiple virtual machines as the key bottlenecks for server consolidation. We perform a comprehensive experimental study to identify the scenarios where applications are isolated from each other. We also establish that the power consumed by HPC applications may be application dependent, non-linear and have a large dynamic range. We show that for HPC applications, working set size is a key parameter to take care of while placing applications on virtualized servers. We use the insights obtained from our experimental study to present a framework and methodology for power-aware application placement for HPC applications.

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Cited By

Hall JLathi ALowenthal DPatki T(2023)Evaluating the Potential of Coscheduling on High-Performance Computing SystemsJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_8(155-172)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_8
Yang JSu H(2022)Integrated Resource Management for Fog NetworksSensors10.3390/s2206240422:6(2404)Online publication date: 21-Mar-2022
https://doi.org/10.3390/s22062404
Netti AShin WOtt MWilde TBates N(2021)A Conceptual Framework for HPC Operational Data Analytics2021 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/Cluster48925.2021.00086(596-603)Online publication date: Sep-2021
https://doi.org/10.1109/Cluster48925.2021.00086
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Index Terms

Power-aware dynamic placement of HPC applications
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

cover image ACM Conferences

ICS '08: Proceedings of the 22nd annual international conference on Supercomputing

June 2008

390 pages

ISBN:9781605581583

DOI:10.1145/1375527

General Chairs:
Theo Papatheodorou
University of Patras, Greece
,
Utpal Banerjee
Intel (retired), USA
,
Program Chairs:
Avi Mendelson
Intel, Israel
,
Kyle Gallivan
Florida State University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 June 2008

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ICS08

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ICS08: International Conference on Supercomputing

June 7 - 12, 2008

Island of Kos, Greece

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

Hall JLathi ALowenthal DPatki T(2023)Evaluating the Potential of Coscheduling on High-Performance Computing SystemsJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_8(155-172)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_8
Yang JSu H(2022)Integrated Resource Management for Fog NetworksSensors10.3390/s2206240422:6(2404)Online publication date: 21-Mar-2022
https://doi.org/10.3390/s22062404
Netti AShin WOtt MWilde TBates N(2021)A Conceptual Framework for HPC Operational Data Analytics2021 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/Cluster48925.2021.00086(596-603)Online publication date: Sep-2021
https://doi.org/10.1109/Cluster48925.2021.00086
Nazir BAhmad Z(2021)Dynamic energy efficient load balancing strategy for computational gridConcurrency and Computation: Practice and Experience10.1002/cpe.648434:1Online publication date: 17-Jul-2021
https://doi.org/10.1002/cpe.6484
Netti AMüller MGuillen COtt MTafani DOzer GSchulz MParashar MVlassov VIrwin DMohror K(2020)DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC SystemsProceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3369583.3392674(101-112)Online publication date: 23-Jun-2020
https://dl.acm.org/doi/10.1145/3369583.3392674
Pittino FDiversi RBenini LBartolini A(2020)Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data SelectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.295037839:10(2042-2054)Online publication date: Oct-2020
https://doi.org/10.1109/TCAD.2019.2950378
Tripathi APathak IVidyarthi D(2020)Modified Dragonfly Algorithm for Optimal Virtual Machine Placement in Cloud ComputingJournal of Network and Systems Management10.1007/s10922-020-09538-9Online publication date: 26-May-2020
https://doi.org/10.1007/s10922-020-09538-9
Singh J(2020)Energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud ComputingIntelligent Computing and Applications10.1007/978-981-15-5566-4_17(193-201)Online publication date: 30-Sep-2020
https://doi.org/10.1007/978-981-15-5566-4_17
Patel TTiwari DWeissman JButt ASmirni E(2019)PERQProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3326607(171-182)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3307681.3326607
Kassab ANicod JPhilippe LRehn-Sonigo V(2019)Green Power Constrained Scheduling for Sequential Independent Tasks on Identical Parallel Machines2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00029(132-139)Online publication date: Dec-2019
https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00029
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