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Energy estimation for MPI broadcasting algorithms in large scale HPC systems

Authors:

M. E. M. Diouri,

O. Glück,

J.-C. Mignot,

L. LefèvreAuthors Info & Claims

EuroMPI '13: Proceedings of the 20th European MPI Users' Group Meeting

Pages 111 - 116

https://doi.org/10.1145/2488551.2488565

Published: 15 September 2013 Publication History

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Abstract

Future supercomputers will gather hundreds of millions of communicating cores. The movement of data in such systems will be very energy consuming. We address in this paper the issue of energy consumption of data broadcasting in such large scale systems. To this end, we propose a framework to estimate the energy consumed by different MPI broadcasting algorithms for various execution settings. Validation results show that our estimations are highly accurate and allow to select the least consuming broadcasting algorithm.

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Lin WShi FWu WLi KWu GMohammed A(2020)A Taxonomy and Survey of Power Models and Power Modeling for Cloud ServersACM Computing Surveys10.1145/340620853:5(1-41)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406208
Idrees SFanfakh A(2018)Performance and Energy Consumption Prediction of Randomly Selected Nodes in Heterogeneous ClusterNew Trends in Information and Communications Technology Applications10.1007/978-3-030-01653-1_2(21-34)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-01653-1_2
O’brien KPietri IReddy RLastovetsky ASakellariou R(2017)A Survey of Power and Energy Predictive Models in HPC Systems and ApplicationsACM Computing Surveys10.1145/307881150:3(1-38)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3078811
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Energy estimation for MPI broadcasting algorithms in large scale HPC systems

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Information

Published In

EuroMPI '13: Proceedings of the 20th European MPI Users' Group Meeting

September 2013

289 pages

ISBN:9781450319034

DOI:10.1145/2488551

General Chair:
Jack Dongarra
University of Tennessee
,
Program Chairs:
Javier Garcia Blas
University Carlos III, Spain
,
Jesus Carretero
University Carlos III, Spain

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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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 15 September 2013

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EuroMPI '13: 20th European MPI Users's Group Meeting

September 15 - 18, 2013

Madrid, Spain

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EuroMPI '13 Paper Acceptance Rate 22 of 47 submissions, 47%;

Overall Acceptance Rate 66 of 139 submissions, 47%

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Lin WShi FWu WLi KWu GMohammed A(2020)A Taxonomy and Survey of Power Models and Power Modeling for Cloud ServersACM Computing Surveys10.1145/340620853:5(1-41)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406208
Idrees SFanfakh A(2018)Performance and Energy Consumption Prediction of Randomly Selected Nodes in Heterogeneous ClusterNew Trends in Information and Communications Technology Applications10.1007/978-3-030-01653-1_2(21-34)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-01653-1_2
O’brien KPietri IReddy RLastovetsky ASakellariou R(2017)A Survey of Power and Energy Predictive Models in HPC Systems and ApplicationsACM Computing Surveys10.1145/307881150:3(1-38)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3078811
Dayarathna MLi YWen YFan R(2017)Energy consumption analysis of data stream processingSoftware—Practice & Experience10.1002/spe.245847:10(1443-1462)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1002/spe.2458
Dayarathna MWen YFan R(2016)Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2015.248118318:1(732-794)Online publication date: 27-Jan-2016
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Diouri MGlück OLefèvre LMignot J(2015)Providing Green Services in HPC Data Centers: A Methodology Based on Energy EstimationHandbook on Data Centers10.1007/978-1-4939-2092-1_9(287-323)Online publication date: 17-Mar-2015
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Diouri MGlück OLefèvre L(2013)SESAMESProceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing10.1109/GreenCom-iThings-CPSCom.2013.52(187-194)Online publication date: 20-Aug-2013
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Leakage energy estimates for HPC applications

Involving users in energy conservation: a case study in scientific clouds

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