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READEX Tool Suite for Energy-efficiency Tuning of HPC Applications

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SEM4HPC '17: Proceedings of the 2017 Workshop on Software Engineering Methods for Parallel and High Performance Applications

Pages 11 - 12

https://doi.org/10.1145/3085158.3091994

Published: 26 June 2017 Publication History

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Abstract

The European Union Horizon 2020 READEX project is developing a tool suite for dynamic energy tuning of HPC applications. The tool suite performs an analysis during design-time before production run to construct a tuning model encapsulated with the best-found configurations that are then fed to the runtime tuning library. The library switches the configurations at runtime to adapt the application for energy-efficiency.

References

[1]

Michael Gerndt, Eduardo Cesar, and Siegfried Benkner (Eds.). 2015. Automatic Tuning of HPC Applications - The Periscope Tuning Framework. Shaker Verlag, Aachen.

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A. Knupfer, C. Rossel, D. an Mey, S. Biersdorff, K. Diethelm, D. Eschweiler, M. Geimer, M. Gerndt, D. Lorenz, A. D. Malony, W. E. Nagel, Y. Oleynik, P. Philippen, P. Saviankou, D. Schmidl, S. S. Shende, R. Tschuter, M. Wagner, B. Wesarg, and F. Wolf. 2012. Score-P: A Joint Performance Measurement Runtime Infrastructure for Periscope, Scalasca, TAU, and Vampir. In Tools for High Performance Computing 2011, H. Brunst, M. Muller, W. E. Nagel, and M. M. Resch (Eds.). Springer, Berlin, 79--91.

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Robert Mijakovic, Michael Firbach, and Michael Gerndt. 2016. An architecture for flexible auto-tuning: The Periscope Tuning Framework 2.0. In Green High Performance Computing (ICGHPC), 2016 2nd International Conference on. IEEE, 1--9.

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Y. Oleynik, M. Gerndt, J. Schuchart, P. G. Kjeldsberg, and W. E. Nagel. 2015. Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX). In Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, C. Plessl, D. El Baz, G. Cong, J. M. P. Cardoso, L. Veiga, and T. Rauber (Eds.). IEEE, Piscataway, 347--350.

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Joseph Schuchart, Michael Gerndt, Per Gunnar Kjeldsberg, Michael Lysaght, David Horak, Lubomír Ríha, Andreas Gocht, Mohammed Sourouri, Madhura Kumaraswamy, Anamika Chowdhury, Magnus Jahre, Kai Diethelm, Othman Bouizi, Umbreen Sabir Mian, Jakub Kruzík, Radim Sojka, Martin Beseda, Venkatesh Kannan, Zakaria Bendifallah, Daniel Hackenberg, and Wolfgang E Nagel. 2017. The READEX formalism for automatic tuning for energy efficiency. Computing (2017), 1--9.

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Cited By

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Silva CVilaça RPereira ABessa R(2024)A review on the decarbonization of high-performance computing centersRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114019189(114019)Online publication date: Jan-2024
https://doi.org/10.1016/j.rser.2023.114019
Simbeye DMakweba D(2021)Improvement of Energy-Efficiency in High Performance Computing (HPC)International Journal of ICT Research in Africa and the Middle East10.4018/IJICTRAME.29083510:2(30-51)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.4018/IJICTRAME.290835
Alcaraz JSikora ACesar E(2018)Dynamic Tuning of OpenMP Memory Bound Applications in Multisocket Systems using MATEWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229748(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229748

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READEX Tool Suite for Energy-efficiency Tuning of HPC Applications

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Published In

SEM4HPC '17: Proceedings of the 2017 Workshop on Software Engineering Methods for Parallel and High Performance Applications

June 2017

36 pages

ISBN:9781450350006

DOI:10.1145/3085158

Program Chairs:
Atul Kumar
IBM Research, India
,
Santonu Sarkar
BITS Pilani, K K Birla Goa Campus, Goa, India
,
Michael Gerndt
Technical University of Munich, Germany

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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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: 26 June 2017

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HPDC '17

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University of Arizona
SIGARCH

HPDC '17: The 26th International Symposium on High-Performance Parallel and Distributed Computing

June 26, 2017

DC, Washington, USA

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SEM4HPC '17 Paper Acceptance Rate 3 of 5 submissions, 60%;

Overall Acceptance Rate 8 of 16 submissions, 50%

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Silva CVilaça RPereira ABessa R(2024)A review on the decarbonization of high-performance computing centersRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114019189(114019)Online publication date: Jan-2024
https://doi.org/10.1016/j.rser.2023.114019
Simbeye DMakweba D(2021)Improvement of Energy-Efficiency in High Performance Computing (HPC)International Journal of ICT Research in Africa and the Middle East10.4018/IJICTRAME.29083510:2(30-51)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.4018/IJICTRAME.290835
Alcaraz JSikora ACesar E(2018)Dynamic Tuning of OpenMP Memory Bound Applications in Multisocket Systems using MATEWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229748(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229748

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The READEX formalism for automatic tuning for energy efficiency

The READEX Project for Dynamic Energy Efficiency Tuning

Tools and methods for measuring and tuning the energy efficiency of HPC systems

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The READEX formalism for automatic tuning for energy efficiency

The READEX Project for Dynamic Energy Efficiency Tuning

Tools and methods for measuring and tuning the energy efficiency of HPC systems

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