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SHAMan: A Versatile Auto-tuning Framework for Costly and Noisy HPC Systems

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Optimization and Learning (OLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1684))

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Abstract

Most of the software of modern computer systems come with many configurable parameters that control the system’s behavior and its interaction with the underlying hardware.

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Acknowledgments

This work has been partially funded by the IO-SEA project [4], funded by the European High-Performance Computing Joint Undertaking (JU) and by BMBF/DLR under grant agreement No 955811. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and France, the Czech Republic, Germany, Ireland, Sweden and the United Kingdom.

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Authors and Affiliations

  1. Atos BDS R &D Data Management, Echirolles, France

    S. Robert & P. Couvée

  2. University of UPSacaly-UVSQ, Guyancourt, France

    S. Zertal

Authors
  1. S. Robert
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  2. S. Zertal
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  3. P. Couvée
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Corresponding author

Correspondence to S. Zertal .

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Editors and Affiliations

  1. Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Paris-Est, Paris, France

    Amir Nakib

  4. Université de Lille, Lille, France

    El-Ghazali Talbi

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Robert, S., Zertal, S., Couvée, P. (2022). SHAMan: A Versatile Auto-tuning Framework for Costly and Noisy HPC Systems. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-22039-5_8

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