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Probabilistic Job History Conversion and Performance Model Generation for Malleable Scheduling Simulations

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High Performance Computing (ISC High Performance 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13999))

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Abstract

Malleability support in supercomputing requires several updates to system software stacks. In addition to this, updates to applications, libraries and the runtime systems of distributed memory programming models are also necessary. Because of this, there are relatively few applications that have been extended or developed with malleability support. As a consequence, there are no job histories from production systems that include sufficient malleable job submissions for scheduling research. In this paper, we propose a solution: a probabilistic job history conversion. This conversion allows us to evaluate malleable scheduling heuristics via simulations based on existing job histories. Based on a configurable probability, job arrivals are converted into malleable versions, and assigned a malleable performance model. This model is used by the simulator to evaluate its changes at runtime, as an effect of malleable operations being applied to it.

This work has received funding under the European Commission’s EuroHPC and Horizon 2020 programmes under grant agreements no. 955606 (DEEP-SEA) and 956560 (REGALE).

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Notes

  1. 1.

    We do not take the impact of manufacturing process variations into account here.

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Authors and Affiliations

  1. Technical University of Munich, Garching, Germany

    Isaías Comprés, Eishi Arima & Martin Schulz

  2. Fraunhofer ITWM, Kaiserslautern, Germany

    Tiberiu Rotaru & Rui Machado

Authors
  1. Isaías Comprés
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  2. Eishi Arima
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  3. Martin Schulz
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  4. Tiberiu Rotaru
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  5. Rui Machado
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Corresponding author

Correspondence to Isaías Comprés .

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Editors and Affiliations

  1. University of New Mexico, Albuquerque, NM, USA

    Amanda Bienz

  2. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  3. Université Paris-Saclay, Gif sur Yvette, France

    Marc Baboulin

  4. CERFACS, Toulouse, France

    Carola Kruse

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Comprés, I., Arima, E., Schulz, M., Rotaru, T., Machado, R. (2023). Probabilistic Job History Conversion and Performance Model Generation for Malleable Scheduling Simulations. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_7

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