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Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

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Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS 2019)

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Abstract

High-Performance Computing (HPC) is rapidly moving towards the adoption of nodes characterized by an heterogeneous set of processing resources. This has already shown benefits in terms of both performance and energy efficiency. On the other side, heterogeneous systems are challenging from the application development and the resource management perspective. In this work, we discuss some outcomes of the MANGO project, showing the results of the execution of real applications on a emulated deeply heterogeneous systems for HPC. Moreover, we assessed the achievements of a proposed resource allocation policy, aiming at identifying a priori the best resource allocation options for a starting application.

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Notes

  1. 1.

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  2. 2.

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  3. 3.

    www.green500.org.

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Acknowledgments

This research was partially funded by the H2020 EU projects “MANGO” (grant no. 671668) and “RECIPE” (grant no. 801137 [11]).

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Authors and Affiliations

  1. Politecnico di Milano – DEIB, via Ponzio 34/5, Milano, Italy

    Giuseppe Massari, Anna Pupykina, Giovanni Agosta & William Fornaciari

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  1. Giuseppe Massari
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  2. Anna Pupykina
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  3. Giovanni Agosta
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  4. William Fornaciari
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Corresponding author

Correspondence to William Fornaciari .

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Editors and Affiliations

  1. Technical University of Crete and ICS - FORTH, Chania, Greece

    Dionisios N. Pnevmatikatos

  2. INSA Rennes, Rennes Cedex 7, France

    Maxime Pelcat

  3. Fraunhofer IESE, Kaiserslautern, Germany

    Matthias Jung

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Massari, G., Pupykina, A., Agosta, G., Fornaciari, W. (2019). Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms. In: Pnevmatikatos, D., Pelcat, M., Jung, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2019. Lecture Notes in Computer Science(), vol 11733. Springer, Cham. https://doi.org/10.1007/978-3-030-27562-4_34

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  • DOI: https://doi.org/10.1007/978-3-030-27562-4_34

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