Abstract
With the increase of demand for computing resources and the struggle to provide the necessary energy, power-aware resource management is becoming a major issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer’s resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different from the others.
We argue that the first step toward properly managing energy is to deeply understand the energy consumption of the workload, which involves predicting the workload’s power consumption and exploiting it by using smart power-aware scheduling algorithms. Crucial questions are (i) how sophisticated a prediction method needs to be to provide accurate workload power predictions, and (ii) to what point an accurate workload’s power prediction translates into efficient energy management.
In this work, we propose a method to predict and exploit HPC workloads’ power consumption, with the objective of reducing the supercomputer’s power consumption while maintaining the management (scheduling) performance of the RJMS. Our method exploits workload submission logs with power monitoring data, and relies on a mix of light-weight power prediction methods and a classical EASY Backfillling inspired heuristic.
We base this study on logs of Marconi 100, a 980 servers supercomputer. We show using simulation that a light-weight history-based prediction method can provide accurate enough power prediction to improve the energy management of a large scale supercomputer compared to energy-unaware scheduling algorithms. These improvements have no significant negative impact on performance.
INP—Institute of engineering Univ. Grenoble Alpes.
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Notes
- 1.
The term homogeneous in this paper means that all computing nodes have the same CPU/accelerators configuration.
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Acknowledgements and Artifact Availability
Experiments presented in this paper were carried out using the Grid’5000 testbed. This work was supported by the research program on Edge Intelligence of the Multi-disciplinary Institute on Artificial Intelligence MIAI at Grenoble Alpes (ANR-19-P3IA-0003), ENERGUMEN (ANR-18-CE25-0008), the France 2030 NumPEx Exa-SofT (ANR-22-EXNU-0003) and Cloud CareCloud (ANR-23-PECL-0003) projects managed by the French National Research Agency (ANR), REGALE (H2020-JTI-EuroHPC-2019-1 agreement n. 956560), and LIGHTAIDGE (HORIZON-MSCA-2022-PF-01 agreement n. 101107953). A CC-BY public copyright licence has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grants’ open access conditions. We thank Salah Zrigui for starting the study on the job energy profiles. We also thank Francesco Antici for curating and sharing the Marconi100 dataset. The experiments described in this article have been made with open science and reproducibility concerns in mind. Code, data and documentation to reproduce our work is available on Zenodo [18].
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Carastan-Santos, D., Da Costa, G., Poquet, M., Stolf, P., Trystram, D. (2024). Light-Weight Prediction for Improving Energy Consumption in HPC Platforms. In: Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., Schreiber, M. (eds) Euro-Par 2024: Parallel Processing. Euro-Par 2024. Lecture Notes in Computer Science, vol 14801. Springer, Cham. https://doi.org/10.1007/978-3-031-69577-3_11
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