Exploring the Utility of Graph Methods in HPC Thermal Modeling-Reference-Cited by-同舟云学术

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Exploring the Utility of Graph Methods in HPC Thermal Modeling

Published:2024-05-07 Issue: Volume: Page:106-111
ISSN:
Container-title:Companion of the 15th ACM/SPEC International Conference on Performance Engineering
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Author:

Guindani Bruno¹^ORCID,Molan Martin²^ORCID,Bartolini Andrea²^ORCID,Benini Luca³^ORCID

Affiliation:

1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy

2. Department of Electrical, Electronic and Information Engineering, Università degli Studi di Bologna, Bologna, Italy

3. Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland

Funder

European Union Horizon Research and Innovation Actions

Italian Ministry of University and Research - PNRR

European Union NextGenerationEU

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3629527.3652895

Reference11 articles.

1. Proctor: A Semi-Supervised Performance Anomaly Diagnosis Framework for Production HPC Systems

2. Rule-Based Thermal Anomaly Detection for Tier-0 HPC Systems

3. Borghesi, A., Conficoni, C., Lombardi, M., and Bartolini, A. Ms3: A mediterranean-stile job scheduler for supercomputers-do less when it's too hot! In 2015 International Conference on High Performance Computing & Simulation (HPCS) (2015), IEEE, pp. 88--95.

4. M100 ExaData: a data collection campaign on the CINECA’s Marconi100 Tier-0 supercomputer

5. Kipf, T. N., and Welling, M. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations (2017).

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