Authors
Academic Research Computing, Office of the Provost, University of Richmond, Richmond, VA 23173
NØMAD: Lightweight HPC Monitoring and Diagnostics with Machine Learning-Based Failure Prediction
DOI: https://doi.org/10.5334/jors.686 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jan 22, 2026
|Accepted on: Feb 24, 2026
|Published on: Mar 12, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2026 João Filipe Riva Tonini, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.