Jobs runtime forecast for JSCC RAS supercomputers using machine learning methods

DOI10.1134/S1995080220120343zbMath1497.68071OpenAlexW3127757627MaRDI QIDQ2225846

G. I. Savin, P. N. Telegin, B. M. Shabanov, D. S. Nikolaev, A. V. Baranov

Publication date: 11 February 2021

Published in: Lobachevskii Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s1995080220120343

zbMATH Keywords

supercomputermachine learninghigh-performance computingrandom forestjob management systemjob runtime predictionsupercomputer job schedulingSUPPZ

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Performance evaluation, queueing, and scheduling in the context of computer systems (68M20)

Uses Software

• Slurm

Cites Work

• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Joint supercomputer center of the Russian Academy of Sciences: present and future
• Data Mining with Decision Trees
• Random forests

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