Abstract
As High Performance Computing (HPC) systems get closer to exascale performance, job dispatching strategies become critical for kee** system utilization high while kee** waiting times low for jobs competing for HPC system resources. In this paper, we take a data-driven approach and investigate whether better dispatching decisions can be made by transforming the log data produced by an HPC system into useful knowledge about its workload. In particular, we focus on job duration, develop a data-driven approach to job duration prediction, and analyze the effect of different prediction approaches in making dispatching decisions using a real workload dataset collected from Eurora, a hybrid HPC system. Experiments on various dispatching methods show promising results.
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Notes
- 1.
The Italian Inter University Consortium for High Performance Computing (http://www.cineca.it).
- 2.
Altair PBS Works (http://www.pbsworks.com/).
- 3.
SLURM Workload Manager (https://slurm.schedmd.com/).
- 4.
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Acknowledgments
We thank Dr. A. Bartolini, Prof. L. Benini, Prof. M. Milano and Dr. M. Lombardi for fruitful discussions on the work presented here and for providing access to the Eurora data, together with the SCAI group in Cineca. We acknowledge the Cineca PM-HPC award allowing access to HPC resources. C. Galleguillos has been supported by Postgraduate Grant PUCV 2017. A. Sîrbu has been partially funded by the E.U. project SoBigData Research Infrastructure—Big Data and Social Mining Ecosystem (grant agreement 654024).
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Galleguillos, C., Sîrbu, A., Kiziltan, Z., Babaoglu, O., Borghesi, A., Bridi, T. (2018). Data-Driven Job Dispatching in HPC Systems. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_37
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