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Autor(en): Bagbaba, Ayse
Titel: Improving collective I/O performance with machine learning supported auto-tuning
Erscheinungsdatum: 2020
Dokumentart: Konferenzbeitrag
Konferenz: IEEE International Parallel and Distributed Processing Symposium (34th, 2020, Online)
Erschienen in: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops : IPDPSW. Piscataway, NJ : IEEE, 2020. - ISBN 978-1-7281-7445-7, S. 814-821
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-117811
http://elib.uni-stuttgart.de/handle/11682/11781
http://dx.doi.org/10.18419/opus-11764
ISBN: 978-1-7281-7445-7
Bemerkungen: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Zusammenfassung: Collective Input and output (I/O) is an essential approach in high performance computing (HPC) applications. The achievement of effective collective I/O is a nontrivial job due to the complex interdependencies between the layers of I/O stack. These layers provide the best possible I/O performance through a number of tunable parameters. Sadly, the correct combination of parameters depends on diverse applications and HPC platforms. When a configuration space gets larger, it becomes difficult for humans to monitor the interactions between the configuration options. Engineers has no time or experience for exploring good configuration parameters for each problem because of long benchmarking phase. In most cases, the default settings are implemented, often leading to poor I/O efficiency. I/O profiling tools can not tell the optimal default setups without too much effort to analyzing the tracing results. In this case, an auto-tuning solution for optimizing collective I/O requests and providing system administrators or engineers the statistic information is strongly required. In this paper, a study of the machine learning supported collective I/O auto-tuning including the architecture and software stack is performed. Random forest regression model is used to develop a performance predictor model that can capture parallel I/O behavior as a function of application and file system characteristics. The modeling approach can provide insights into the metrics that impact I/O performance significantly.
Enthalten in den Sammlungen:13 Zentrale Universitätseinrichtungen

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