Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
http://dx.doi.org/10.18419/opus-13290
Langanzeige der Metadaten
DC Element | Wert | Sprache |
---|---|---|
dc.contributor.advisor | Resch, Michael M. (Prof. Dr.-Ing. Dr. h.c. Dr. h.c. Prof. E.h.) | - |
dc.contributor.author | Bağbaba, Ayşe | - |
dc.date.accessioned | 2023-07-17T12:39:41Z | - |
dc.date.available | 2023-07-17T12:39:41Z | - |
dc.date.issued | 2023 | de |
dc.identifier.issn | 0941-4665 | - |
dc.identifier.other | 1852825545 | - |
dc.identifier.uri | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-133091 | de |
dc.identifier.uri | http://elib.uni-stuttgart.de/handle/11682/13309 | - |
dc.identifier.uri | http://dx.doi.org/10.18419/opus-13290 | - |
dc.description.abstract | Data access is a considerable challenge because of the scalability limitation of I/O. In addition, some applications spend most of their total execution times in I/O. This causes a massive slowdown and wastage of useful computing resources. Unfortunately, there is not any one-size-fits-all solution to the I/O problems, so I/O becomes a limiting factor for such applications. Parallel I/O is an essential technique for scientific applications running on high-performance computing systems. Typically, parallel I/O stacks offer many parameters that need to be tuned to achieve an I/O performance as good as possible. Unfortunately, there is no default best configuration of these parameters; in practice, these differ not only between systems but often also from one application use case to the other. However, scientific users might not have the time or the experience to explore the parameter space sensibly and choose a proper configuration for each application use case. I present a line of solutions to this problem containing a machine learning supported auto-tuning system which uses performance modelling to optimize I/O performance. I demonstrate the value of these solutions across applications and at scale. | en |
dc.language.iso | en | de |
dc.publisher | Stuttgart : Höchstleistungsrechenzentrum, Universität Stuttgart | de |
dc.relation.ispartofseries | HLRS;25 | - |
dc.rights | info:eu-repo/semantics/openAccess | de |
dc.subject.ddc | 004 | de |
dc.title | Optimizing I/O performance with machine learning supported auto-tuning | en |
dc.type | doctoralThesis | de |
ubs.dateAccepted | 2023-01-16 | - |
ubs.fakultaet | Energie-, Verfahrens- und Biotechnik | de |
ubs.fakultaet | Zentrale Einrichtungen | de |
ubs.institut | Institut für Höchstleistungsrechnen | de |
ubs.institut | Höchstleistungsrechenzentrum Stuttgart (HLRS) | de |
ubs.publikation.seiten | 2, xx, 114 | de |
ubs.publikation.typ | Dissertation | de |
ubs.schriftenreihe.name | HLRS | de |
ubs.thesis.grantor | Energie-, Verfahrens- und Biotechnik | de |
Enthalten in den Sammlungen: | 04 Fakultät Energie-, Verfahrens- und Biotechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
abagbaba_phd.pdf | 2,16 MB | Adobe PDF | Öffnen/Anzeigen |
Alle Ressourcen in diesem Repositorium sind urheberrechtlich geschützt.