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Automatic Performance Modeling of Configurable Scientific Software

dc.contributor.authorSchmid, Larissa
dc.contributor.editorHerrmann, Andrea
dc.date.accessioned2025-01-08T13:30:59Z
dc.date.available2025-01-08T13:30:59Z
dc.date.issued2024
dc.description.abstractModern software is configurable and allows users to set many parameters according to their needs. Due to many non-functional parameters, usually, the same functionality can be achieved with varying performance. Performance models express application performance as functions of input parameters, helping users and developers understand application behavior. Automatic performance modeling approaches can generate performance models automatically from empirical measurements of the software. Current modeling approaches employ heuristics for deciding which configurations to measure, resulting in a trade-off between the cost of measurements and accuracy of the model. To overcome this trade-off, we propose approaches to derive the smallest necessary measurement setup based on results of a system analysis, and to automatically identify performance-irrelevant options. Our evaluation with real-world applications show that we can significantly decrease cost of performance modeling while maintaining accuracy of the resulting models.en
dc.identifier.issn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45536
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 44, Heft 4
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectperformance
dc.subjectperformance model
dc.subjectmeasurement
dc.subjectconfiguration
dc.titleAutomatic Performance Modeling of Configurable Scientific Softwareen
dc.typeText/Conference Paper
mci.conference.dateNovember 6-7, 2024
mci.conference.locationLinz, Austria
mci.conference.sessiontitle15th Symposium on Software Performance 2024
mci.reference.pages21-23

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