Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11477
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dc.contributor.authorÖz, Işıl-
dc.contributor.authorArslan, Sanem-
dc.date.accessioned2021-11-06T09:49:35Z-
dc.date.available2021-11-06T09:49:35Z-
dc.date.issued2021-
dc.identifier.issn0885-7458-
dc.identifier.issn1573-7640-
dc.identifier.urihttps://doi.org/10.1007/s10766-021-00707-0-
dc.identifier.urihttps://hdl.handle.net/11147/11477-
dc.description.abstractWith the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal of Parallel Programmingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSoft error analysisen_US
dc.subjectFault injectionen_US
dc.subjectParallel programmingen_US
dc.subjectMachine learningen_US
dc.titlePredicting the soft error vulnerability of parallel applications using machine learningen_US
dc.typeArticleen_US
dc.authorid0000-0002-8310-1143-
dc.institutionauthorÖz, Işıl-
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume49en_US
dc.identifier.issue3en_US
dc.identifier.startpage410en_US
dc.identifier.endpage439en_US
dc.identifier.wosWOS:000633744600001en_US
dc.identifier.scopus2-s2.0-85103371927en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s10766-021-00707-0-
dc.identifier.scopusqualityQ3-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextembargo_20240101-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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