Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12232
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dc.contributor.authorTopçu, Buraken_US
dc.contributor.authorÖz, Işılen_US
dc.date.accessioned2022-08-01T13:16:36Z-
dc.date.available2022-08-01T13:16:36Z-
dc.date.issued2022-
dc.identifier.urihttps://doi.org/10.1109/PDP55904.2022.00025-
dc.identifier.urihttps://hdl.handle.net/11147/12232-
dc.descriptionThis work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK), Grant No: 119E011.en_US
dc.description.abstractAs Graphics Processing Units (GPUs) have evolved to deliver performance increases for general-purpose computations as well as graphics and multimedia applications, soft error reliability becomes an important concern. The soft error vulnerability of the applications is evaluated via fault injection experiments. Since performing fault injection takes impractical times to cover the fault locations in complex GPU hardware structures, prediction-based techniques have been proposed to evaluate the soft error vulnerability of General-Purpose GPU (GPGPU) programs based on the hardware performance characteristics.In this work, we propose ML-based prediction models for the soft error vulnerability evaluation of GPGPU programs. We consider both program characteristics and hardware performance metrics collected from either the simulation or the profiling tools. While we utilize regression models for the prediction of the masked fault rates, we build classification models to specify the vulnerability level of the programs based on their silent data corruption (SDC) and crash rates. Our prediction models achieve maximum prediction accuracy rates of 96.6%, 82.6%, and 87% for masked fault rates, SDCs, and crashes, respectively.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer graphicsen_US
dc.subjectComputer graphics equipmenten_US
dc.subjectSoft erroren_US
dc.subjectRadiation hardeningen_US
dc.titlePredicting the soft error vulnerability of GPGPU applicationsen_US
dc.typeConference Objecten_US
dc.authorid0000-0002-8310-1143en_US
dc.institutionauthorTopçu, Buraken_US
dc.institutionauthorÖz, Işılen_US
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000827652300016en_US
dc.identifier.scopus2-s2.0-85129624617en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.publicationProceedings - 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2022en_US
dc.identifier.doi10.1109/PDP55904.2022.00025-
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.relation.isbn978-166546958-6en_US
dc.relation.doi10.1109/PDP55904.2022en_US
dc.description.startpage108en_US
dc.description.endpage115en_US
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.author.dept01. Izmir Institute of Technology-
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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