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Efficiency of e-NR Labeling for On-the-fly Race Detection of Programs with Nested Parallelism

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dc.contributor.authorKim, Sun-Sook-
dc.contributor.authorHa, Ok-Kyoon-
dc.contributor.authorJun, Yong-Kee-
dc.date.accessioned2022-12-27T04:01:57Z-
dc.date.available2022-12-27T04:01:57Z-
dc.date.issued2011-
dc.identifier.issn1865-0929-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/24812-
dc.description.abstractOn-the-fly race detector using Lamport's happened-before relation needs thread labeling scheme for generating unique identifiers which maintain logical concurrency information for the parallel thread. e-NR. labeling creates the NR labels by inheriting from the parent thread a pointer to their shared information, so it requires constant amount of time and space overhead on every fork/join operation. Previous work compares e-NR labeling only with OS labeling using a set of synthetic programs to support that e-NR labeling is efficient in detecting races during an execution of programs with nested parallelism. This paper compares empirically e-NR labeling with BD labeling that shows the same complexity of space and time with OS labeling. The results using OpenMP benchmarks with nested parallelism show that e-NR labeling is more efficient than T-BD labeling by at least 2 times in total monitoring time for race detection, and by at least 3 times in average space for maintaining thread labels.-
dc.language영어-
dc.language.isoENG-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.titleEfficiency of e-NR Labeling for On-the-fly Race Detection of Programs with Nested Parallelism-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.scopusid2-s2.0-79955090504-
dc.identifier.wosid000307265800024-
dc.identifier.bibliographicCitationUBIQUITOUS COMPUTING AND MULTIMEDIA APPLICATIONS, PT II, v.151, pp 191 - +-
dc.citation.titleUBIQUITOUS COMPUTING AND MULTIMEDIA APPLICATIONS, PT II-
dc.citation.volume151-
dc.citation.startPage191-
dc.citation.endPage+-
dc.type.docTypeProceedings Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordPlusCLOCKS-
dc.subject.keywordAuthorEmpirical comparison-
dc.subject.keywordAuthorthread labeling-
dc.subject.keywordAuthorrace detection-
dc.subject.keywordAuthornested parallelism-
dc.subject.keywordAuthore-NR labeling-
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