Soft error resilience in Big Data kernels through modular analysis.
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| Title: | Soft error resilience in Big Data kernels through modular analysis. |
|---|---|
| Authors: | Chen, Sui1, Bronevetsky, Greg2, Peng, Lu1 lpeng@lsu.edu, Li, Bin3, Fu, Xin4 |
| Source: | Journal of Supercomputing. Apr2016, Vol. 72 Issue 4, p1570-1596. 27p. |
| Subjects: | Soft errors, High performance computing research, Big data, Computer software developers, Computer software development |
| Abstract: | The shrinking processor feature and operating voltages of processor circuits are making them increasingly vulnerable to soft faults, which calls for fault resilience techniques at both the software and hardware levels under the big data context. To assist software developers in writing fault-resilient big data applications, we propose the tool ErrorSight, which helps them to focus their efforts on code regions and data structures that are most vulnerable to soft errors, understand how numerical errors propagate through the program, and apply fault resilience techniques effectively. ErrorSight achieves this through efficient generation of error profiles leveraging the predictive power of the Boosted Regression Tree model. We use four big data kernels to illustrate the modular analysis mechanism of ErrorSight and show its usefulness in the development of numerical fault-resilience in Big Data. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 114515149 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Soft error resilience in Big Data kernels through modular analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Sui%22">Chen, Sui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bronevetsky%2C+Greg%22">Bronevetsky, Greg</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Peng%2C+Lu%22">Peng, Lu</searchLink><relatesTo>1</relatesTo><i> lpeng@lsu.edu</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Bin%22">Li, Bin</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Fu%2C+Xin%22">Fu, Xin</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Apr2016, Vol. 72 Issue 4, p1570-1596. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Soft+errors%22">Soft errors</searchLink><br /><searchLink fieldCode="DE" term="%22High+performance+computing+research%22">High performance computing research</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+developers%22">Computer software developers</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The shrinking processor feature and operating voltages of processor circuits are making them increasingly vulnerable to soft faults, which calls for fault resilience techniques at both the software and hardware levels under the big data context. To assist software developers in writing fault-resilient big data applications, we propose the tool ErrorSight, which helps them to focus their efforts on code regions and data structures that are most vulnerable to soft errors, understand how numerical errors propagate through the program, and apply fault resilience techniques effectively. ErrorSight achieves this through efficient generation of error profiles leveraging the predictive power of the Boosted Regression Tree model. We use four big data kernels to illustrate the modular analysis mechanism of ErrorSight and show its usefulness in the development of numerical fault-resilience in Big Data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-016-1682-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1570 Subjects: – SubjectFull: Soft errors Type: general – SubjectFull: High performance computing research Type: general – SubjectFull: Big data Type: general – SubjectFull: Computer software developers Type: general – SubjectFull: Computer software development Type: general Titles: – TitleFull: Soft error resilience in Big Data kernels through modular analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Sui – PersonEntity: Name: NameFull: Bronevetsky, Greg – PersonEntity: Name: NameFull: Peng, Lu – PersonEntity: Name: NameFull: Li, Bin – PersonEntity: Name: NameFull: Fu, Xin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 72 – Type: issue Value: 4 Titles: – TitleFull: Journal of Supercomputing Type: main |
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