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Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives

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dc.contributor.author Canal, Ramon es_ES
dc.contributor.author Hernández Luz, Carles es_ES
dc.contributor.author Tornero-Gavilá, Rafael es_ES
dc.contributor.author Cilardo, Alessandro es_ES
dc.contributor.author Massari, Giuseppe es_ES
dc.contributor.author Reghenzani, Federico es_ES
dc.contributor.author Fornaciari, William es_ES
dc.contributor.author Zapater, Marina es_ES
dc.contributor.author Atienza, David es_ES
dc.contributor.author Oleksiak, Ariel es_ES
dc.contributor.author Piatek, Wojciech es_ES
dc.contributor.author Abella, Jaume es_ES
dc.date.accessioned 2021-05-29T03:33:18Z
dc.date.available 2021-05-29T03:33:18Z
dc.date.issued 2020-10 es_ES
dc.identifier.issn 0360-0300 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166962
dc.description © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, Vol. 53, No. 5, Article 95. Publication date: September 2020. https://doi.org/10.1145/3403956 es_ES
dc.description.abstract [EN] Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems. es_ES
dc.description.sponsorship This work has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the FET-HPC Grant Agreement No. 801137 (RECIPE). Jaume Abella was also partially supported by the Ministry of Economy and Competitiveness of Spain under Contract No. TIN2015-65316-P and under Ramon y Cajal Postdoctoral Fellowship No. RYC-2013-14717, as well as by the HiPEAC Network of Excellence. Ramon Canal is partially supported by the Generalitat de Catalunya under Contract No. 2017SGR0962. es_ES
dc.language Inglés es_ES
dc.publisher Association for Computing Machinery es_ES
dc.relation.ispartof ACM Computing Surveys es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject HPC es_ES
dc.subject Supercomputing es_ES
dc.subject Exascale es_ES
dc.subject Reliability es_ES
dc.subject Prediction es_ES
dc.subject Survey es_ES
dc.subject Faults es_ES
dc.subject Failures es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1145/3403956 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/801137/EU/REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RYC-2013-14717/ES/RYC-2013-14717/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Generalitat de Catalunya//2017 SGR 0962/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Canal, R.; Hernández Luz, C.; Tornero-Gavilá, R.; Cilardo, A.; Massari, G.; Reghenzani, F.; Fornaciari, W.... (2020). Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives. ACM Computing Surveys. 53(5):1-32. https://doi.org/10.1145/3403956 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1145/3403956 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 32 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 53 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela S\431095 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Generalitat de Catalunya es_ES
dc.contributor.funder HiPEAC Network of Excellence es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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