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Exploring the effects of silent data corruption in distributed deep learning training

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dc.contributor.authorRojas, Elvis
dc.contributor.authorPérez, Diego
dc.contributor.authorMeneses, Esteban
dc.date.accessioned2026-03-24T16:53:23Z
dc.date.available2026-03-24T16:53:23Z
dc.date.issued2022
dc.identifier.doidoi: 10.1109/SBAC-PAD55451.2022.00013
dc.identifier.urihttps://hdl.handle.net/20.500.12337/11582
dc.language.isoen
dc.publisher2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)
dc.relationdoi: 10.1109/SBAC-PAD55451.2022.00013
dc.rightsacceso abierto
dc.source2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Bordeaux, France, 2022, pp. 21-30
dc.subjectCOMPUTACIÓN DE ALTO RENDIMIENTO
dc.subjectSUPERCOMPUTADORES
dc.subjectAPRENDIZAJE PROFUNDO
dc.subjectAPRENDIZAJE AUTOMÁTICO
dc.titleExploring the effects of silent data corruption in distributed deep learning training
dc.typeartículo de investigación

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