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Desh : deep learning for system health prediction of lea..:
, In:
Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing
,
Das, Anwesha
;
Mueller, Frank
;
Siegel, Charles
. - p. 40-51 , 2018
Link:
https://dl.acm.org/doi/10.1145/3208040.3208051
RT T1 <cTitle>Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing</cTitle>: T1 <Title>Desh : deep learning for system health prediction of lead times to failure in HPC</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3208051&Exemplar=1&LAN=DE A1 Das, Anwesha A1 Mueller, Frank A1 Siegel, Charles A1 Vishnu, Abhinav PB ACM YR 2018 K1 HPC K1 LSTM K1 anomaly detection K1 deep learning K1 failure prediction K1 lead times K1 log mining K1 node failures K1 Computer systems organization K1 Dependable and fault-tolerant systems and networks K1 Reliability K1 General and reference K1 Cross-computing tools and techniques K1 Evaluation K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 40 OP 51 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3208040.3208051 DO https://dl.acm.org/doi/10.1145/3208040.3208051 SF ELIB - SuUB Bremen
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