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1 Ergebnisse
1
Scalable Composition and Analysis Techniques for Massive Sc..:
, In:
2022 IEEE 18th International Conference on e-Science (e-Science)
,
Ahn, Dong H.
;
Zhang, Xiaohua
;
Mast, Jeffrey
... - p. 32-43 , 2022
Link:
https://doi.org/10.1109/eScience55777.2022.00018
RT T1 <cTitle>2022 IEEE 18th International Conference on e-Science (e-Science)</cTitle>: T1 <Title>Scalable Composition and Analysis Techniques for Massive Scientific Workflows</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9973491&Exemplar=1&LAN=DE A1 Ahn, Dong H. A1 Zhang, Xiaohua A1 Mast, Jeffrey A1 Herbein, Stephen A1 Di Natale, Francesco A1 Kirshner, Dan A1 Jacobs, Sam Ade A1 Karlin, Ian A1 Milroy, Daniel J. A1 De Supinski, Bronis A1 Van Essen, Brian A1 Allen, Jonathan A1 Lightstone, Felice C. YR 2022 K1 Cloud computing K1 Computational modeling K1 Software K1 Hardware K1 Performance analysis K1 Windows K1 Tuning K1 composite science workflow K1 Aspect Oriented Programming K1 Critical Path Analysis SP 32 OP 43 LK http://dx.doi.org/https://doi.org/10.1109/eScience55777.2022.00018 DO https://doi.org/10.1109/eScience55777.2022.00018 SF ELIB - SuUB Bremen
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