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DEEP: Hybrid Approach for Deep Learning

Alic, Andy S.; Antonacci, Marica; Caballer, Miguel; Campos, Isabel; Costantini, Alessandro; David, Mario; Dlugolinsky, Stefan; Donvito, Giacinto; Duma, Cristina; Gomes, Jorge; Hardt, Marcus ORCID iD icon; Heredia, Ignacio; Hluchy, Ladislav; Ito, Keiichi; Kozlov, Valentin ORCID iD icon; Lloret, Lara; López García, Alvaro; Marco, Jesus; Matyska, Ludek; ... mehr

Abstract (englisch):

The DEEP-HybridDataCloud project researches on intensive computing techniques such as deep learning, that require specialized GPU hardware to explore very large datasets, through a hybrid-cloud approach that enables access to such resources. We understand the needs of our user communities and help them to combine their services in a way that encapsulates technical details the end user does not have to deal with.

Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Poster
Publikationsmonat/-jahr 06.2019
Sprache Englisch
Identifikator KITopen-ID: 1000100541
HGF-Programm 46.12.02 (POF III, LK 01) Data Activities
Veranstaltung ISC High Performance (2019), Frankfurt am Main, Deutschland, 16.06.2019 – 20.06.2019
Projektinformation DEEP-HybridDataCloud (EU, H2020, 777435)
Externe Relationen Siehe auch
Schlagwörter AI/Machine Learning/Deep Learning, Clouds and Distributed Computing, Heterogeneous Systems, Scientific Software Development
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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