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
; Heredia, Ignacio; Hluchy, Ladislav; Ito, Keiichi; Kozlov, Valentin
; Lloret, Lara; López García, Alvaro; Marco, Jesus; Matyska, Ludek;
; Heredia, Ignacio; Hluchy, Ladislav; Ito, Keiichi; Kozlov, Valentin
; Lloret, Lara; López García, Alvaro; Marco, Jesus; Matyska, Ludek; 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 |