• Media type: Text; E-Article; Electronic Conference Proceeding
  • Title: DEEP: Hybrid Approach for Deep Learning
  • Contributor: Alic, Andy S. [Author]; Antonacci, Marica [Author]; Caballer, Miguel [Author]; Campos, Isabel [Author]; Costantini, Alessandro [Author]; David, Mario [Author]; Dlugolinsky, Stefan [Author]; Donvito, Giacinto [Author]; Duma, Cristina [Author]; Gomes, Jorge [Author]; Hardt, Marcus [Author]; Heredia, Ignacio [Author]; Hluchy, Ladislav [Author]; Ito, Keiichi [Author]; Kozlov, Valentin [Author]; Lloret, Lara [Author]; López García, Alvaro [Author]; Marco, Jesus [Author]; Matyska, Ludek [Author]; Moltó, Germán [Author]; Nguyen, Giang [Author]; Orviz, Pablo [Author]; Plociennik, Marcin [Author]; Šustr, Zdeněk [Author]; [...]
  • Published: KITopen (Karlsruhe Institute of Technologie), 2019-12-05
  • Language: English
  • Keywords: Scientific Software Development ; DATA processing & computer science ; AI/Machine Learning/Deep Learning ; Heterogeneous Systems ; Clouds and Distributed Computing
  • Origination:
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  • Description: 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.