DEEP-HybridDataCloud
Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud
| Coordinator | INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK ; USTAV INFORMATIKY, SLOVENSKA AKADEMIA VIED ; CESNET ZAJMOVE SDRUZENI PRAVNICKYCH OSOB ; AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS ; ATOS SPAIN SA ; LABORATORIO DE INSTRUMENTACAO E FISICA EXPERIMENTAL DE PARTICULAS LIP ; Karlsruher Institut für Technologie ; Universitat Politècnica de València ; Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH) ; National Institute for Nuclear Physics |
| Grant period | 2017-11-01 - 2020-04-30 |
| Funding body | European Union |
| Call number | H2020-EINFRA-2017 |
| Grant number | 777435 |
| Identifier | G:(EU-Grant)777435 |
Note: The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables the access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure.
We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams.
Three pilot applications exploiting very large datasets in Biology, Physics and Network Security are proposed, and further pilots for dissemination into other areas like Medicine, Earth Observation, Astrophysics, and Citizen Science will be supported in a testbed with significant HPC resources, including latest generation GPUs, to evaluate the performance and scalability of the solutions.
A DevOps approach will be implemented to provide the chain to ensure the quality of the software and services released, that will also be offered to the developers of research applications.
The project will evolve to TRL8 existing services and technologies at TRL6+, including relevant contributions to the EOSC by the INDIGO-DataCloud H2020 project, that the project will enrich with new functionalities already available as prototypes, notably the support for GPUs and low latency interconnects. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.
Recent Publications
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Contribution to a conference proceedings/Contribution to a book
Cavallaro, G.FZJ* ; Kozlov, V. ; Götz, M. ; Riedel, M.FZJ*
Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
2019Proc. of the 2019 conference on Big Data from Space (BiDS’2019), EUR 29660 EN, ISBN 978-92-76-00034-1, doi:10.2760/848593
Conference on Big Data from Space (BiDS'19), MunichMunich, Germany, 19 Feb 2019 - 21 Feb 20192019-02-192019-02-21
Luxembourg : Publications Office of the European Union 177-180 (2019)2019
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Poster (After Call)
Cavallaro, G.FZJ* ; Kozlov, V. ; Götz, M. ; Riedel, M.FZJ*
Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
2019Conference on Big Data from Space (BiDS'19), MunichMunich, Germany, 19 Feb 2019 - 21 Feb 20192019-02-192019-02-21
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