Descrizione del progetto
Creare un servizio di cloud ibrido per i ricercatori dell’UE
Negli ultimi anni l’Unione europea ha riconosciuto l’importanza di sostenere i ricercatori fornendo loro i dati e le risorse necessarie per svolgere il proprio lavoro. Tuttavia, a causa dell’elevato numero di ricercatori, può essere difficile per loro accedere ad apparecchiature informatiche sufficientemente potenti per le tecniche e i processi di calcolo intensivo. Il progetto DEEP-HybridDataCloud, finanziato dall’UE, mira a sviluppare un servizio di cloud ibrido per i ricercatori dell’Unione. Questo servizio fornirà l’accesso alle risorse informatiche attraverso il cloud, consentendo loro di lavorare in modo più efficiente. Inoltre, il progetto metterà in atto un approccio DevOps che consentirà di migliorare l’efficienza e la manutenzione.
Obiettivo
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.
Campo scientifico
- natural sciencescomputer and information sciencessoftwaresoftware development
- natural sciencescomputer and information sciencescomputer securitynetwork security
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social sciencespolitical sciencespolitical policiescivil society
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-EINFRA-2017
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
28006 Madrid
Spagna