Focus

Interest of intensive computing techniques for the analysis of very large datasets.

Evolve

Intensive computing services exploiting specialized hardware components, like GPUs, low-latency interconnects, and others resources.

Integrate

Hybrid Cloud approach, assuring the interoperability with the existing EOSC platforms and their services.

Define

Solution to offer an adequate integration path to the developers of final applications.

DEEP Hybrid DataCloud

Infraestructure providers, Technical and research communities, education and citizen science

Summary

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 . 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.

News & Events

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

Unless otherwise indicated, all materials created by the DEEP-Hybrid-DataCloud consortium are licensed under a Creative Commons Attribution 4.0 International License.

Licencia de Creative Commons

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