Resumen
H2020 E-INFRA Project
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 postprocessing
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.