Using Fenix Resources for COVID-19 Research

29 Apr 2020

Fenix is a distributed infrastructure providing HPC, cloud and data resources to European research and scientists, in particular including those performing research related to the current COVID-19 pandemic. The resources are provided by five European supercomputing centres, namely BSC (Spain), CEA (France), CINECA (Italy), CSCS (Switzerland) and JSC (Germany). They are being made available through the Human Brain Project and PRACE. Resources are ready for allocations at short notice.

The following services make significant hardware resources available, which can be composed in different ways to facilitate specific research workflows:

●    Interactive Computing Services: Quick access to single compute servers to analyse and visualise data interactively using, e.g., interactive frameworks like Jupyter notebooks.
●    Scalable Computing Services: Massively parallel HPC systems that are suitable for highly parallel simulations, high-throughput data analysis tasks or deep learning jobs.
●    Virtual Machine Services: Service for deploying virtual machines (VMs) in a stable and controlled environment that is, for example, suitable for deploying web-services for sharing data.
●    Active Data Repositories: Site-local data repositories close to computational and/or visualization resources that are based on high-performance parallel file systems.
●    Archival Data Repositories: Federated data stores realised through Swift-based object stores that allow sharing of large-scale data sets.

For researchers and scientists working on topics related to the current COVID-19 pandemic we provide consultancy support and help for getting access to these resources with the goal of minimising the efforts on your side. We furthermore offer support to enable the use of these resources.

In case you expect the aforementioned resources and services to be useful for your research, we encourage you to ask for a meeting to explore options and opportunities via

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Fenix has received funding from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858.