Planned Resources

The ICEI project expects to be able to provide researchers with access to resources listed in the table below by end of 2020:

 

Component Site (Country) Total ICEI (100%) Minimum Request Technical Details Expected* Availability
Scalable Computing Services
CINECA-ICEI CINECA (IT) 348 nodes (TBD) 1 node

Thin nodes

April 2021 (TBD)
Interactive Computing Services
Interactive Computing Cluster CINECA (IT) 216 nodes (TBD) 1 node

Fat nodes + GPU nodes

April 2021
Interactive Computing Cluster BSC (ES) 3 nodes 1 node Nodes for interactive access with dense memory March 2021 (TBD)
Virtual Machine (VM) Services
Openstack Compute Cluster CEA (FR) 20 servers
(up to 600 VMs)
1 VM 20 nodes with 2 CPUs (18 cores @ 2.6GHz each), 192GB of RAM March 2021
Nord3 BSC (ES) 84 nodes 1 node

- Nord3: Intel Sandybridge cluster being able to be used as VM host or scalable cluster. 84 nodes dx360m4.

- Each node has the following configuration: 2x Intel SandyBridge-EP E5-2670/1600 20M 8-core at 2.6 GHz and 32 GB RAM

March 2021
CINECA-ICEI Openstack cluster CINECA (IT) 77 nodes (TBD) 1 VM - 2x CPU 8260 Intel CascadeLake, 24 cores, 2.4 GHz
- 768 GB RAM DDR4 2933MT/s
- 2 TB SSD
May 2021 (TBD)
Archival Data Repositories
Archival Data Repository CINECA (IT) 10000 TB (TBD) 1 TB Object store with Swift interface April 2021 (TBD)
Active Archive 2 BSC (ES) 6000 TB 1 TB HSM system with Object storage interface based on Spectrum Scale and Spectrum Archive technology March 2021
Active Data Repositories

HPC Storage @ CINECA

CINECA (IT)

10000 TB (TBD)

1 TB

Lustre Storage (w DDN IME) accessed from HPC clusters

April 2021 (TBD)

HPC Storage @ BSC

BSC (ES)

70 TB

1 TB

GPFS Storage accessed from HPC clusters

March 2021

*The provided time frame is subject to change

 

Fenix Virtual Machine Services Models

 

For detailed information on the Fenix Virtual Machine Services (VM) Models, download the document in PDF. This file offers a description of the Fenix VM Models that may be useful for potential applicants.

 

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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.