The FENIX infrastructure is currently able to provide researchers with access to resources as listed in the table below:
| Component |
Site (Country) |
Minimum Request |
Total Resources offered | Unit |
|---|---|---|---|---|
| Cloud Computing Services | ||||
| GAIA Cloud |
CINECA (IT) | 1 VM |
500 vCPUs 30 TB CEPH storage 30 floating IPs |
vCPUs |
| JSC Cloud | JSC (DE) | 1 VM | 1500 vCPUs, 3000 GB Memory | vCPUs, Memory |
Further details on the components are provided below:
Components from CINECA (Italy):
- GAIA Cloud: GAIA HPC Cloud is an infrastructure combining cloud flexibility with High Performance Computing power. Hosted at CINECA Dama Technopole in Bologna, it is the largest HPC cloud for research in Italy. It supports data processing, analysis and data management services, sensitive workloads, and Kubernetes-based automation. GAIA proves particularly suitable for handling AI workloads, such as fine-tuning and inference, applicable across multiple scientific domains. Based on OpenStack virtualization tool, it provides access to computing nodes (CPU Intel Xeon Sierra Forrest 144 cores 2.2GHz) and CEPH storage (both IOPS-optimized and Capacity-optimized). GAIA operates under an Infrastructure as a Service (IaaS) model. CINECA manages hardware and virtualization, while users control their environments. The system empowers researchers with scalable, customizable, and high-performance resources.
Components from JSC (Germany)
- JSC Cloud: A subset of the hardware listed at the official documentation is available for FENIX. A specific amount of the mentioned AMD EPYC Rome 7742 64-core CPU nodes that feature 256 GB DDR4 memory. Nodes with an additional Nvidia Volta V100 GPU with 16 GB high-bandwidth memory are available.
- Research groups that have already allocated resources on JSC Cloud are eligible to apply. However, such projects will be given lower priority in the evaluation process than projects that have not previously used resources from this cloud infrastructure.
In particular, projects with active allocations will
1. Be assessed to justify the need for additional resources, including why existing allocations cannot meet their needs.
2. Have their application ranked lower unless they demonstrate significant new scientific or technical contributions.
This measure will ensure equitable access to resources and encourage the participation of new users and projects.
