DocumentationNews
Welcome to the MetaCentrum Grid Computing News.
Kubernetes News20/01/2026
Your Opinion Matters: Evaluate e-INFRA CZ Services
Are you computing with us? We want to hear from you.
To ensure our computing and cloud services continue to meet the demands of your research, we need your input. Your feedback is crucial for our strategic planning. It helps us identify exactly which aspects of our infrastructure—from job scheduling to storage availability—need improvement or expansion.
If you have already completed the survey, thank you very much for your feedback.
- Privacy & Reward: The survey is anonymous by default. However, if you choose to provide your login, we will credit your MetaCenter account with the equivalent of 0.5 publications as a thank you for your time.
- Deadline: Please submit your responses by 14 February 2026.
- Link: User Satisfaction Survey EN, User Satisfaction Survey CZ
09/01/2025
New Clusters in MetaCentrum: Infrastructure Expansion with CESNET and ZČU Resources
New computing clusters owned by CESNET and the University of West Bohemia (ZČU) have been integrated into the MetaCentrum infrastructure. This hardware update increases CPU and GPU capacity and adds a new SMP node with high shared memory.
- Cluster Adan (CESNET)
adan[1-48].grid.cesnet.cz(6,144 CPUs)
This cluster replaces the previous Adan hardware. It consists of 48 nodes designed for CPU-based calculations (no GPU acceleration).
- Cluster Alfrid (ZČU)
alfrid[1-9].meta.zcu.cz(1,152 CPUs, 24x GPU)
The Alfrid cluster has been updated with 8 GPU nodes (2x NVIDIA L40 or 4x NVIDIA L40S) and 1 SMP node with 4.5 TB RAM and 128 CPUs.
Link: MetaCentrum Hardware List
06/10/2025
Presentations and video from the High-Performance Computing Seminar available
On Thursday, October 2, 2025, the MetaCentrum 2025 High-Performance Computing Seminar took place at the Lávka Club in Prague, with more than 90 participants attending in person and another 40 joining online.
The program focused on data processing and storage, security, working with containers and the cloud, as well as the use of AI models on the MetaCentrum and CERIT-SC infrastructure. Experiences were shared not only by experts from CESNET and CERIT-SC, but also by users from research groups at Masaryk University and Charles University.
The seminar presentations are available on the event page. The same link hosts a video recording of the seminar.
06/10/2025
New Stable AI Models
The AI models DeepSeek R1 0528, Qwen3‑Coder‑480B, and GPT‑OSS‑120B are publicly available through the https://chat.ai.e-infra.cz service.
19/08/2025
New GPU Cluster in MetaCentrum
We are pleased to announce that a new computing cluster fobos.meta.zcu.cz has been successfully integrated into the MetaCentrum infrastructure. Cluster Specification:
- Number of nodes: 20
- Total CPU cores: 1920
- Configuration of each node:
- CPU: 2x AMD EPYC 9454 2.75GHz 48-core 290W Processor
- RAM: 768 GiB
- GPU: 4x NVIDIA L40S 48GB
- Disk: 4x 3.84 TB NVMe
- Network: Ethernet 100Gbit/s, InfiniBand 200Gbit/s
- Power (SPECrate 2017_fp_base): 1160
- Owner: CESNET
Access to Computing Resources: The cluster is available in regular queues. A complete list of available computing servers can be found here: https://metavo.metacentrum.cz/pbsmon2/hardware
08/08/2025
BeeGFS: Fast Shared Scratch
We’re pleased to announce the availability of a new fast shared scratch using the parallel distributed file system BeeGFS on our bee.cerit-sc.cz cluster. This new resource, available as ‘scratch_shared’, is specifically designed for high-performance computing (HPC) needs and offers several advantages for data-intensive and compute-intensive applications.
BeeGFS is ideal for demanding jobs that require:
- Working with large files or a huge number of small files
- Utilizing many threads or processes that read or write in parallel
- Spanning multiple compute nodes
- Sequential computations with intermediate results
Typical Use Cases:
- High-Performance Computing (HPC) – BeeGFS is designed to efficiently handle large files and parallel input/output operations, making it an ideal choice for scientific computing workloads.
- Machine Learning and AI – With BeeGFS, you can train machine learning models faster by accessing large volumes of data with high-throughput and low-latency.
- Simulations, Rendering, Genomics, and Big Data Research – BeeGFS is perfect for handling massive datasets, such as those found in 3D rendering, complex simulations, genomic sequencing, and big data research.
More Information:
Last updated on
