Data Center
Compute nodes, storage, and network behind CBE, the CLIP Batch Environment.
CBE, the CLIP Batch Environment, combines CPU, GPU, and high-memory compute nodes with a parallel file system and a high-speed network. The platform runs on OpenStack-managed infrastructure.
Overall capacity
- 3,456 CPU cores in C3 nodes
- 112 GPUs: 64 NVIDIA B200 and 48 NVIDIA A100
- 8 TB memory per M3 node, 32 TB across all M3 nodes
- 1 PB BeeGFS parallel file system with 800 GB/s throughput
- 400 Gbit/s RoCEv2 network fabric
Compute node classes
| Class | Best suited for | Nodes | Hardware per node |
|---|---|---|---|
| C3 (CPU) | General-purpose batch and interactive workloads | 18 | 2× AMD EPYC 9655 (192 cores), 768 GB RAM |
| G5 (GPU) | Large model training, multi-GPU jobs, and GPU-accelerated simulation | 8 | 8× NVIDIA B200 |
| G4 (GPU) | Machine learning, deep learning, and GPU-accelerated analyses | 12 | 4× NVIDIA A100 |
| M3 (high memory) | Memory-intensive analyses that don’t fit on standard nodes | 4 | 4× Intel Xeon 8460H, 8 TB RAM |
Storage and network
BeeGFS is a parallel file system: it spreads data across many servers so that many jobs can read and write at the same time. The CBE file system offers 1 PB of capacity and up to 800 GB/s of throughput.
The nodes connect through a 400 Gbit/s Ethernet fabric that uses RoCEv2 (RDMA over Converged Ethernet, version 2). Remote direct memory access (RDMA) lets nodes exchange data directly between their memories, with low latency and little load on the processors.
For details on the current configuration, contact the HPC Servicedesk.
Get help
Account holders can open a ticket in the HPC Servicedesk. Prospective users can email contact@clip.science. See Support for all options.