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Center for Computational and Theoretical Biology

Resources

Shared Workspaces

We provide workplaces for MSc/PhD students and Postdocs who want to spend some time at the CCTB while working on their thesis, e.g. to analyze and interpret genomic data, perform image analysis, or develop theoretical models. We offer an open and friendly environment where you can discuss your computational problems, and we provide access to our high-performance computing resources. If you are interested, please contact one of the group leaders.

Machines

The CCTB runs its own high-performance compute cluster with 10 compute nodes and 992 cores, running Linux and the SLURM scheduler, connected to our own storage systems (232TB+232TB). This is  the configuration of the individual nodes:

  • jupiter: Intel Xeon E7 4870 @2.4GHz, 80 cores, 1024 GB RAM
  • jupiter2: Intel Xeon E5-4669 @ 2.9GHz, 144 cores, 512GB RAM
  • jupiter3: Intel Xeon E5-4669 @ 2.9GHz, 144 cores, 512GB RAM
  • jupiter4: Intel Xeon E7-8880 @ 3.1GHz, 144 cores, 512GB RAM
  • jupiter5: Intel Xeon E7-8880 @ 3.1GHz, 144 cores, 512GB RAM
  • jupiter6: Intel Xeon E7-8880 @ 3.1GHz, 144 cores, 512GB RAM
  • saturn1: Intel Xeon E7 4850 @2.0 GHz, 80 cores, 512 GB RAM
  • saturn2: Intel Xeon E7 4870 @2.4 GHz, 80 cores, 512 GB RAM
  • 2x uranus[2-3]: AMD Opteron 6274 @2.2 GHz, 32 cores, 192 GB RAM

The GPU node saturn2 additionally includes two graphics processing units for GPGPU computing, one Nvidia Pascal GP104 GPU (8 GB RAM, 2560 cores) and one Nvidia Pascal GP107 GPU (4 GB RAM, 768 cores), both running under CUDA 11.6.

Computer Pool

For courses and tutorials, the CCTB has a computer pool with 14 Raspberry Pi thin clients that are connected to a terminal server with 64 cores and 256 GB RAM using X2GO and Linux. It is even possible to use the HPC cluster from the thin clients!

Software / Expertise

We offer expertise in a large variety of open-source tools and scientific software, from genomics, statistics, modeling to image analysis and machine learning. We also have experience in statistics, data science and programming (R, Julia, Python etc.). If you want to learn these tools or want to collaborate, please talk to us!