Server / HPC Cluster

• Subject Area: Medicine
• Term: Funded in 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 527049502

We develop algorithms and tools in various bioinformatics areas, such as network biology, genetic epidemiology, systems medicine, and others. These help us to gain new insights from molecular data and sensor data and generate new hypotheses that can be used to explore new diagnosis and treatment approaches for various diseases. Due to the amount of data involved, we need several hundred terabytes of of storage space. Much of our work also places very high demands on the computing hardware, which we cannot meet with conventional grid-computing architectures. For example, biological networks often comprise thousands of nodes and hundreds of thousands of edges and require a particularly large main memory to be processed adequately. In order to investigate these and other data effectively, a high number of processor cores is required at the same time. This combination is not represented in conventional computing centers, since they offer only either many processor cores or a large amount of main memory. More precise artificial intelligence- based analyses of molecular and sensor data can only be performed in highly parallel shared-memory architectures. Due to the heterogeneous hardware requirements in different projects, we need a cluster that can satisfy these needs flexibly and, depending on the project, can provide enough memory, processor cores, main memory, and GPUs, if necessary.

• DFG Programme: Major Research Instrumentation
• Major Instrumentation: Data Science in Biomedicine Cluster
• Instrumentation Group: 7000 Datenverarbeitungsanlagen, zentrale Rechenanlagen
• Applicant Institution: Technische Universität Braunschweig
• Leader: Professor Dr. Tim Kacprowski