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Parallel Software and Data Engineering
Recent advances in multi-/many-core revolution technology, distributed data storage, and data parallel analysis have resulted in an increasing move towards data-driven investigations in nearly all fields of science. Parallel computing and data parallel methods become even more important both as enabling technologies and to improve efficiency across all domains. And at the high end we are approaching the exascale era – which will bring lots of cores, accelerators, and deep memory hierarchies. At the same time the amount of data that needs to be managed and analyzed is increasing tremendously thanks to new instruments like NGS, the SKA, or EISCAT-3D, data collected by online sensors, data available on the Internet, but also data produced by ever larger simulations made possible through more powerful HPC systems. Parallel and distributed techniques are an essential tool to deal with this data deluge area. The SeRC Parallel Software and Data Engineering (PSDE) community will allow a holistic view on computing and data and will be a key core area within SeRC with collaborations with many of the application areas but also with other core areas, for instance visualization.
Here are some examples of our research:
- Pediatric systems medicine: evaluating needs and opportunities using congenital heart block as a case study
- Enabling efficient and future-proof HPC applications: High-level component-based programming frameworks for heterogeneous parallel systems
- Object-Oriented Modeling and Simulation with Modelica
- Scalable Performance Monitoring
- Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation
- An atlas of combinatorial transcriptional regulation in mouse and man