Big Data in Atmospheric Physics (BINARY) is an interdisciplinary project, involving the research fields Atmospheric Physics and Computer Sciences. Researcher from the Institutes of Atmospheric Physics and Computer Sciences at the Johannes Gutenberg University Mainz investigate important scientific questions in Atmospheric Physics applying modern machine learning methods for big data sets.
In atmospheric physics, as in many other natural sciences, one is confronted with the situation that, due to the improvement of measurement technology and the enormous increase in computing power, huge amounts of data are available that can hardy be evaluated or not at all with conventional means. At the same time, we have a relatively poor understanding of the complex multi-scale system of the atmosphere; many fundamental processes and their impact on the system remain unclear.
Next to building up specialized machine learning methods, a major focus of the BINARY project is the development of new systems and infrastructure to handle the data size and throughput requirements.
Project Partners
Funding
Funded by Carl-Zeiss-Stiftung
Funding period: 03/2020 - 08/2025
External Links
Contact
Big Data in Atmospheric Physics (BINARY)
Johannes Gutenberg University Mainz
Staudingerweg 7
55128 Mainz, Germany
Telefon: +49 6131 39-23 157
E-Mail: binary@uni-mainz.de
Publications
2021
- Frederic Schimmelpfennig, Marc-André Vef, Reza Salkhordeh, Alberto Miranda, Ramon Nou, and André Brinkmann. 2021. Streamlining distributed Deep Learning I/O with ad hoc file systems. In 2021 IEEE International Conference on Cluster Computing (CLUSTER), 169–180. DOI Author/Publisher URL
- Nafiseh Moti, Frederic Schimmelpfennig, Reza Salkhordeh, David Klopp, Toni Cortes, Ulrich Rückert, and André Brinkmann. Simurgh: A Fully Decentralized and Secure NVMM User SpaceFile System. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC). Author/Publisher URL