Earth/Environmental Science Applications on Cloud and HPC Infrastructures
Convener: Horst Schwichtenberg | Co-conveners: Wim Som de Cerff, Christopher Kadow, Paul Kucera
| Thu, 11 Apr, 14:00–15:45
Room M1
| Attendance Fri, 12 Apr, 08:30–10:15
Hall X1

This session aims to highlight Earth Science research concerned with state of the art computational and data infrastructures such as Clouds (commercial, on premis, European EOSC ) and HPC (Supercomputer, Clusters, accelerator-based systems (GPGPU, FPGA)).

We will focus on data intensive workflows (scientific workflows) between Infrastructures e.g. European data and compute infrastructures down to complex analysis workflows on an HPC system e.g. in situ coupling frameworks.

The session presents an opportunity for everyone to present and learn from results achieved, success stories and experience gathered during the process of study, adaptation and exploitation of these systems.

Further contributions are welcome that showcase middleware and tools developed to support Earth Science applications on Cloud and HPC, e.g. to increase effectiveness, robustness or ease of use.

Topics of interest include:
- Data intensive Earth Science applications and how they have been adapted to different HPC infrastructures
- Data mining software stacks in use for large environmental datasets
- HPC simulation and High Performance Data Analytics e.g. code coupling, in-situ workflows
- Experience with Earth Science applications in Cloud environments e.g. solutions on Amazon Web Services, Google Earth Engine, Microsoft Azure, and Earth Science simulation codes in private and European Cloud infrastructures (Open Science Cloud)
- Tools and services for Earth Science data management, workflow execution, web services and portals to ease access to compute resources.
- Tools and middleware for Earth Science applications on Grid, Cloud and on High Performance Computing infrastructures.
- Earth Science Application using cloud native solutions
- Innovative Evaluation and Prediction Applications for Large Earth Science Datasets