Authors: Tanya van Goch, Margriet Miedema
Contributors: Carlos Teijeiro Barjas, Marco Verdicchio
Status: Under development
Target audience: Researchers
Prerequisites:
It is recommended that you have some basic knowledge in research data management. Background knowledge can be acquired by doing the following trainings:
- Essentials 4 Data Support
- Data Carpentry
- Software capentry with python
Code refinery workshop
- Reproducible software environments using containers
Learning objectives:
- Using advanced cluster usage for computational science
- Using the Amsterdam modelling suite in PHC systems
- Porting of codes for next generation GPU architectures
- Using SIM codes series
- Managing data with IRODS and compute workflows
- Managing sensitive data and EUDAT services
- Parallel and GPU programming with Python
- Using containers in HPC systems
- Understanding high performance machine learning
- Basic parallel programming with MPI and OPENMP
- Using MD analysis for efficient simulation pre- and post-processing
- Using Apache Spark for big data
- Local and remote visualisation
- Manipulate simulated data
- Using interfaces (Jupyter, Rstudio)
- Programming Fortran
- Programming C
- Using Blender for advanced scientific visualisation