[TEST] Become an HPC User (as a Researcher)

This learning path shows the different elements that one needs to master to make optimal use of High Performance Computing (HPC). Skills acquired in these courses can be applied to various infrastructure facilities (e.g. in-house supercomputer, Snellius, ODISSEI secure supercomputer, Hábrók, Nikhef Data Processing Facility).

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

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