Introduction to Biomolecular modeling and Molecular dynamics in HPC

(Classical and Quantum)

Purpose of the course

The purpose of this course is to present to existing and potential users of Molecular Dynamics packages the method, the necessary steps for a successful simulation, common practices, common mistakes. The steps for a complete simulation workflow i.e. system setup up to final properties evaluation will be presented using popular software packages.


After the course the participants should be able to efficiently use their prefered MD application (i.e. NAMD, GROMACS, LAMMPS, CP2K), for molecular modelling and molecular dynamics simulations,  how to create configuration files based on their needs, tuning the models, how to efficiently use the resources based on the simulation details, avoid common mistakes

GRNET HPC resources for research against COVID-19 pandemic

With a sense of responsibility for Public Health and for the Scientific communities that perform exhaustive research to tackle the COVID-19 epidemic, GRNET opens its HPC infrastructure for directly related Research, via high priority – fast track review process.

GRNET provides computing and storage resources for modeling and running simulations, as well as for data processing and Artificial Intelligence.

If you are a researcher and / or member of a research team in Greece, interested and ready to immediately use GRNET national HPC System ARIS  in the COVID-19 directly related Research, please contact us at: hpc-info [at]

RESCHEDULED, ONLINE – PRACE Training Centre: Machine Learning in HPC, 11-12 June 2020.

Machine Learning in HPC


11-12 June 2020


After the course the participants should be able to understand basic principles in Machine Learning and apply basic machine learning methods.

Learn how to efficiently use HPC infrastructures to get
the best performance out of different machine learning tools. How to use these machine learning frameworks like: Tensorflow, PyTorch, Keras, Horovood with hands-on sessions.

Learn using multiple GPUs to significantly shorten the time required to train lots of data, making solving complex problems feasible.

Learn best-practices to avoid common mistakes to efficiently use the HPC infrastracture and overcome the scalability challenges when using parallel computing techniques

PRACE Training Centre: Accelerator Programming, 10-11 Feb.

Accelerator Programming
GPU programming using CUDA

10 – 11 Febryary 2020


The focus is to understand the basics of accelerator programming with the CUDA parallel computing platform model.

CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach known as GPGPU. The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

The course also contains performance and best practice considerations, e.g., gpu libraries, performance optimizations, tools for debugging and profiling.

After the course the participants should have the basic skills needed for utilizing CUDA and OpenACC in new, or existing (own code) programs.