Summary and Setup

Prerequisite

Before joining Reproducible HPC workflows using JUBE, participants should be able to

• login to an HPC system and navigate a UNIX shell.
• edit text files with an editor available on the HPC system.
• submit jobs to a job scheduler present on the HPC system.
• basic shell, Python, or Perl programming knowledge.
• basic understanding of regular expressions.

Summary

This course provides an introduction into the Jülich Benchmarking Environment (JUBE), a workflow management system created for running performance benchmarks on high-performance computing (HPC) systems. JUBE can also be used for general workflows involving running applications on HPC systems, and this course will further provide an example on how to run the molecular-dynamic code GROMACS and will discuss aspects of workflow reproducibility in JUBE.

🚧 Under Construction 🚧

This lesson is still under construction and will likely change abrubtly and frequently—please be patient.

If you are looking for an introduction to the Jülich Benchmarking Environment (JUBE) in the meantime, feel free to head over to the JUBE tutorial pages of the Jülich Supercomputing Centre.

This is a lesson built with The Carpentries Workbench.

This lessons focuses on execution of workflows on HPC systems, including interaction with its job scheduler. You will therefore have to

1. have access to a system with a job scheduler (e.g., SLURM), and
2. setup the JUBE software environment on that system.

Data Sets

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This lesson uses the GROMACS software as an example HPC code. GROMACS is a free and open-source software suite for high-performance molecular dynamics and output analysis.

Eventually, downloading GROMACS will be part of the overall HPC workflow, but you can download the 2024.5 version of GROMACS from the download page of GROMACS.

Data sets for this lesson are packaged with this lesson and can be downloaded in preparation.

Software Setup

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Conda

Python Virtual Env

The environment.yaml file describes a Conda virtual environment that includes JUBE: the tool you’ll need to run this lesson, as well as some depencencies. To prepare the environment, install Miniconda following the official instructions. Then download the file environment.yaml, which contains all necessary Python packages for this course. Finally, open a shell application and create a new environment:

SH

user@cluster:~$ cd path/to/local/jube-novice
user@cluster:jube-novice$ conda env create -f environment.yaml

Note: The environment will be named “jube-novice” by default. If you prefer another name, add -n <alternate_name> to the command.

One option to install JUBE manually is via a Python virtual environment. To prepare the environment, create a Python virtual environment first, activate it and update pip, before installing jube.

SH

user@cluster:~$ python -m venv jube-venv
user@cluster:~$ source jube-venv/bin/activate
user@cluster:~$ pip install --upgrade pip
user@cluster:~$ pip install http://apps.fz-juelich.de/jsc/jube/download.php?version=latest