Introduction

Last updated on 2026-02-02 | Edit this page

Overview

Questions

  • How can I run an HPC workflow in a reproducible way?
  • What is the easiest way to create a scaling experiment for an HPC application?
  • How do I identify the optimal runtime configuration for an application on an HPC system?

Objectives

  • Enumerate the benefits of workflow automation.
  • Describe potential use cases for HPC workflow automation using JUBE.

When applying for computing time on HPC systems, applicants are often required to provide measurements on different scales of parallelism. Furthermore, preparing performance measurements often involves an application-specific workflow as well as platform-specific configurations. The objective of this lesson is to enable users of HPC systems to run performance measurements with minimal intervention with high reproducibility, using the Jülich Benchmarking Environment (JUBE) [1].

It is important to understand that, although JUBE emerged as a benchmarking system, it actually is a workflow management system, where application and system benchmarking is one of its potential uses. Further use cases may include running multiple HPC workflows as integration tests during application development and defining reproducible execution workflows for data generation for publications.

Caution

Reproducibility Note

JUBE is an automation tool. This means that it does not intrinsically make your workflows reproducible; rather, it enables you to automate all the actions that could make your workflow reproducible.

The key benefits of using JUBE are:

  • It enables systematic and reproducible benchmarks.
  • It provides a flexible, script-based framework to set up tasks and control various aspects of the execution.
  • It allows for custom workflows that can adapt to different HPC platforms.
  • It provides a powerful tagging system to influence execution of a given workflow.
  • It automatically isolates your workflow steps so concurrent tasks don’t overwrite or re-use files in unintentionally shared execution environments.
  • It enables efficient parameter space exploration.
  • It allows to easily retrieve strings from runtime output and create tables and CSV files.
Discussion

Consider your own workflows on an HPC system. What individual steps are involved? Please discuss this in groups.

Steps may include:

  • Loading and preparing the software environment
  • Building a software package
  • Creating a runtime configuration for your application
  • Creating a scheduler job script
  • Submitting a job to the scheduler
  • Extracting strings from application output.

JUBE is written and maintained by the Jülich Supercomputing Centre at Forschungszentrum Jülich in Germany. It is written in Python 3. Please use a Python 3 interpreter when working with it. The user can write configurations in XML or YAML syntax. It is freely available for download and also available via HPC software package managers, such as Spack and EasyBuild.

Challenge

Challenge

Create an empty workspace for all subsequent challenges in this lesson.

It is best to start with an empty directory. You are free to choose any name, but for the sake of this lesson, we will use jube-workspace/.

SH 

$ mkdir jube-workspace/
$ cd jube-workspace/
$ pwd

OUTPUT 

/home/user/jube-workspace
Key Points
  • Workflow automation aids in getting reproducible results.
  • JUBE enables execution and management of complex workflows on HPC systems.
  • JUBE simplifies exploration of a large parameter space of measurements.
  • JUBE automatically isolates the individual workpackages of a run in separate directories steps to avoid individual concurrent workpackages to overwrite or unintentional reuse of intermediate data.
  • JUBE does not inherently generate fully reproducible workflows, but users can manually record any settings that make the workflow reproducible.