Software Sustainability

a track presented at

Argonne Training Program on Extreme-Scale Computing

on 8:30 am - 6:00 pm CDT Friday 8 August 2025

Presenters: Anshu Dubey (Argonne National Laboratory), David E. Bernholdt (Oak Ridge National Laboratory), and Todd Gamblin (Lawrence Livermore National Laboratory)

This page provides detailed information specific to the tutorial event above. Expect updates to this page up to, and perhaps shortly after, the date of the tutorial. Pages for other tutorial events can be accessed from the main page of this site.

Quick Links

Presentation Slides (FigShare)

Description

The BSSw tutorial focuses on issues of developer productivity, software sustainability, and reproducibility in scientific research software, particularly targeting high-performance computers.

Agenda

Time (CDT)	Title	Presenter
8:30 AM	Introduction	David E. Bernholdt (ORNL)
8:35 AM	Motivation and Overview of Best Practices in HPC Software Development	David E. Bernholdt (ORNL)
9:15 AM	Scientific Software Design	Anshu Dubey (ANL)
10:00 AM	Break
10:30 AM	Spack: Package Management for HPC	Todd Gamblin (LLNL)
11:30 AM	Spack Hands-On	Todd Gamblin (LLNL)
12:30 PM	Lunch
1:30 PM	Software Testing and Verification	Anshu Dubey (ANL)
2:30 PM	Refactoring Scientific Software	Anshu Dubey (ANL)
3:00 PM	Break
3:30 PM	Software Licensing	David E. Bernholdt (ORNL)
4:15 PM	Improving Reproducibility Through Better Software Practices	David E. Bernholdt (ORNL)
5:00 PM	Final Exam
6:00 PM	Adjourn

Presentation Slides

The latest version of the slides will always be available at https://doi.org/10.6084/m9.figshare.29816981.

Note that these files may include additional slides that will not be discussed during the tutorial, but questions are welcome.

How to Participate

We want to interact with you! We find these tutorials most interesting and informative (for everyone) if you ask questions and share experiences! We learn too!
Please raise your hand at any time to ask a question

Stay in Touch

After the tutorial please feel free to email questions or feedback to the BSSw tutorial team at bssw-tutorial@lists.mcs.anl.gov.
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Resources from Presentations

Links from the tutorial presentations are listed here for convenience

Module 1: Introduction
Module 2: Motivation and Overview of Best Practices in HPC Software Development
Module 3: Scientific Software Design
- https://enterprisersproject.com/article/2020/6/technical-debt-explained-plain-english
- Code generation example
  - https://github.com/Flash-X/Flash-X/blob/ylee/try_pushTile_spark/source/physics/Hydro/HydroMain/Spark/Hydro_interface.ini
  - This link will work only if you have access to the Flash-X code repository. Please email flash-x@lists.cels.anl.gov with your github username to get access
- References
  - Dubey Anshu, “Insights from the software design of a multiphysics multicomponent scientific code” Computing in Science & Engineering, 2021. DOI:10.1109/MCSE.2021.3069343
  - Dubey, Anshu, et al. “Flash-X: A multiphysics simulation software instrument.” SoftwareX 19 (2022): 101168. DOI:10.1016/j.softx.2022.101168
  - Rudi, Johann, et al. “CG-Kit: Code Generation Toolkit for Performant and Maintainable Variants of Source Code Applied to Flash-X Hydrodynamics Simulations.” arXiv preprint arXiv:2401.03378 (2024).
  - O’Neal, Jared, et al. “Domain-specific runtime to orchestrate computation on heterogeneous platforms.” European Conference on Parallel Processing. Cham: Springer International Publishing, 2021. DOI:10.1007/978-3-031-06156-1_13
  - Dubey, Anshu, et al. “A tool and a methodology to use macros for abstracting variations in code for different computational demands.” Future Generation Computer Systems (2023). DOI:10.1016/j.future.2023.07.014
Module 4: Spack: Package Management for HPC
Module 5: Software Testing and Verification
Module 6: Refactoring Scientific Software
- No links
Module 7: Software Licensing
- See Additional Resources in lesson
Module 8: Improving Reproducibility Through Better Software Practices
- Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences
- Reproducibility and Replicability in Science
- Many Psychology Findings Not As Strong As Claimed
- The War Over Supercooled Water
- Researchers find bug in Python Script may have affected hundreds of studies
- National Science Foundation Data Management Plan Requirements
- Introducing the FAIR Principles for research software
- FAIR Principles for Data
- ACM Reproducible Computational Results
- ACM Artifact Review and Badging
- http://fursin.net/reproducibility.html
- National Information Standards Organization (NISO) on Reproducibility and Badging
- HPC and the Lab Manager
- Writing the Laboratory Notebook
- DIKW pyramid
- Helpful Tools
  - Floating Point Analysis Tools
  - Code Ocean (Cloud platforms - publish and reproduce research code and data)
  - DOIs and hosting of data, code, documents:
    - Zenodo
    - FigShare
- Other Resources:
  - The FAIR Guiding Principles for Scientific Data Management and Stewardship. Mark D. Wilkinson, et al. 2016
  - FAIR4RS (previously linked to ~~www.rd-alliance.org/groups/fair-research-software-fair4rs-wg~~)
  - Editorial: ACM TOMS Replicated Computational Results Initiative. Michael A. Heroux. 2015
  - Enhancing Reproducibility for Computational Methods
  - Simple experiments in reproducibility and technical trust by Mike Heroux and students (work in progress)
  - What every scientist should know about floating-point arithmetic. David Goldberg.
- Jupyter4Science: Better Practices for Using Jupyter Notebooks for Science by Nicole Brewer

Requested Citation

The requested citation the overall tutorial is:

Anshu Dubey, David E. Bernholdt, and Todd Gamblin, Software Sustainability track, in Argonne Training Program on Extreme-Scale Computing, St. Charles, Illinois, 2025. DOI: 10.6084/m9.figshare.29816981.

Individual modules may be cited as Speaker, Module Title, in Software Sustainability track…

Acknowledgements

This tutorial is produced by the IDEAS Productivity project.

This work was supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research (ASCR), and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

This work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Next-Generation Scientific Software Technologies (NGSST) program.