Better Scientific Software
a tutorial presented at
Presenters: David E. Bernholdt (Oak Ridge National Laboratory), Anshu Dubey (Argonne National Laboratory), Patricia A. Grubel (Los Alamos National Laboratory), Rinku K. Gupta (Argonne National Laboratory), and David M. Rogers (Oak Ridge 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.Last updated: 2021-07-26
On this Page
- Presentation Slides
- How to Participate
- Hands-On Exercises
- Stay in Touch
- Resources from Presentations
- Requested Citation
The computational science and engineering (CSE) community is in the midst of an extremely challenging period created by the confluence of disruptive changes in computing architectures, demand for greater scientific reproducibility, and new opportunities for greatly improved simulation capabilities, especially through coupling physics and scales. Computer architecture changes require new software design and implementation strategies, including significant refactoring of existing code. Reproducibility demands require more rigor across the entire software endeavor. Code coupling requires aggregate team interactions including integration of software processes and practices. These challenges demand large investments in scientific software development and improved practices. Focusing on improved developer productivity and software sustainability is both urgent and essential.
This tutorial will provide information and hands-on experience with software practices, processes, and tools explicitly tailored for CSE. Goals are improving the productivity of those who develop CSE software and increasing the sustainability of software artifacts. We discuss practices that are relevant for projects of all sizes, with emphasis on small teams, and on aggregate teams composed of small teams. Topics include software licensing, effective models, tools, and processes for small teams (including agile workflow management), reproducibility, and scientific software testing (including automated testing and continuous integration).
|Day||Time (CEST)||Module||Topic||Presenter||Time (EDT)|
|Thu||2:00pm-2:10pm||00||Introduction and Setup||David E. Bernholdt, ORNL||8:00am-8:10am|
|Thu||2:10pm-2:30pm||01||Motivation and Overview of Best Practices in HPC Software Development||David E. Bernholdt, ORNL||8:10am-8:30am|
|Thu||2:30pm-3:00pm||02||Agile Methodologies||Rinku Gupta, ANL||8:30am-9:00am|
|Thu||3:00pm-3:30pm||03||Git Workflows||Patricia A. Grubel, LANL||9:00am-9:30am|
|Thu||3:30pm-4:00pm||04||Scientific Software Design||Anshu Dubey, ANL||9:30am-10:00am|
|Thu||4:30pm-5:00pm||05||Improving Reproducibility Through Better Software Practices||David E. Bernholdt, ORNL||10:30am-11:00am|
|Thu||5:00pm-5:15pm||06||Agile Methodologies Redux||Rinku Gupta, ANL||11:00am-11:15am|
|Fri||2:00pm-2:25pm||07||Software Testing Introduction||Patricia A. Grubel, LANL||8:00am-8:25am|
|Fri||2:25pm-2:40pm||08||Testing Walk-Through||David M. Rogers, ORNL||8:25am-8:40am|
|Fri||2:40pm-3:00pm||09||Testing Complex Software||David M. Rogers, ORNL||8:40am-9:00am|
|Fri||3:00pm-3:30pm||10||Continuous Integration||David M. Rogers, ORNL||9:00am-9:30am|
|Fri||4:30pm-5:15pm||11||Refactoring Scientific Software||Anshu Dubey, ANL||10:30am-11:15am|
|Fri||5:15pm-5:30pm||12||Summary||Anshu Dubey, ANL||11:15am-11:30am|
- The latest version of the slides will always be available at https://doi.org/10.6084/m9.figshare.14256257.
- Note that these files may include additional slides that will not be discussed during the tutorial, but questions are welcome.
- Version History:
- v2: Updates to 08-testing-walkthrough to match presented version
- v1: Initial publication
How to Participate
Please use Zoom chat to ask questions at any time. We will respond in chat or verbally as opportunities permit.
During breaks, the instructors are happy to hold further discussions with anyone interested.
The hands-on exercises for this tutorial are based around a simple numerical model using the one-dimensional heat equation. The example is described briefly in the repository’s README file, and in greater detail in the ATPESC Hands-On lesson. The ATPESC version focuses on the numerical aspects of the model. But for this tutorial, we’re focused on how to make the software better from a quality perspective, so you don’t need to understand the math to do these exercises.
For the purposes of these hands-on exercises, you should imagine you’ve inherited an early version of the hello-numerical-world software from a colleague who’s left the project, and you’ve been assigned to get it into better shape so that it can be used in the next ATPESC summer school.
The repository you’ll be working with is on GitHub: bssw-tutorial/hello-numerical-world-2021-06-isc. Note: most of the screenshots will refer to the generic “hello-numerical-world” repository rather than the one specifically for this tutorial.
List of Hands-On Exercises
Note that the exercise numbers align with the presentation modules. Not every module has exercises (yet).
- Exercise 0: Setting up the Prerequisites. Setup the accounts needed for these exercises.
- Exercise 2: Agile Methodologies. You’ll use GitHub issues and project boards to setup a simple “personal kanban” board.
- Exercise 3: Git Workflows. You’ll fork our hello-numerical-world repository, create a feature branch, and make a pull request
- Exercise 6: Agile Redux. You’ll create epic, story, and task issues for the refactoring task and track them on a kanban board
- Exercise 8: Software Testing. You’ll establish a simple continuous integration workflow and then refine it, adding code coverage assessment
- Exercise 10: Continuous Integration. You’ll establish a simple continuous integration workflow and then refine it, adding code coverage assessment
- Exercise 11: Refactoring Scientific Software. You’ll perform a small, well-defined refactoring exercise
Stay in Touch
After the tutorial please feel free to email questions or feedback to the BSSw tutorial team at email@example.com.
If you want to do the hands-on exercises, we’re happy to provide feedback on your pull requests and issues, even after the end of the tutorial.
To find out about future events organized by the IDEAS Productivity Project, you can subscribe to our mailing list (usually ~2 messages/month).
For monthly updates on the Better Scientific Software site, subscribe to our monthly digest.
Resources from Presentations
These are the links included in the tutorial presentations, included here for easier access
- Module 0: Introduction and Setup
- Module 1: Motivation and Overview of Best Practices in HPC Software Development
- Best Practices for Scientific Computing
- Good Enough Practices in Scientific Computing
- Linux Foundation Core Infrastructure Initiative (CII) Best Practices](https://bestpractices.coreinfrastructure.org/en) Badging Program
- Portability and Sustainability Improvement Planning
- Module 2: Agile Methodologies
- Agile Manifesto
- A-team tools for Agile practices
- Policies: A Code of Conduct for Open Source Projects
- Module 3: Git Workflows
- Module 4: Software Design
- Module 5: 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
- The FAIR Guiding Principles for Scientific Data Management and Stewardship. Mark D. Wilkinson, et al. 2016
- National Science Foundation Data Management Plan Requirements
- SC21 Reproducibility Initiative
- ACM Transactions on Mathematical Software (TOMS)
- ACM Artifact Review and Badging
- National Information Standards Organization (NISO) on Reproducibility and Badging
- Floating Point Analysis Tools
- Code Ocean (Cloud platforms - publish and reproduce research code and data)
- DOIs and hosting of data, code, documents:
- Editorial: ACM TOMS Replicated Computational Results Initiative. Michael A. Heroux. 2015
- Simple experiments in reproducibility and technical trust by Mike Heroux and students (work in progress)
- Module 6: Agile Methodologies Redux
- Module 7: Testing Introduction
- Python Build and Test Framework: pyscaffold.org
- Build-Link-Test CMake Framework: llnl-blt.readthedocs.io
- Static Source Analysis (C++): clang-tidy
- Static Source Analysis (python): flake8 and pylint
- Code Coverage Webservices: codecov and coveralls
- Tutorials for code coverage: Online Tutorial, Another example
- Development Practices Survey Article
- Module 8: Testing Walk-Through
- See Hands-on activities.
- Module 9: Testing Complex Applications
- Useful How-to resources on test and test suites on ideas-productivity.org
- Related Articles: 1, 2
- Module 10: Continuous Integration
- Module 11: Refactoring Scientific Software
- Module 12: Summary
- COVID-19 epidemiology saga
- Productivity and Sustainability Improvement Planning
- Better Scientific Software web site
- COVID-19 epidemiology saga
The requested citation the overall tutorial is: David E. Bernholdt, Anshu Dubey, Rinku K. Gupta, Patricia A. Grubel, and David Rogers, Better Scientific Software tutorial, in ISC High Performance, online, 2021. DOI: 10.6084/m9.figshare.14256257.
Individual modules may be cited as Speaker, Module Title, in Better Scientific Software tutorial…
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.