Producing scientific software is a challenge. The high-performance modeling and simulation community, in particular, faces the confluence of disruptive changes in computing architectures and new opportunities (and demands) for greatly improved simulation capabilities, especially through coupling physics and scales. Simultaneously, computational science and engineering (CSE), as well as other areas of science, are experiencing an increasing focus on scientific reproducibility and software quality. Large language models (LLMs), can significantly increase developer productivity through judicious off-loading of tasks. However, models can hallucinate, therefore it is important to have a good methodology to get the most benefit out of this approach.

We propose a tutorial in which attendees will learn about practices, processes, and tools to improve the productivity of those who develop CSE software, increase the sustainability of software artifacts, and enhance trustworthiness in their use. We will focus on aspects of scientific software development that are not adequately addressed by resources developed for industrial software engineering. We will additionally impart state-of-the-art approaches for using LLMs to enhance developer productivity in the context of scientific software development and maintenance. Topics include the design, test-driven development, refactoring, code translation and testing of complex scientific software systems; and conducting computational experiments with reproducibility built in.

The inclusion of LLM assistance on coding related tasks is particularly important to include in any software productivity concern given that it has the potential to change the way development is done. It is particularly challenging to get this assistance in developing research software because of limited training data. We have developed methodologies and tools for software development and translation that use LLMs. The use of these tools and methodologies for hands-on activities will be a part of this tutorial.

Presenters

• Anshu Dubey (Argonne National Laboratory)
• Akash Dhruv (Argonne National Laboratory)