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.

In this tutorial, 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, offering a strategy for the responsible use of LLMs to enhance developer productivity in the context of scientific software development, incorporating testing strategies for the generated code, and discussing reproducibility considerations in the development and use of scientific software.

Presenters

• David E. Bernholdt (Oak Ridge National Laboratory)
• Anshu Dubey (Argonne National Laboratory)