The Future of Parallel Code Development: Will AI Lead the Way

Artificial intelligence (AI) and large language models (LLMs) specifically, have recently been used to model source code, which has proven to be effective for a variety of software development tasks such as code completion, summarization, translation, and debugging, among others. While the programming languages and software engineering (PL/SE) communities are abuzz with AI-based tools for code development (AIforDev), the application of AIforDev to parallel code has been largely unexplored. Writing, debugging and optimizing parallel code is hard, and the question before the HPC community is -- do AI and LLMs hold the potential for revolutionizing parallel software development. In this talk, I will address the shortcomings of current LLMs when used for parallel code development and how we can close the gap toward building HPC-capable LLMs. I will further highlight emerging areas of research such as improving code modeling capabilities to facilitate various aspects of parallel code development, such as generating correct and efficient parallel code, reasoning about parallel performance, and much more.