SC23 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Research Posters Archive

Modeling Parallel Programs Using Large Language Models

Authors: Daniel Nichols (University of Maryland); Aniruddha Marathe, Harshitha Menon, and Todd Gamblin (Lawrence Livermore National Laboratory); and Abhinav Bhatele (University of Maryland)

Abstract: In the past year a large number of large language model (LLM) based tools for software development have been released. These tools have the capability to assist developers with many of the difficulties that arise from the ever-growing complexity in the software stack. As we enter the exascale era, with a diverse set of emerging hardware and programming paradigms, developing, optimizing, and maintaining parallel software is becoming burdensome for developers. While LLM-based coding tools have been instrumental in revolutionizing software development, mainstream models are not designed, trained, or tested on High Performance Computing (HPC) problems. We present a LLM fine-tuned on HPC data and demonstrate its effectiveness in HPC code generation, OpenMP parallelization, and performance modeling.

Best Poster Finalist (BP): no

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