LM4HPC: Towards Effective Language Model Application in High-Performance
  Computing
Le Chen Pei-Hung Lin Tristan Vanderbruggen Chunhua Liao Murali Emani Bronis de Supinski
In recent years, language models (LMs), such as GPT-4, have been widely used
in multiple domains, including natural language processing, visualization, and
so on. However, applying them for analyzing and optimizing high-performance
computing (HPC) software is still challenging due to the lack of HPC-specific
support. In this paper, we design the LM4HPC framework to facilitate the
research and development of HPC software analyses and optimizations using LMs.
Tailored for supporting HPC datasets, AI models, and pipelines, our framework
is built on top of a range of components from different levels of the machine
learning software stack, with Hugging Face-compatible APIs. Using three
representative tasks, we evaluated the prototype of our framework. The results
show that LM4HPC can help users quickly evaluate a set of state-of-the-art
models and generate insightful leaderboards.