LM4HPC: Towards Effective Language Model Application in High-Performance Computing

TL;DR

This paper introduces LM4HPC, a framework that leverages language models to improve analysis and optimization of high-performance computing software, addressing the lack of HPC-specific support.

Contribution

The paper presents a novel framework tailored for HPC tasks that integrates LMs with HPC datasets and pipelines, supporting rapid evaluation and insights.

Findings

LM4HPC enables quick evaluation of multiple models

It generates insightful leaderboards for HPC tasks

The framework supports HPC-specific datasets and pipelines

Abstract

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.