Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in high-performance computing (HPC) domain tasks has been less than optimal due to the specialized expertise required to interpret the model responses. In response to this challenge, we propose HPC-GPT, a novel LLaMA-based model that has been supervised fine-tuning using generated QA (Question-Answer) instances for the HPC domain. To evaluate its effectiveness, we concentrate on two HPC tasks: managing AI models and datasets for HPC, and data race detection. By employing HPC-GPT, we demonstrate comparable performance with existing methods on both tasks, exemplifying its excellence in HPC-related scenarios. Our experiments on open-source benchmarks yield extensive results, underscoring HPC-GPT's potential to bridge the performance gap between LLMs and HPC-specific tasks. With HPC-GPT, we aim to pave the way for LLMs to excel in HPC domains, simplifying the utilization of language models in complex computing applications.
@article{arxiv.2311.12833,
title = {HPC-GPT: Integrating Large Language Model for High-Performance Computing},
author = {Xianzhong Ding and Le Chen and Murali Emani and Chunhua Liao and Pei-Hung Lin and Tristan Vanderbruggen and Zhen Xie and Alberto E. Cerpa and Wan Du},
journal= {arXiv preprint arXiv:2311.12833},
year = {2023}
}