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arXiv · 2311.12833

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Xianzhong Ding·Le Chen·Murali Emani·Chunhua Liao·Pei-Hung Lin·Tristan Vanderbruggen·Zhen Xie·Alberto E. Cerpa·Wan Du
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Abstract

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.

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Xianzhong Ding, Le Chen, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Zhen Xie, Alberto E. Cerpa, Wan Du. 2023-10-03. HPC-GPT: Integrating Large Language Model for High-Performance Computing. https://doi.org/10.1145/3624062.3624172

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