Lawrence Berkeley National Laboratory

Effectively leveraging the complex software and hardware I/O stacks of HPC systems to deliver needed I/O performance has been a challenging task for domain scientists. To identify and address I/O issues in their applications, scientists largely rely on I/O experts to analyze the recorded I/O traces of their applications and provide insights into the potential issues. However, due to the limited number of I/O experts and the growing demand for data-intensive applications across the wide spectrum of sciences, inaccessibility has become a major bottleneck hindering scientists from maximizing their productivity. Inspired by the recent rapid progress of large language models (LLMs), in this work we propose IO Navigator (ION), an LLM-based framework that takes a recorded I/O trace of an application as input and leverages the in-context learning, chain-of-thought, and code generation capabilities of LLMs to comprehensively analyze the I/O trace and provide diagnosis of potential I/O issues. Similar to an I/O expert, ION provides detailed justifications for the diagnosis and an interactive interface for scientists to ask detailed questions about the diagnosis. We illustrate ION's applicability by assessing it on a set of controlled I/O traces generated with different I/O issues. We also demonstrate that ION can match state-of-the-art I/O optimization tools and provide more insightful and adaptive diagnoses for real applications. We believe ION, with its full capabilities, has the potential to become a powerful tool for scientists to navigate through complex I/O subsystems in the future.