Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.22017v1</id>\n    <title>IOAgent: Democratizing Trustworthy HPC I/O Performance Diagnosis Capability via LLMs</title>\n    <updated>2026-02-25T15:30:55Z</updated>\n    <link href='https://arxiv.org/abs/2602.22017v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.22017v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>As the complexity of the HPC storage stack rapidly grows, domain scientists face increasing challenges in effectively utilizing HPC storage systems to achieve their desired I/O performance. To identify and address I/O issues, scientists largely rely on I/O experts to analyze their I/O traces and provide insights into potential problems. However, with a limited number of I/O experts and the growing demand for data-intensive applications, inaccessibility has become a major bottleneck, hindering scientists from maximizing their productivity. Rapid advances in LLMs make it possible to build an automated tool that brings trustworthy I/O performance diagnosis to domain scientists. However, key challenges remain, such as the inability to handle long context windows, a lack of accurate domain knowledge about HPC I/O, and the generation of hallucinations during complex interactions.In this work, we propose IOAgent as a systematic effort to address these challenges. IOAgent integrates a module-based pre-processor, a RAG-based domain knowledge integrator, and a tree-based merger to accurately diagnose I/O issues from a given Darshan trace file. Similar to an I/O expert, IOAgent provides detailed justifications and references for its diagnoses and offers an interactive interface for scientists to ask targeted follow-up questions. To evaluate IOAgent, we collected a diverse set of labeled job traces and released the first open diagnosis test suite, TraceBench. Using this test suite, we conducted extensive evaluations, demonstrating that IOAgent matches or outperforms state-of-the-art I/O diagnosis tools with accurate and useful diagnosis results. We also show that IOAgent is not tied to specific LLMs, performing similarly well with both proprietary and open-source LLMs. We believe IOAgent has the potential to become a powerful tool for scientists navigating complex HPC I/O subsystems in the future.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.DC'/>\n    <published>2026-02-25T15:30:55Z</published>\n    <arxiv:comment>Published in the Proceedings of the 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2025)</arxiv:comment>\n    <arxiv:primary_category term='cs.DC'/>\n    <author>\n      <name>Chris Egersdoerfer</name>\n      <arxiv:affiliation>DK</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Arnav Sareen</name>\n      <arxiv:affiliation>DK</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Jean Luca Bez</name>\n      <arxiv:affiliation>DK</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Suren Byna</name>\n      <arxiv:affiliation>DK</arxiv:affiliation>\n    </author>\n    <author>\n      <name> Dongkuan</name>\n      <arxiv:affiliation>DK</arxiv:affiliation>\n    </author>\n    <author>\n      <name> Xu</name>\n    </author>\n    <author>\n      <name>Dong Dai</name>\n    </author>\n    <arxiv:doi>10.1109/IPDPS64566.2025.00036</arxiv:doi>\n    <link href='https://doi.org/10.1109/IPDPS64566.2025.00036' rel='related' title='doi'/>\n  </entry>"
}