Presentation
STELLAR: Storage Tuning Engine Leveraging LLM Autonomous Reasoning for High-Performance Parallel File Systems
DescriptionI/O performance is crucial to efficiency in data-intensive scientific computing, but tuning large-scale storage systems is complex, costly, and notoriously manpower-intensive, making it inaccessible for most domain scientists. In this study, we propose STELLAR, an autonomous tuner for high-performance parallel file systems. Our evaluations show that STELLAR always selects near-optimal configurations for the parallel file systems within the first five attempts, even for previously unseen applications. STELLAR’s human-like efficiency is fundamentally different from existing auto-tuning methods, which often require hundreds of thousands of iterations to converge. STELLAR achieves this through Retrieval-Augmented Generation, external tool execution, LLM-based reasoning, and a multi-step agent design to stabilize reasoning and combat hallucinations. STELLAR's architecture opens new avenues for addressing complex system optimization problems, especially those characterized by vast search spaces and high exploration costs. Its extremely efficient autonomous tuning will broaden access to I/O performance optimizations for domain scientists with minimal additional resource investment.
Event Type
Paper
TimeWednesday, 19 November 20251:30pm - 1:52pm CST
Location275
Data Analytics, Visualization & Storage