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DTSTAMP:20260625T133339Z
LOCATION:Bldg. 6 - 001 - Plenary Room
DTSTART;TZID=Europe/Stockholm:20260630T123800
DTEND;TZID=Europe/Stockholm:20260630T123900
UID:submissions.pasc-conference.org_PASC26_sess129_posC111@linklings.com
SUMMARY:ACMP09 - Medulla: Cluster- and Application-level I/O Performance D
 iagnosis with LLMs
DESCRIPTION:Anish Sathyanarayanan (BITS Pilani K K Birla Goa Campus)\n\nMo
 dern High-Performance Computing (HPC) I/O systems offer immense bandwidth 
 but are notoriously difficult to utilize effectively.\nIdentifying critica
 l bottlenecks is a challenge that persists for both\ndomain scientists and
  cluster administrators. Existing analysis tools\ncan identify common bott
 lenecks but often lack interpretability,\nprocess only single logs at a ti
 me, and fail to capture patterns across\nmultiple runs. Inspired by the se
 mantic reasoning capabilities of\nLarge Language Models (LLMs), we introdu
 ce Medulla, a novel diagnosis framework. Medulla provides two interfaces: 
 an interactive\nCLI for natural language querying and an automated report 
 generator for batch analysis. We evaluate Medulla on the TraceBench\ndiagn
 osis benchmark, demonstrating that it outperforms state-of-the-art tools l
 ike Drishti and IOAgent , achieving an F1-score\nof 0.80 compared to 0.63 
 and 0.68 respectively. Furthermore, using\nproduction logs from the ALCF P
 olaris supercomputer, we show\nhow Medulla identifies critical user-level 
 anomalies, such as massive redundant read patterns, which traditional tool
 s overlook\n\nSession Chair: Tobias Hodel (University of Bern, Switzerland
 )\n\n
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