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UID:submissions.supercomputing.org_SC24_sess533_post175@linklings.com
SUMMARY:Interactive and Tool-Agnostic ML-Driven Workflow for Automated HPC
  Performance Modeling
DESCRIPTION:Zhaobin Zhu and Sarah Neuwirth (Johannes Gutenberg University 
 Mainz)\n\nThis work presents an automated, reproducible, ML-based performa
 nce modeling workflow for HPC systems. The proposed workflow fully automat
 es data generation, preprocessing, ML model training and validation. Since
  the proposed approach is generic and not tailored to a specific applicati
 on, our workflow can be utilized for performance modeling across a wide ra
 nge of performance domains. The prototype implementation is based on the J
 UBE workflow environment, through which a user-friendly interactive consol
 e is realized. The effectiveness of the automated workflow is demonstrated
  with a case study on I/O bandwidth modeling and prediction.\n\nRegistrati
 on Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: A
 yesha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen 
 National High Performance Computing Center); Sally Ellingson (University o
 f Kentucky); and Alan Sussman (University of Maryland)\n\n
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