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DTSTAMP:20260202T201259Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251121T080000
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UID:submissions.supercomputing.org_SC25_sess620_post155@linklings.com
SUMMARY:CROSS-HPC System Bayesian Optimization with Adaptive Transfer
DESCRIPTION:Abrar Hossain and Kishwar Ahmed (University of Toledo)\n\nThis
  paper introduces CROSS BOAT (Cross HPC System Bayesian Optimization with 
 Adaptive Transfer), a novel method for efficient parameter tuning in high 
 performance computing (HPC) systems. Optimizing the many configurable para
 meters in HPC environments usually requires costly evaluations on each tar
 get system. To address this, we propose a transfer learning approach that 
 leverages knowledge from a well understood source system to accelerate opt
 imization on new targets. CROSS BOAT uses an adaptive transfer mechanism t
 hat combines expected improvement from the target with a progressively wei
 ghted source knowledge term, balancing exploration and exploitation. Exper
 iments on simulated HPC systems show that CROSS BOAT outperforms standard 
 Bayesian optimization when target systems differ significantly from the so
 urce, achieving up to 24.5% better performance with fewer evaluations. For
  more similar systems, standard methods remain competitive, underscoring t
 he context-dependent value of transfer learning for faster and more effect
 ive HPC system optimization.\n\nTag: Research & ACM SRC Posters\n\nRegistr
 ation Category: Technical Program Reg Pass\n\nSession Chairs: Kento Sato (
 RIKEN Center for Computational Science (R-CCS)); Anja Gerbes (Georg-August
 -Universität Göttingen); and Chris Schlipalius (Pawsey Supercomputing Rese
 arch Centre; Commonwealth Scientific and Industrial Research Organisation 
 (CSIRO), Australia)\n\n
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