Presentation
CROSS-HPC System Bayesian Optimization with Adaptive Transfer
DescriptionThis 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 parameters in HPC environments usually requires costly evaluations on each target system. To address this, we propose a transfer learning approach that leverages knowledge from a well understood source system to accelerate optimization on new targets. CROSS BOAT uses an adaptive transfer mechanism that combines expected improvement from the target with a progressively weighted source knowledge term, balancing exploration and exploitation. Experiments on simulated HPC systems show that CROSS BOAT outperforms standard Bayesian optimization when target systems differ significantly from the source, achieving up to 24.5% better performance with fewer evaluations. For more similar systems, standard methods remain competitive, underscoring the context-dependent value of transfer learning for faster and more effective HPC system optimization.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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