Adrian P. Dieguez
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126
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Distributed×1AI×1ML×1
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2026
Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems
This paper proposes an HPC-aware methodology for Knowledge Distillation (KD) that decouples teacher and student partitioning efficiently, achieving up to 67% higher samples-per-second than the widely adopted TRL library.
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