Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems
Knowledge Distillation (KD) enables training smaller student models under the guidance of larger teacher models, and the widely adopted TRL library implements it. Yet, TRL treats both models symmetrically, missing opportunities to exploit their pronounced asymmetry in memory footprint, and communication requirements. This paper presents an HPC-aware methodology for KD that decouples teacher and student partitioning efficiently. Our approach achieves up to 67% higher samples-per-second than TRL by avoiding unnecessary teacher-model data structures and selecting the best split strategy. We combine vertical and horizontal partitioning of models, deriving an analytical expression that identif...
| Authors | Adrian P. Dieguez, Victor Conchello Vendrell, Alex Batlle et al. |
|---|---|
| Primary category | cs.DC |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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