Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems
Kolmogorov-Arnold Networks (KANs) replace the fixed activation functions and linear weights of Multi-Layer Perceptrons (MLPs) with learnable univariate functions on network edges, offering improved interpretability and, in some settings, competitive parameter efficiency. While the approximation properties of KANs have received considerable attention, their behavior under distributed, multi-GPU training has not been systematically characterized. This paper presents an empirical scalability study of data-parallel KAN training on multi-node, multi-GPU high-performance computing (HPC) infrastructure, evaluated along four dimensions: strong scaling, weak scaling, communication overhead, and mo...
| Authors | Guangneng Chen, David Garcia Selfa, Pablo Quesada Barriuso |
|---|---|
| Primary category | cs.DC |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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