Author

Yeonho Yoo

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Preprint Sep 2026

Accurate Simulation of Distributed Training Jobs with Network Contention Modeling

Trace-driven simulation is widely used to evaluate distributed training (DT) jobs in GPU clusters, but existing simulators either ignore network contention or approximate it with a fixed penalty. This misses how scheduling decisions determine which jobs share server network interfaces and inter-server links, thereby ch...

Yeonho Yoo, Hyunho Lee, Hyun-Eem Choi et al. · 0 citations

Preprint Sep 2026

Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs

Xronos is proposed, a collaborative fine-tuning framework for heterogeneous CPU edge devices that uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck.

Wonmi Choi, Sunjae Park, Dohyeok Kwon et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs

PARSER is a new residual sparsification method that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error, and achieves this by introducing output importance, which measures the actual contribution to the expert output error.

Seung-Woo Jung, Dohyeok Kwon, Seung-Min Cha et al. · 0 citations