13 citations · 13 across the 3 of their papers we have counts for
4 papers
Accurate Simulation of Distributed Training Jobs with Network Contention Modeling
Yeonho Yoo, Hyunho Lee, Hyunmok Choi +2
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…
Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs
Wonmi Choi, Sunjae Park, Dohyeok Kwon +4
Collaborative fine-tuning on edge devices adapts large language models to domain-specific data while keeping each device's data local. State-of-the-art (SOTA) collaborative fine-tu…
Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs
Seungwoo Jung, Dohyeok Kwon, Seungmin Cha +4
Mixture-of-experts (MoE) architectures scale large language models efficiently, but they demand massive GPU memory. To cope with such demand, models are commonly compressed to redu…
Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations
Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang +1
Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability res…