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cs.DC2025★ 1 cited
RailS: Load Balancing for All-to-All Communication in Distributed Mixture-of-Experts Training
Heng Xu, Zhiwei Yu, Chengze Du +5
Training Mixture-of-Experts (MoE) models introduces sparse and highly imbalanced all-to-all communication that dominates iteration time. Conventional load-balancing methods fail to…
cs.DC2025
Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection
Yuhang Zhou, Zhibin Wang, Peng Jiang +12
Training large language models faces frequent interruptions due to various faults, demanding robust fault-tolerance. Existing backup-free methods, such as redundant computation, dy…
cs.DC2025
Temporal-Aware GPU Resource Allocation for Distributed LLM Inference via Reinforcement Learning
Chengze Du, Zhiwei Yu, Heng Xu +3
The rapid growth of large language model (LLM) services imposes increasing demands on distributed GPU inference infrastructure. Most existing scheduling systems follow a reactive p…