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cs.DC2026
ARGUS: Production-Scale Tracing and Performance Diagnosis for over 10,000-GPU Clusters
Jiasheng Zhou, Longbin Zeng, Clavis Chen +5
Large-scale LLM training requires always-on, fine-grained observability for effective performance diagnosis at scale. Coarse resource monitors alone cannot localize root causes, an…
cs.DC2026
LiveR: Fine-Grained Elasticity via Live Reconfiguration for Model Training
Haoyuan Liu, Kairui Zhou, Shuyao Qi +4
To reduce user costs and maximize cluster utilization, large model training increasingly leverages volatile but inexpensive GPU capacity, such as spot instances and reclaimable res…