1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
SHIFT: Exploring the Boundary of RDMA Network Fault Tolerance
Shengkai Lin, Kairui Zhou, Hongtao Zhang +7
Under gang scheduling for large-scale distributed large language model (LLM) training, a single network anomaly can stall or abort an entire job. Current network fault tolerance me…
Isolated Scheduling for Distributed Training Tasks in GPU Clusters
Xinchi Han, Weihao Jiang, Peirui Cao +5
Distributed machine learning (DML) technology makes it possible to train large neural networks in a reasonable amount of time. Meanwhile, as the computing power grows much faster t…