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cs.OS2026
Flare: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus Scale
Weihao Cui, Ji Zhang, Han Zhao +5
The rapid proliferation of large language models has driven the need for efficient GPU training clusters. However, it is challenging due to the frequent occurrence of training anom…
cs.OS2026
Towards High-Goodput LLM Serving with Prefill-decode Multiplexing
Yukang Chen, Weihao Cui, Han Zhao +7
Large Language Model (LLM) serving must meet stringent Service Level Objectives (SLOs) for both the prefill and decode phases. Some existing solutions disaggregate the two phases,…
cs.OS2025
Efficient Function-as-a-Service for Large Language Models with TIDAL
Weihao Cui, Ziyi Xu, Han Zhao +4
Large Language Model (LLM) applications have emerged as a prominent use case for Function-as-a-Service (FaaS) due to their high computational demands and sporadic invocation patter…