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cs.DC2025
Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference
Rongzhi Li, Ruogu Du, Zefang Chu +8
Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. Thi…
cs.DC2025
Understanding Stragglers in Large Model Training Using What-if Analysis
Jinkun Lin, Ziheng Jiang, Zuquan Song +13
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines. Su…