2 citations · 2 across the 5 of their papers we have counts for
6 papers
CascadeEP: Asynchronous Expert Execution for MoE Prefill under Attention Imbalance
Jin Qin, Tiancheng Hu, Shiyan Wang +12
Mixture-of-experts (MoE) serving commonly deploys data and expert parallelism (DEP): attention replicas run distinct request batches while routed experts are sharded across an expe…
Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
Tiancheng Hu, Jin Qin, Zheng Wang +10
Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existing solutions operate at a coars…
Hummingbird: SLO-Oriented GPU Preemption at Microsecond-scale
Tiancheng Hu, Chenxi Wang, Ting Cao +9
Existing GPU-sharing techniques, including spatial and temporal sharing, aim to improve utilization but face challenges in simultaneously ensuring SLO adherence and maximizing effi…
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
Junhao Hu, Fangze Li, Mingtao Xu +9
Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference e…
RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning
Junhao Hu, Wenrui Huang, Weidong Wang +6
Large Language Models (LLMs) have demonstrated strong capabilities across various domains, with recent advancements in challenging reasoning tasks such as mathematics and programmi…
EPIC: Efficient Position-Independent Caching for Serving Large Language Models
Junhao Hu, Wenrui Huang, Weidong Wang +7
Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become mor…