3 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
RedKnot-MLA: Multi-Head Offline-Online Reuse for DeepSeek-V4 Long-Context Serving
Yang Liu, Zhaokai Luo, Huayi Jin +10
Multi-head latent attention (MLA) exposes many logical query heads through one packed latent KV stream. This representation is memory efficient, but it removes the physical per-hea…
StrataCL: Fabric-Native Communication Library for Production Supernodes
Tiancheng Hu, Jin Qin, Yuzheng Wang +14
Modern distributed AI workloads run across hundreds of accelerators, making communication a major bottleneck. Existing communication libraries remain largely buffer-centric because…
GEB-Bench: Abstract Structures Told in Many Voices
Tong Zhang, Zhiyuan Shi, Yun Peng +1
Analogy allows models to apply relational knowledge from one situation to another. Benchmarks of equivalent representations test different ways of expressing the same underlying pr…
When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers
Tong Zhang, Junhao Hu, Yun Peng +1
When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm? We answer with four findings from controlled populations on group word pro…
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…