9 papers · 1 filter
Accelerating MoE Training through Expert Placement and Replica Coordination
Zhiqiang Tan, Maoxin Wang, Wenxiang Lin +4
Distributed training of sparsely activated mixture-of-experts (MoE) models is often bottlenecked by workload imbalance and all-to-all (A2A) communication induced by dynamic token r…
Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training
Zhiqiang Tan, Maoxin Wang, Sijie Wang +4
It is well established that the reasoning capabilities of large language models (LLMs) can be improved by applying reinforcement learning (RL) in a post-training stage. In a standa…
Zellige: Moldable Sequence Placement for Mixed Image-Video DiT Training
Guangyu Xiang, Xueze Kang, Minwei Zhao +4
High-quality video generation requires training Diffusion Transformers (DiTs) jointly on image and video data, posing a mixed-length sequence training problem across GPUs. Existing…
Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration
Xueze Kang, Guangyu Xiang, Suyi Li +4
Diffusion models are increasingly deployed as production visual-generation services, where serving high-resolution image and long video generation is often limited by GPU memory. P…
KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding
Guangyu Xiang, Xueze Kang, Lin Zhang +4
LLM serving is increasingly dominated by long and dynamic decode workloads from agents, reasoning models, and extended conversations. When bursty long-context demand exceeds deploy…
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
Xinglin Pan, Shaohuai Shi, Wenxiang Lin +4
The mixture-of-experts (MoE) architecture scales model size with sublinear computational increase but suffers from memory-intensive inference due to KV caches and sparse expert act…
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
Zhexiang Zhang, Ye Wang, Yumiao Zhao +9
Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most…
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
Wenxiang Lin, Xinglin Pan, Lin Zhang +3
The sparsely activated mixture-of-experts (MoE) transformer has become a common architecture for large language models (LLMs) due to its sparsity, which requires fewer computationa…
Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
Yizhou Luo, Qiang Wang, Shaohuai Shi +4
Deep learning (DL) has demonstrated significant success across diverse fields, leading to the construction of dedicated GPU accelerators within GPU clusters for high-quality traini…