12 citations · 12 across the 8 of their papers we have counts for
4 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…
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…
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
Wenxiang Lin, Juntao Huang, Luhan Zhang +5
Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective for 4-bit activations and 8-b…
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Wenxiang Lin, Xinglin Pan, Ruibo Fan +2
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication…