12 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.DC2025
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
Zhenheng Tang, Zichen Tang, Junlin Huang +7
The growth of large language models (LLMs) increases challenges of accelerating distributed training across multiple GPUs in different data centers. Moreover, concerns about data p…
cs.LG2025★ 12 cited
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
Xinglin Pan, Wenxiang Lin, Lin Zhang +5
Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces fou…
cs.CL2024
LPZero: Language Model Zero-cost Proxy Search from Zero
Peijie Dong, Lujun Li, Xiang Liu +4
In spite of the outstanding performance, Neural Architecture Search (NAS) is criticized for massive computation. Recently, Zero-shot NAS has emerged as a promising approach by expl…