6 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
Daegun Yoon, Sangyoon Oh
Communication overhead is a major obstacle to scaling distributed training systems. Gradient sparsification is a potential optimization approach to reduce the communication volume…
cs.LG2023★ 6 cited
DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification
Daegun Yoon, Sangyoon Oh
Gradient sparsification is a widely adopted solution for reducing the excessive communication traffic in distributed deep learning. However, most existing gradient sparsifiers have…