# MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms

arXiv: https://arxiv.org/abs/1811.11141 · HTML: https://mlsanity.com/paper/1811.11141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs](https://mlsanity.com/paper/2309.01172/) (2023)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [Evaluation and Optimization of Gradient Compression for Distributed Deep Learning](https://mlsanity.com/paper/2306.08881/) (2023)
- [FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression](https://mlsanity.com/paper/2410.12707/) (2024)
- [Accelerating Distributed K-FAC with Smart Parallelism of Computing and Communication Tasks](https://mlsanity.com/paper/2107.06533/) (2021)
- [Enhancing Neural Training via a Correlated Dynamics Model](https://mlsanity.com/paper/2312.13247/) (2023)
- [FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training](https://mlsanity.com/paper/2303.01778/) (2023)
- [DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining](https://mlsanity.com/paper/2302.12445/) (2023)
- [OSP: Boosting Distributed Model Training with 2-stage Synchronization](https://mlsanity.com/paper/2306.16926/) (2023)
- [FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training](https://mlsanity.com/paper/2510.00207/) (2025)
- [CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation](https://mlsanity.com/paper/2106.10796/) (2021)
- [SparDL: Distributed Deep Learning Training with Efficient Sparse Communication](https://mlsanity.com/paper/2304.00737/) (2023)

## Abstract (arXiv)

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks on computer clusters. With the increase of computational power, network communications have become one limiting factor on system scalability. In this paper, we observe that many deep neural networks have a large number of layers with only a small amount of data to be communicated. Based on the fact that merging some short communication tasks into a single one may reduce the overall communication time, we formulate an optimization problem to minimize the training iteration time. We develop an optimal solution named merged-gradient WFBP (MG-WFBP) and implement it in our open-source deep learning platform B-Caffe. Our experimental results on an 8-node GPU cluster with 10GbE interconnect and trace-based simulation results on a 64-node cluster both show that the MG-WFBP algorithm can achieve much better scaling efficiency than existing methods WFBP and SyncEASGD.