# Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems

## TL;DR

Hulk employs a graph neural network to optimize distributed training of large deep learning models, significantly reducing communication overhead and improving training efficiency across geographically distributed systems.

## Contribution

The paper introduces Hulk, a novel graph neural network-based approach for optimizing distributed deep learning training across regions, addressing communication bottlenecks.

## Key findings

- Over 20% improvement in training time efficiency
- Effective optimization of data communication across regions
- Enhanced deployment strategies for distributed models

## Abstract

Large deep learning models have shown great potential for delivering exceptional results in various applications. However, the training process can be incredibly challenging due to the models' vast parameter sizes, often consisting of hundreds of billions of parameters. Common distributed training methods, such as data parallelism, tensor parallelism, and pipeline parallelism, demand significant data communication throughout the process, leading to prolonged wait times for some machines in physically distant distributed systems. To address this issue, we propose a novel solution called Hulk, which utilizes a modified graph neural network to optimize distributed computing systems. Hulk not only optimizes data communication efficiency between different countries or even different regions within the same city, but also provides optimal distributed deployment of models in parallel. For example, it can place certain layers on a machine in a specific region or pass specific parameters of a model to a machine in a particular location. By using Hulk in experiments, we were able to improve the time efficiency of training large deep learning models on distributed systems by more than 20\%. Our open source collection of unlabeled data:https://github.com/DLYuanGod/Hulk.