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.

翻译：摘要：大型深度学习模型在各类应用中展现出卓越潜力，但因其包含数千亿参数的庞大参数量，训练过程极具挑战性。常规分布式训练方法（如数据并行、张量并行和流水线并行）在训练全程需要大量数据通信，导致物理距离较远的分布式系统中部分机器出现长时间等待。针对该问题，我们提出一种名为Hulk的新型解决方案，该方案利用改进的图神经网络优化分布式计算系统。Hulk不仅能优化跨国家甚至同城市不同区域间的数据通信效率，还能实现模型并行部署的最优分布。例如，它可将特定层部署于某区域机器上，或将模型特定参数传递至指定位置的设备。实验表明，采用Hulk可使分布式系统上大型深度学习模型的时间效率提升超过20%。我们的开源无标签数据集已公开（https://github.com/DLYuanGod/Hulk）。