PARING: Joint Task Placement and Routing for Distributed Training With In-Network Aggregation
Yuhang Qiu, Gongming Zhao, Hongli Xu, He Huang, Chunming Qiao
Abstract
With the increase in both the model size and dataset size of distributed training (DT) tasks, communication between the workers and parameter servers (PSs) in a cluster has become a bottleneck. In-network aggregation (INA) enabled by programmable switches has been proposed as a promising solution to alleviate the communication bottleneck. However, existing works focused on in-network aggregation implementation based on simple DT placement and fixed routing policies, which may lead to a large communication overhead and inefficient use of resources (e.g., storage, computing power and bandwidth). In this paper, we propose PARING, the first-of-its-kind INA approach that jointly optimizes DT task placement and routing in order to reduce traffic volume and minimize communication time. We formulate the problem as a nonlinear multi-objective mixed-integer programming problem, and prove its NP-Hardness. Based on the concept of Steiner trees, an algorithm with bounded approximation factors is proposed for this problem. Large-scale simulations show that our algorithm can reduce communication time by up to 81.0% and traffic volume by up to 19.1% compared to the state-of-the-art algorithms.