一句话：提出 NetPack：面向统计式网内聚合集群，周期性批处理并启发式+动态规划放置分布式训练作业，最大化计算与网络资源利用率。
分布式训练中的梯度聚合常被卸载到可编程交换机（网内聚合 INA）。交换机内存可通过同步或统计复用方式分配给作业；统计式 INA 在内存利用率、控制面简单性和管理安全性上有优势，但作业放置存在跨层资源效率问题。为此，论文提出作业放置系统 NetPack：它周期性批处理作业并放入集群；放置每个作业时，先运行稳态估计算法获取集群可用资源，再根据可用 GPU 与带宽对每台服务器启发式估值，最后用动态规划高效搜索对该作业价值最高的服务器。原型与生产 trace 实验表明，NetPack 的平均作业完成时间比先前作业放置方法优 45%。
In-Network Aggregation (INA) offloads the gradient aggregation in distributed training (DT) onto programmable switches, where the switch memory could be allocated to jobs in either synchronous or statistical multiplexing mode. Statistical INA has advantages in switch memory utilization, control-plane simplicity, and management safety, but it faces the problem of cross-layer resource efficiency in job placement. This paper presents a job placement system NetPack for clusters with statistical INA, which aims to maximize the utilization of both computation and network resources. NetPack periodically batches and places jobs into the cluster. When placing a job, NetPack runs a steady state estimation algorithm to acquire the available resources in the cluster, heuristically values each server according to its available resources (GPU and bandwidth), and runs a dynamic programming algorithm to efficiently search for servers with the highest value for the job. Our prototype of NetPack and the experiments demonstrate that NetPack outperforms prior job placement methods by 45% in terms of average job completion time on production traces.
@inproceedings{ZhaoXLTWW24,
title = {Training Job Placement in Clusters with Statistical In-Network Aggregation},
author = {Bohan Zhao and Wei Xu and Shuo Liu and Yang Tian and Qiaoling Wang and Wenfei Wu},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3617232.3624863}
}