通过网内模型参数分发加速分布式模型的训练分析

Analysis of Accelerating Training of Distributed Models through Intra Network Model Parameter Distribution

阐述一种基于随机舍入的下行链路通信优化方案,其算法实现近似系数为O(log|V|),其中V为可编程交换机的数量。通过算法模拟的大规模实验结果表明,与最先进的解决方案相比,此方案可以减少14.5%~35.8%的下行链路通信开销。

This paper a downlink communication optimization scheme based on random rounding,with an algorithm implementation approximation coefficient of O(log|V|),where V is the number of programmable switches.The large-scale experimental results through algorithm simulation show that compared with state-of-the-art solutions,this scheme can reduce downlink communication overhead by 14.5%to 35.8%.