Improvement of Distributed Machine Learning Communication Performance Based on GPU Server
GPU virtualization technology promotes the evolution of cloud server industry,distributed machine learning completes optimization training locally,aggregates the result data through the communication link,and starts the next round of training iteration.In this paper,it is clarified that communication performance is the key to restricting the computing power through the modular division of distributed machine learning functions.Firstly,the communication performance of the hierarchical synchronization algorithm and the planar synchronization algorithm is compared,and then the global synchronization time GST is used as the characterization parameter to compare the advantages and disadvantages,layout difficulty and application occasions of different communication algorithms.
万方数据
维普
