Distributed Implementation of Computing Language Model in Cloud Computing Network

Abstract

In this paper, the distributed implementation of Computing Language Model (CLM) in cloud computing network is studied and discussed. With the advent of the era of big data, the application of CLM in natural language processing (NLP), machine translation and other fields is increasingly extensive, and the demand for computing resources is also increasing. As an effective way to manage computing resources, distributed computing can make full use of resources in cloud computing environment and realize efficient execution of computing tasks. In this paper, the application of distributed computing methods such as data parallelism, model parallelism and hybrid parallelism in cloud computing environment is studied and analyzed, and the advantages and disadvantages of different methods in CLM implementation are discussed. The experimental results show that the hybrid parallel method can effectively combine the advantages of data parallelism and model parallelism, and improve the training efficiency and performance of CLM. This study provides important theoretical guidance and technical support for the distributed implementation of CLM in cloud computing networks, and is of great significance for further promoting the development of big data and AI technology.