Distributed deep learning on cloud GPU clusters
Independent Researcher, USA.
World Journal of Advanced Research and Reviews, 2022, 15(02), 840-849
Publication history:
Received on 15 June 2022; revised on 16 August 2022; accepted on 28 August 2022
Abstract:
Deep learning has revolutionized various domains like computer vision, natural language processing, and speech recognition. However, training large-scale deep neural networks requires significant computational resources. This paper explores distributed deep learning approaches leveraging cloud GPU clusters to accelerate training and enable processing of massive datasets. We provide a comprehensive overview of distributed deep learning architectures, optimization algorithms, communication protocols, and resource management techniques for cloud environments. Experimental results on image classification and language modeling tasks demonstrate the scalability and performance benefits of distributed training on cloud GPU clusters. We also discuss key challenges and future research directions in this rapidly evolving field.
Keywords:
Deep Learning; Distributed Computing; Cloud Computing; GPU Clusters; Neural Networks; Parallel Processing; Model Training; Data Parallelism; Resource Management; Communication Protocols
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0