Independent Researcher, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 15(02), 840-849
Received on 15 June 2022; revised on 16 August 2022; accepted on 28 August 2022
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.
Deep Learning; Distributed Computing; Cloud Computing; GPU Clusters; Neural Networks; Parallel Processing; Model Training; Data Parallelism; Resource Management; Communication Protocols
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Rahul Modak. Distributed deep learning on cloud GPU clusters. World Journal of Advanced Research and Reviews, 2022, 15(2), 840-849. Article DOI: https://doi.org/10.30574/wjarr.2022.15.2.0723