Year: 2022
Type: article
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.
Author: Rahul Modak
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411947674