The large communication overhead has imposed a bottleneck on the performance of
distributed Stochastic Gradient Descent (SGD) for training deep neural networks. Previous …

Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training
large-scale deep learning models, while the communication overhead among workers …

Communication overhead is one of the key challenges that hinders the scalability of
distributed optimization algorithms to train large neural networks. In recent years, there has …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in
large-scale deep learning applications, while it is known that the communication bottleneck …

Modern distributed training of machine learning models often suffers from high
communication overhead for synchronizing stochastic gradients and model parameters. In …

Large-scale distributed optimization is of great importance in various applications. For data-
parallel based distributed learning, the inter-node gradient communication often becomes …

Communication bottleneck has been identified as a significant issue in distributed
optimization of large-scale learning models. Recently, several approaches to mitigate this …

Powerful computer clusters are used nowadays to train complex deep neural networks
(DNN) on large datasets. Distributed training workloads increasingly become …

Communication overhead is one of the major obstacles to train large deep learning models
at scale. Gradient sparsification is a promising technique to reduce the communication …