It is important to scale out deep neural network (DNN) training for reducing model training
time. The high communication overhead is one of the major performance bottlenecks for …

It is important to scale out deep neural network (DNN) training for reducing model training
time. The high communication overhead is one of the major performance bottlenecks for …

Distributed training using multiple devices (eg, GPUs) has been widely adopted for learning
DNN models over large datasets. However, the performance of large-scale distributed …

As deep neural networks get more complex and input datasets get larger, it can take days or
even weeks to train a deep neural network to the desired accuracy. Therefore, enabling …

Most work in the deep learning systems community has focused on faster inference, but
arriving at a trained model requires lengthy experiments. Accelerating training lets …

Communication overhead poses an important obstacle to distributed DNN training and
draws increasing attention in recent years. Despite continuous efforts, prior solutions such …

Optimizing performance for Distributed Deep Neural Network (DDNN) training has recently
become increasingly compelling, as the DNN model gets complex and the training dataset …

Deep neural networks (DNNs) have gained tremendous attractions as compelling solutions
for applications such as image classification, object detection, speech recognition, and so …

The advanced fabrics like NVIDIA NVLink are enabling the deployment of dense Graphics
Processing Unit (GPU) systems such as DGX-2 and Summit. With the wide adoption of large …

As deep neural networks become more complex and input datasets grow larger, it can take
days or even weeks to train a deep neural network to the desired accuracy. Therefore …