Recently, pipeline parallelism for large-scale Deep Neural Network (DNN) training has been
developed, which partitions the DNN model across multiple devices (eg, GPUs) and …

Because training a deep neural network (DNN) takes arduous amounts of time and
computation, often researchers expedite the training process via distributed parallel training …

Exorbitant resources (computing and memory) are required to train a deep neural network
(DNN). Often researchers deploy an approach that uses distributed parallel training to …

PipeDream is a Deep Neural Network (DNN) training system for GPUs that parallelizes
computation by pipelining execution across multiple machines. Its pipeline parallel …

With the increase of both data and parameter volume, it has become a big challenge to
efficiently train large-scale DNN models on distributed platforms. Ordinary parallelism …

As the deep learning model grows larger, training model with a single computational
resource becomes impractical. To solve this, hybrid parallelism, which combines data and …

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine
learning algorithm increases. To satisfy the requirement of computation and memory of DNN …

Deep Neural Network (DNN) models have continuously been growing in size in order to
improve the accuracy and quality of the models. Moreover, for training of large DNN models …

The training process of Deep Neural Network (DNN) is compute-intensive, often taking days
to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a …

In recent years, Deep Neural Networks (DNNs) have emerged as a widely adopted
approach in many application domains. Training DNN models is also becoming a significant …