Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as
the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However …

Deep learning (DL) applications are becoming one of the most important applications for
HPC and cloud systems. The massive datasets and deep neural networks (DNN) used by …

In the age of big data, deep learning has emerged as a powerful tool to extract insight and
exploit its value, both in industry and scientific applications. One common pattern emerging …

The amount of available resources of a cloud is constantly changing. However, the current
distributed DNN framework does not allow dynamic scaling of a training cluster. Therefore, a …

Deep neural networks (DNNs) are increasingly popular owing to their ability to solve
complex problems such as image recognition, autonomous driving, and natural language …

Distributed Deep Neural Network (DDNN) training on cloud spot instances is increasingly
compelling as it can significantly save the user budget. To handle unexpected instance …

Finding the best VM configuration is key to achieve lower cost and higher throughput, two
primary concerns in cloud-based distributed neural network (NN) training today. Optimal VM …

This paper aims to answer the question: Can deep learning models be cost-efficiently
trained on a global market of spot VMs spanning different data centers and cloud providers …

Accelerator virtualization offers several advantages in the context of cloud-edge computing.
Relatively weak user devices can enhance performance when running workloads by …

Training large, complex machine learning models such as deep neural networks with big
data requires powerful computing clusters, which are costly to acquire, use and maintain. As …