Distributed deep learning (DDL) has recently been proposed to accelerate the training
process of a deep learning model. The core idea is to have multiple workers collaboratively …

Distributed deep learning frameworks train large deep leaning workload with multiple
training jobs on shared distributed GPU servers. There are new challenges when …

In recent work, the decentralized algorithm has received more attention. In the centralized
network, the worker nodes need to communicate with the central nodes, which results in the …

Deep Neural Networks (DNNs) find widespread applications across various domains,
including computer vision, recommendation systems, and natural language processing …

Emerging distributed applications, such as microservices, machine learning, big data
analysis, consist of both compute and network tasks. DAG-based abstraction primarily …

ML workloads are becoming increasingly popular in the cloud. Good cloud training
performance is contingent on efficient parameter exchange among VMs. We find that …

The recent success of Deep Learning (DL) in a board range of AI services has led to a
surging amount of DL workloads in production clusters. To support DL jobs at scale, the …

Mobile edge computing enables the execution of compute-intensive applications, eg deep
learning applications, on the end devices with limited computation resources. However, the …

A network device configured to perform scalable, in-network computations is described. The
network device is configured to process pull requests and/or push requests from a plurality …

In centralized distributed machine learning systems, communication overhead between
servers and computing nodes has always been an important issue affecting the training …