Many cluster management systems (CMSs) have been proposed to share a single cluster
with multiple distributed computing systems. However, none of the existing approaches can …

Many cluster management systems (CMSs) have been proposed to share a single cluster
with multiple distributed computing systems. However, none of the existing approaches can …

Many machine-learning applications rely on distributed machine learning (DML) systems to
train models from massive datasets using massive computing resources (eg, GPUs and …

We introduce Harmony, a new scheduling framework that executes multiple Parameter-
Server ML training jobs together to improve cluster resource utilization. Harmony …

This paper introduces RankMap, a platform-aware end-to-end framework for efficient
execution of a broad class of iterative learning algorithms for massive and dense datasets …

To support various types of applications submitted by multiple users, a large-scale cluster
composed of different types of computing platforms, such as supercomputers, grids, and …

The rise of BigData leads to demand for machine learning (ML) for training complex models
on a huge volume of input data. Thus, distributed ML is getting prevalent in both academia …

With the ever-increasing demand for computing power in deep learning, distributed training
techniques have proven to be effective in meeting these demands. However, current existing …

Machine learning is currently shifting from a centralized paradigm to decentralized ones
where machine learning models are trained collaboratively. In fully decentralized learning …

Machine learning (ML) models are increasingly trained in clusters with non-dedicated
workers possessing heterogeneous resources. In such scenarios, model training efficiency …