The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Production machine learning (ML) clusters commonly host a variety of distributed ML
workloads, eg, speech recognition, machine translation. While server sharing among jobs …

Nowadays large-scale distributed machine learning systems have been deployed to support
various analytics and intelligence services in IT firms. To train a large dataset and derive the …

Efficient resource scheduling is essential for maximal utilization of expensive deep learning
(DL) clusters. Existing cluster schedulers either are agnostic to machine learning (ML) …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

In distributed DNN training, parameter servers (PS) can become performance bottlenecks
due to PS stragglers, caused by imbalanced parameter distribution, bandwidth contention …

Nowadays, most leading IT companies host a variety of distributed machine learning (ML)
workloads in ML clusters to support AI-driven services, such as speech recognition, machine …