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 …

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 …

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 …

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 …

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 …

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

Fueled by advances in distributed deep learning (DDL), recent years have witnessed a
rapidly growing demand for resource-intensive distributed/parallel computing to process …

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

With the rapid proliferation of Machine Learning (ML) and Deep learning (DL) applications
running on modern platforms, it is crucial to satisfy application performance requirements …