Recent years have witnessed phenomenal growth in the computational capabilities and
applications of GPUs. However, this trend has also led to a dramatic increase in their power …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

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 …

Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers
complete coverage of modern distributed computing technology including clusters, the grid …

Since sequential information plays an important role in modeling user behaviors, various
sequential recommendation methods have been proposed. Methods based on Markov …

The increasing computing requirements for GPUs (Graphics Processing Units) have
favoured the design and marketing of commodity devices that nowadays can also be used to …

Containers are widely used for resource management in datacenters. A common practice to
support deep learning (DL) training in container clouds is to statically bind GPUs to …

Abstract The GPU Virtualization Service (gVirtuS) presented in this work tries to fill the gap
between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for …

GPUs have become ubiquitous in the cloud due to the dramatic performance gains they
enable in domains such as machine learning and computer vision. However, offloading …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …