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) 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 …

Consumer electronics such as mobile phones, wearable devices, and vehicle electronics
use many intelligent applications such as voice commands, machine translation, and face …

With the rapid growth of cloud computing, efficient operational optimization and resource
scheduling of complex cloud business processes rely on real-time and accurate …

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN)
inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of …

While recent deep learning workload schedulers exhibit excellent performance, it is arduous
to deploy them in practice due to some substantial defects, including inflexible intrusive …

The development of cloud computing delivery models inspires the emergence of cloud-
native computing. Cloud-native computing, as the most influential development principle for …

A cloud data center provides various facilities such as storage, data accessibility, and
running many specific applications on cloud resources. The unpredictable demand for …

To achieve a high degree of resource utilization, production clusters need to co-schedule
diverse workloads–including both batch analytic jobs with short-lived tasks and long-running …

Data analysts predict that the GPU as a service (GPUaaS) market will grow to support 3D
models, animated video processing, gaming, and deep learning model training. The main …