Compute heterogeneity is increasingly gaining prominence in modern datacenters due to
the addition of accelerators like GPUs and FPGAs. We observe that datacenter schedulers …

Modern data centers are increasingly being provisioned with compute accelerators such as
GPUs, FPGAs and ASIC's to catch up with the workload performance demands and reduce …

Disaggregating expensive and power-hungry GPUs enable a cost-efficient and adaptive
ecosystem for cloud deployment, particularly for emerging markets, wherein AI applications …

Modern cluster management systems, such as Kubernetes, support heterogeneous
workloads and resources. However, existing resource schedulers in these systems do not …

While deep neural network (DNN) models are often trained on GPUs, many companies and
research institutes build GPU clusters that are shared by different groups. On such GPU …

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 …

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 …

In the current era of data explosion accelerators such as GPUs facilitate data-driven
applications with requisite compute boost. Availability of GPUs in Public Cloud offerings has …

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 …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …