Datacenters are witnessing a rapid surge in the adoption of serverless functions for
microservices-based applications. A vast majority of these microservices typically span less …

DNN inferences are widely emerging as a service and must run in sub-second latency,
which need GPU hardware to achieve parallel accelerating. Prior works to improve the …

Modern Deep Learning Training (DLT) schedulers in GPU datacenters are designed to be
very sophisticated with many configurations. These configurations need to be adjusted …

In this work, we address the problem of running HPC workloads efficiently on Kubernetes
clusters. To do so, we compare the Kubernetes' default scheduler with KubeFlux, a …

Because training a deep neural network (DNN) takes arduous amounts of time and
computation, often researchers expedite the training process via distributed parallel training …

Datacenters are evolving to host heterogeneous workloads on shared clusters to reduce the
operational cost and achieve higher resource utilization. However, it is challenging to …

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 …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …

Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple
tenants concurrently. Each tenant may have varied and dynamic resource demands …