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 …

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 …

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) …

Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-
optimizing inter-dependent factors both at the per-job level and at the cluster-wide level …

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 …

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 …

The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to
elastic resource-adaptive jobs. Although some recent schedulers address one aspect or …

Deep learning (DL) is becoming increasingly popular in many domains, including computer
vision, speech recognition, self-driving automobiles, etc. GPU can train DL models efficiently …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …