With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Heterogeneous systems-on-chip (SoCs) integrate diverse cores with different performance
and energy tradeoffs. Scheduling applications with soft deadline constraints is highly …

With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …

The growth of deep learning training (DLT) jobs in modern GPU clusters calls for efficient
deep learning (DL) scheduler designs. Due to the extensive applications of DL technology …

The landscape of modern computers is undoubtedly heterogeneous, as all computing
platforms integrate multiple types of processing units and hardware accelerators. However …

In this work, we optimize the scheduling of Deep Learning (DL) training jobs from the
perspective of a Cloud Service Provider running a data center, which efficiently selects …

With the wide adoption of deep neural network (DNN) models for various applications,
enterprises, and cloud providers have built deep learning clusters and increasingly …