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 …

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 …

Modern GPU clusters inherently exhibit heterogeneity, encompassing various aspects such
as computation and communication. This heterogeneity poses a significant challenge for 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 …

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 …

Distributed machine learning (DML) has shown great promise in accelerating model training
on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning …

We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables
proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the …

Joint consideration of scheduling and adaptive parallelism offers great opportunities for
improving the training efficiency of large models on heterogeneous GPU clusters. However …

In this paper we build a case for providing job completion time predictions to cloud users,
similar to the delivery date of a package or arrival time of a booked ride. Our analysis reveals …