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 …

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 increasing demand for learning from massive datasets is restructuring our economy.
Effective learning, however, involves nontrivial computing resources. Most businesses utilize …

While recent deep learning workload schedulers exhibit excellent performance, it is arduous
to deploy them in practice due to some substantial defects, including inflexible intrusive …

This paper proposes ElasticFlow, an elastic serverless training platform for distributed deep
learning. ElasticFlow provides a serverless interface with two distinct features:(i) users …

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

With the rapid proliferation of deep learning (DL) jobs running on heterogeneous GPUs,
scheduling DL jobs to meet various scheduling requirements, such as meeting deadlines …

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 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 …