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 …

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 …

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

Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are
critical for maximizing system performance and optimizing resource utilization. However …

With the proliferation of deep learning, there exists a strong need to efficiently operate GPU
clusters for deep learning production in giant AI companies, as well as for research and …

Public cloud GPU clusters are becoming emerging platforms for training distributed deep
learning jobs. Under this training paradigm, the job scheduler is a crucial component to …

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 …

Abstract In General-Purpose computing on Graphics Processing Unit (GPGPU), the use of
CPUs is combined with that of GPUs. CPUs are used for sequential code, while GPUs are …

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 …