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 …

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 …

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

With the increasing popularity of large deep learning model-serving workloads, there is a
pressing need to reduce the energy consumption of a model-serving cluster while …

Minimizing monetary cost and maximizing the goodput of inference serving systems are
increasingly important with the ever-increasing popularity of deep learning models. While it …

Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …

GPU technology has been improving at an expedited pace in terms of size and performance,
empowering HPC and AI/ML researchers to advance the scientific discovery process …

The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which
requires urgent intervention. In this work, we present a comprehensive analysis of the …

Large Language Models (LLMs) like GPT and LLaMA are revolutionizing the AI industry with
their sophisticated capabilities. Training these models requires vast GPU clusters and …

Modern GPU clusters support Deep Learning training (DLT) jobs in a distributed manner.
Job scheduling is the key to improve the training performance, resource utilization and …