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 …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …

In this paper, we offer an original framework to study Artificial Intelligence (AI). The
perspective we propose is based on the idea that AI is a system technology, and that a …

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 …

Organizations often build separate training and inference clusters for deep learning, and use
separate schedulers to manage them. This leads to problems for both: inference clusters …

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 …

Deep learning (DL) jobs use multi-dimensional parallelism, ie, combining data, model, and
pipeline parallelism, to use large GPU clusters efficiently. Long-running jobs may …

Deep learning training on cloud platforms usually follows the tradition of the separation of
storage and computing. The training executes on a compute cluster equipped with …