Deep learning training (DLT), eg, large language model (LLM) training, has become one of
the most important services in multitenant cloud computing. By deeply studying in …

This paper argues that congestion control protocols in machine learning datacenters sit at a
sweet spot between centralized and distributed flow scheduling solutions. We present …

As machine learning models scale in size and complexity, their computational requirements
become a significant barrier. Mixture-of-Experts (MoE) models alleviate this issue by …

Distributed deep learning (DL) training constitutes a significant portion of workloads in
modern data centers that are equipped with high computational capacities, such as GPU …

We present MLTCP, a technique to augment today's congestion control algorithms to
accelerate DNN training jobs in shared GPU clusters. MLTCP enables the communication …

The past few years have witnessed the flourishing of large-scale deep neural network
models with ever-growing parameter numbers. Training such large-scale models typically …