This paper surveys the various algorithms and techniques used to distribute training and presents the current state of the art for a modern distributed training framework.

“Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training …

To motivate this section, we first review the mechanism of synchronous distributed training: at each step, each node will first compute gradients locally, then they wait for the collective operation to transmit …

In this work, we aim to systematically explore the communica-tion characteristics of distributed training. Our analysis focuses on the individual job scenarios, paying attention to fine-grained within-job features.

Outline reasons to train models using more than one GPU. Understand different GPU collective communication primitives and their role in each parallel technique. Understand different …

We propose a new data parallel based distributed training framework, named Co-Adaptive Data Parallelism (C-ADP), for a geo-distributed cluster with heterogeneous computing and communication …

Outline Distributed Simulation for training: What is it? Why do it? What is Canada doing?