Model Parallelism

Model parallelism is a technique for distributing the training and inference of large machine learning models across multiple devices to overcome memory limitations and accelerate computation. Current research focuses on optimizing model parallelism for transformer-based architectures and Mixture-of-Experts models, employing strategies like pipeline parallelism, tensor parallelism, and various scheduling algorithms to minimize communication overhead and improve efficiency. This approach is crucial for training and deploying increasingly complex models like large language models and is impacting diverse fields by enabling the use of larger, more powerful models in resource-constrained environments such as edge devices and decentralized clusters.