Model Parallel

Model parallelism addresses the challenge of training extremely large neural networks that exceed the memory capacity of a single machine by distributing the model across multiple devices. Current research focuses on optimizing communication efficiency between these devices, exploring techniques like data and model partitioning, compression of activations and gradients, and novel algorithms such as SWARM parallelism and MGRIT for handling long sequences. These advancements enable training of massive models for diverse applications, including large language models, recommender systems, and the solution of complex partial differential equations, significantly accelerating scientific discovery and industrial processes.