Pipeline Parallelism

Pipeline parallelism is a technique for accelerating the training and inference of large deep learning models by dividing the model's computation across multiple devices in a pipelined fashion. Current research focuses on optimizing pipeline parallelism for various model architectures, including large language models (LLMs) and diffusion transformers, addressing challenges like memory efficiency, bubble minimization (idle time in the pipeline), and efficient communication strategies across heterogeneous hardware (e.g., GPUs, CPUs, edge devices). These advancements are crucial for enabling the training and deployment of increasingly large models, impacting both the scalability of scientific research and the practical application of AI in resource-constrained environments.