Pipeline Parallelism
Pipeline parallelism is a parallel computing technique where a computational task is divided into sequential stages, and data flows through these stages like an assembly line, with multiple data items being processed concurrently in different stages. It is commonly used in deep learning and high-performance computing to accelerate training and inference by overlapping computation across layers or operations. This approach improves hardware utilization, especially on multi-GPU or distributed systems, by reducing idle time between stages.
Developers should learn pipeline parallelism when working with large neural networks or complex data processing pipelines that do not fit into a single GPU's memory or require faster throughput. It is essential for scaling deep learning models like transformers (e.g., GPT, BERT) across multiple devices, enabling efficient training of billion-parameter models. Use cases include distributed training in frameworks like PyTorch and TensorFlow, real-time inference systems, and optimizing latency in streaming applications.