What is Pipeline Parallelism primarily used for?

Pipeline Parallelism is primarily used for processing data in parallel through multiple stages of computation, which allows different parts of a model to be trained simultaneously. This technique enhances computational efficiency and speeds up the training process by dividing the model into segments, each responsible for a specific part of the computation.

In the context of large models, Pipeline Parallelism enables the training of deep learning models that may be too large to fit into the memory of a single GPU or machine. By splitting the model into smaller chunks and processing these chunks in tandem across available compute resources, it effectively utilizes the hardware, thus optimizing performance.

While the other choices may pertain to aspects of model training and data management, they do not specifically describe the function or application of Pipeline Parallelism. For instance, reducing unnecessary weight updates pertains more to techniques that streamline learning or optimizations in training algorithms, and managing data storage effectively or integrating with cloud services are broader aspects that do not specifically leverage the core principles of Pipeline Parallelism.