What is the role of parallel processing in training large language models?
Parallel processing plays a crucial role in training large language models. Training LLMs on large datasets can be computationally intensive, and parallel processing allows for the efficient use of computing resources to speed up training.
Parallel processing involves breaking up the training process into smaller, more manageable chunks that can be executed simultaneously across multiple processors or machines. This can significantly reduce the time required for training LLMs, especially when dealing with massive datasets.
Parallel processing can be implemented in several ways, including data parallelism, model parallelism, and pipeline parallelism.
* Data parallelism involves distributing the training data across multiple processors or machines, with each processor or machine processing a subset of the data.
* Model parallelism involves splitting the model across multiple processors or machines, with each processor or machine responsible for a subset of the model.
* Pipeline parallelism involves breaking up the training process into smaller stages, with each stage executed on a separate processor or machine.
