Collective Communication in Distributed Systems with PyTorch

PyTorch's distributed collective communication feature enables efficient tensor sharing across multiple GPUs, crucial for tasks like training neural networks. This is achieved through six key collection strategies: reduce, all_reduce, scatter, gather, all_gather, and broadcast. Each strategy serves a unique purpose, such as reducing tensors to a single GPU, distributing portions of a tensor across multiple GPUs, or collecting tensors from all GPUs to a single or all GPUs. The implementation involves setting up a distributed environment using Python code, initializing processes, and applying these strategies to manage tensor operations across devices. By leveraging these strategies, users can enhance the scalability and performance of their neural network training processes, fully utilizing the capabilities of distributed computing.