MPI-powered gradient synchronization in PyTorch distributed training

This article delves into gradient synchronization in distributed training using PyTorch with a focus on MPI (Message Passing Interface), detailing the bottlenecks of conventional training methods and offering strategies to improve efficiency. Key techniques discussed include data, tensor, pipeline, and sharded data parallelism, along with the advantages of GPU-aware MPI to minimize overhead during data transfer. The piece also outlines the workflow for distributed training and how to set up PyTorch to utilize MPI effectively.