Multi Hop All Reduce

Multi-hop all-reduce is a crucial communication primitive for synchronizing model parameters in distributed deep learning, aiming to optimize the speed and efficiency of training large models across multiple nodes. Current research focuses on improving all-reduce performance through techniques like in-network aggregation, optimized routing strategies (e.g., short-cutting rings), and fine-grained pipelining to minimize communication latency and maximize bandwidth utilization. These advancements are vital for scaling deep learning models, particularly in large-scale applications like Mixture-of-Experts (MoE) models and are impacting the development of efficient distributed training frameworks.