Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs

Abstract

Data-parallel distributed deep learning requires an AllReduce operation between all GPUs with message sizes in the order of hundreds of megabytes. The popular implementation of AllReduce for deep learning is the Ring-AllReduce, but this method suffers from latency when using thousands of GPUs. There have been efforts to reduce this latency by combining the ring with more latency-optimal hierarchical methods. In the present work, we consider these hierarchical communication methods as a general hierarchical Ring-AllReduce with a pure Ring-AllReduce on one end and Rabenseifner's algorithm on the other end of the spectrum. We exhaustively test the various combinations of hierarchical partitioning of processes on the ABCI system in Japan on up to 2048 GPUs. We develop a performance model for this generalized hierarchical Ring-AllReduce and show the lower-bound of the effective bandwidth achievable for the hierarchical NCCL communication on thousands of GPUs. Our measurements agree well with our performance model. We also find that the optimal large-scale process hierarchy contains the optimal small-scale process hierarchy so the search space for the optimal communication will be reduced.