Designing Hierarchical Multi-HCA Aware Allgather in MPI

Abstract

To accelerate the communication between nodes, supercomputers are now equipped with multiple network adapters per node, resulting in a ”multi-rail” network. The second and third-placed systems of the Top500 use two adapters per node; recently, the ThetaGPU system at Argonne National Laboratory (ANL) uses eight adapters per node. With such an availability of networking resources, it is a non-trivial task to utilize all of them. The Message Passing Interface (MPI) is a dominant model for high-performance computing clusters. Not all MPI collectives utilize all resources, and this becomes more apparent with advances in bandwidth and adapter count in a given cluster. In this work, we take up this task and propose hierarchical, multi-HCA aware Allgather designs; Allgather is a communication-intensive collective widely used in applications like matrix multiplication and other collectives. The proposed designs fully utilize all the available network adapters within a node and provides high overlap between inter-node and intra-node communication. At the micro-benchmark level, our new schemes achieve performance improvement for both single node and multiple node communication. We see inter-node improvements up to 62% and 61% better than HPC-X and MVAPICH2-X for 1024 processes. The design for inter-node communication also boosts the performance of Ring Allreduce by 56% and 44% compared to HPC-X and MVAPICH2-X. At the application level, the enhanced Allgather shows 1.98x and 1.42x improvement in a matrix-vector multiplication kernel when compared to HPC-X and MVAPICH2-X, and Allreduce performs up to 7.83% better in deep learning training against MVAPICH2-X.