Accelerating communication with multi‐HCA aware collectives in MPI

• Published in: Concurrency and computation 2024-01, Vol.36 (1), p.n/a
• Main Authors: Tran, Tu, Ramesh, Bharath, Michalowicz, Benjamin, Abduljabbar, Mustafa, Subramoni, Hari, Shafi, Aamir, Panda, Dhabaleswar K.
• Format: Article
• Language: English
• Subjects: Adapters; Allgather; Allreduce; collectives; Communication; HCA‐aware; Message passing; MPI; Network adapters; network‐aware; Nodes
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7879

Summary To accelerate the communication between nodes, supercomputers are now equipped with multiple network adapters per node, also referred to as HCAs (Host Channel Adapters), resulting in a “multi‐rail”/“multi‐HCA” network. For example, the ThetaGPU system at Argonne National Laboratory (ANL) has eight adapters per node; with this many networking resources available, utilizing all of them becomes non‐trivial. 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 provide a thorough performance analysis of existing multirail solutions and their implications on collectives and present the necessity for further enhancement. Specifically, we propose novel designs for hierarchical, multi‐HCA‐aware Allgather. The proposed designs fully utilize all the available network adapters within a node and provide high overlap between inter‐node and intra‐node communication. At the micro‐benchmark level, we see large inter‐node improvements up to 62% and 61% better than HPC‐X and MVAPICH2‐X for 1024 processes. Because Allgather is used in Ring‐Allreduce, our designs also improve its performance by 56% and 44% compared to HPC‐X and MVAPICH2‐X, respectively. At the application level, our enhanced Allgather shows 1.98×$$ 1.98\times $$ and 1.42×$$ 1.42\times $$ 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.