🤖 AI Summary
This work addresses the high intra-node multi-GPU communication overhead in MPI-based high-performance computing by seamlessly integrating CUDA Graphs into the UCX communication framework for the first time. By co-scheduling multiple communication pathways—including NVLink and PCIe—the proposed approach enables unified optimization of point-to-point GPU data transfers. This strategy overcomes the bandwidth limitations inherent in conventional single-path methods such as UCT::CUDA-IPC. Experimental results on a four-GPU node demonstrate a peak bandwidth improvement of up to 2.95× for 512 MB messages, significantly enhancing multi-GPU communication efficiency.
📝 Abstract
Effective intra-node GPU communication is essential for optimizing performance in MPI-based HPC applications, especially when leveraging multiple communication paths. In this study, we propose a novel approach that integrates CUDA Graphs into the UCX framework to enhance intra-node multi-path point-to-point GPU communication. By concurrently leveraging multiple paths, including NVLink and PCIe through the host, and optimizing communication workflows using CUDA Graph, we achieve significant reductions in communication overhead and improve execution efficiency. To the best of our knowledge, our proposed approach is the first to seamlessly integrate CUDA Graphs into UCX. Through extensive experiments on a four-GPU node, our proposed CUDA Graph-based multi-path communication approach achieves up to a 2.95x bandwidth improvement, compared to the single-path UCX (UCT::CUDA-IPC), in GPU-to-GPU OMB bandwidth test when utilizing the host path and two other GPU paths, at message sizes up to 512MB.