Performant all-to-all collective operations in MPI are critical to fast Fourier transforms, transposition, and machine learning applications. There are many existing implementations for all-to-all exchanges on emerging systems, with the achieved performance dependent on many factors, including message size, process count, architecture, and parallel system partition. This paper presents novel all-to-all algorithms for emerging many-core systems. Further, the paper presents a performance analysis against existing algorithms and system MPI, with novel algorithms achieving up to 3x speedup over system MPI at 32 nodes of state-of-the-art Sapphire Rapids systems.

翻译：MPI中高性能的全对全集合操作对于快速傅里叶变换、矩阵转置和机器学习应用至关重要。针对新兴系统已存在多种全对全交换的实现方案，其达到的性能取决于诸多因素，包括消息大小、进程数量、体系结构以及并行系统分区。本文提出了面向新兴众核系统的新型全对全算法。此外，本文还对现有算法与系统MPI进行了性能分析，结果表明，在32个节点的先进Sapphire Rapids系统上，新型算法相比系统MPI可实现高达3倍的加速。