Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based supercomputers are emerging to be the standard, the use of graph partitioning becomes even more important as applications are rapidly moving to these architectures. However, there is no distributed-memory-parallel, multi-GPU graph partitioner available for applications. We developed a spectral graph partitioner, Sphynx, using the portable, accelerator-friendly stack of the Trilinos framework. In Sphynx, we allow using different preconditioners and exploit their unique advantages. We use Sphynx to systematically evaluate the various algorithmic choices in spectral partitioning with a focus on the GPU performance. We perform those evaluations on two distinct classes of graphs: regular (such as meshes, matrices from finite element methods) and irregular (such as social networks and web graphs), and show that different settings and preconditioners are needed for these graph classes. The experimental results on the Summit supercomputer show that Sphynx is the fastest alternative on irregular graphs in an application-friendly setting and obtains a partitioning quality close to ParMETIS on regular graphs. When compared to nvGRAPH on a single GPU, Sphynx is faster and obtains better balance and better quality partitions. Sphynx provides a good and robust partitioning method across a wide range of graphs for applications looking for a GPU-based partitioner.

图形分区一直是在多个处理器之间划分工作以最小化通信成本并平衡工作量的重要工具。尽管基于加速器的超级计算机已成为标准，但是随着应用程序迅速向这些体系结构迁移，使用图形分区变得更加重要。但是，没有可用于应用程序的分布式内存并行，多GP​​U图形分区程序。我们使用Trilinos框架的便携式，加速器友好的堆栈开发了光谱图分区器Sphynx。在Sphynx中，我们允许使用不同的预处理器并利用它们的独特优势。我们使用Sphynx来系统地评估频谱划分中的各种算法选择，重点是GPU性能。规则（例如网格，有限元方法的矩阵）和不规则（例如社交网络和网络图），并表明这些图类需要不同的设置和预处理器。在Summit超级计算机上的实验结果表明，在易于使用的环境中，Sphynx是不规则图上最快的替代方案，并且在正则图上可获得接近ParMETIS的分区质量。与单个GPU上的nvGRAPH相比，Sphynx更快，并获得更好的平衡和更好的分区质量。Sphynx为正在寻找基于GPU的分区程序的应用程序提供了一种良好而强大的分区方法，适用于各种图形。