Because of the importance of Delaunay Triangulation in science and engineering, researchers have devoted extensive attention to parallelizing this fundamental algorithm. However, generating unstructured meshes for extremely large point sets remains a barrier for scientists working with large scale or high resolution datasets. In our previous paper, we introduced a novel algorithm – Triangulation of Independent Partitions in Parallel (TIPP) which divides the domain into many independent partitions that can be triangulated in parallel. However, using only a single master process introduced a performance bottleneck and inhibited scalability. In this paper, we refine our description of the original TIPP algorithm, and also extend TIPP to employ multiple master processes, distributing computational load across several machines. This new design improves both performance and scalability, and can produce 20 billion triangles using only 10 commodity nodes in under 30 minutes.

由于Delaunay三角剖分在科学和工程学中的重要性，研究人员已将注意力集中在并行化此基本算法上。但是，为极大的点集生成非结构化网格仍然是使用大规模或高分辨率数据集的科学家的障碍。在之前的论文中，我们介绍了一种新颖的算法-并行独立分区的三角剖分（TIPP）它将域划分为许多可以并行三角剖分的独立分区。但是，仅使用单个主进程会导致性能瓶颈并抑制可伸缩性。在本文中，我们完善了对原始TIPP算法的描述，还将TIPP扩展为采用多个主进程，从而将计算负荷分布在多台计算机上。这项新设计提高了性能和可伸缩性，并且在30分钟内仅使用10个商品节点就可以生成200亿个三角形。