Delaunay triangulation of large-scale datasets using two-level parallelism

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.