No abstract

This paper presents a new algorithm for the fast, shared memory multi-core computation of augmented merge trees on triangulations. In contrast to most existing parallel algorithms, our technique computes augmented trees. This augmentation is required to enable the full extent of merge tree based applications, including data segmentation. Our approach completely revisits the traditional, sequential merge tree algorithm to re-formulate the computation as a set of independent local tasks based on Fibonacci heaps. This results in superior time performance in practice, in sequential as well as in parallel thanks to the OpenMP task runtime. In the context of augmented contour tree computation, we show that a direct usage of our merge tree procedure also results in superior time performance overall, both in sequential and parallel. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the runtime efficiency of our approach as well as its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.

This paper presents a new algorithm for the fast, shared memory multi-threaded computation of contour trees on tetrahedral meshes. In contrast to previous multi-threaded algorithms, our technique computes the augmented contour tree. Such an augmentation is required to enable the full extent of contour tree based applications, including for instance data segmentation. Our approach relies on a range-driven domain partitioning. We show how to exploit such a partitioning to rapidly compute contour forests. We also show how such forests can be efficiently turned into the output contour tree. We report performance numbers that compare our approach to a reference sequential implementation for the computation of augmented contour trees. These experiments demonstrate the run-time efficiency of our approach. We demonstrate the utility of our approach with several data segmentation tasks. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.