Scalable Computation of Stream Surfaces on Large Scale Vector Fields

Abstract

Stream surfaces and streamlines are two popular methods for visualizing three-dimensional flow fields. While several parallel streamline computation algorithms exist, relatively little research has been done to parallelize stream surface generation. This is because load-balanced parallel stream surface computation is nontrivial, due to the strong dependency in computing the positions of the particles forming the stream surface front. In this paper, we present a new algorithm that computes stream surfaces efficiently. In our algorithm, seeding curves are divided into segments, which are then assigned to the processes. Each process is responsible for integrating the segments assigned to it. To ensure a balanced computational workload, work stealing and dynamic refinement of seeding curve segments are employed to improve the overall performance. We demonstrate the effectiveness of our parallel stream surface algorithm using several large scale flow field data sets, and show the performance and scalability on HPC systems.