Scalable Parallel Flow Visualization Using 3D Line Integral Convolution for Large Scale Unstructured Simulation Data

Abstract

To address the need of highly efficient and scalable parallel flow visualization methods, we developed a flow visualization system for large unstructured simulation data using parallel 3D line integral convolution (LIC). The main consideration for a parallel LIC implementation is a trade-off between the additional memory cost of replicating cells at sub-domain boundaries, or the communication cost of exchanging those data among computation nodes. To improve scalability, we introduce a load-balancing scheme that partitions datasets based on estimated LIC computation time. We also introduce a data-driven sub-domain extension scheme that determines which external cells at sub-domain boundary need to be added based on current boundary cells, which reduces memory overhead because the same visual quality can be achieved with a significantly smaller number of replicated external cells. We evaluate our visualization method by first comparing its parallel scalability to traditional integral field lines methods. Next, we compare our cost-driven domain decomposition method to existing methods to verify that ours leads to more balanced computation and improved scalability. Finally, we compare our data-driven sub-domain expansion method to traditional layer-based expansion methods in terms of memory overhead and visual quality. We conclude that our parallel 3D LIC method is an efficient and scalable approach to visualization of large and complex 3D vector fields.