Visualization of Large Non-Trivially Partitioned Unstructured Data With Native Distribution on High-Performance Computing Systems

• Published in: IEEE transactions on visualization and computer graphics Vol. 31; no. 9; pp. 5000 - 5014
• Main Authors: Sahistan, Alper, Demirci, Serkan, Wald, Ingo, Zellmann, Stefan, Barbosa, Joao, Morrical, Nate, Gudukbay, Ugur
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2025
• Subjects: Data visualization; Deep compositing; Distributed databases; Finite element analysis; Graphics processing units; ray-marching; Rendering (computer graphics); Scalability; scientific visualization; sort-last compositing; Sorting; unstructured volumetric mesh; volume rendering
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2024.3427335

Interactively visualizing large finite element simulation data on High-Performance Computing (HPC) systems poses several difficulties. Some of these relate to unstructured data, which, even on a single node, is much more expensive to render compared to structured volume data. Worse yet, in the data parallel rendering context, such data with highly non-convex spatial domain boundaries will cause rays along its silhouette to enter and leave a given rank's domains at different distances. This straddling, in turn, poses challenges for both ray marching, which usually assumes successive elements to share a face, and compositing, which usually assumes a single fragment per pixel per rank. We holistically address these issues using a combination of three inter-operating techniques: first, we use a highly optimized GPU ray marching technique that, given an entry point, can march a ray to its exit point with high-performance by exploiting an exclusive-or (XOR) based compaction scheme. Second, we use hardware-accelerated ray tracing to efficiently find the proper entry points for these marching operations. Third, we use a "deep" compositing scheme to properly handle cases where different ranks' ray segments interleave in depth. We use GPU-to-GPU remote direct memory access (RDMA) to achieve interactive frame rates of 10-15 frames per second and higher for our motivating use case, the Fun3D NASA Mars Lander.