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.

Abstract Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

Abstract We describe a hybrid acceleration structure for ray tracing. The hybrid structure is a Bounding Volume Hierarchy (BVH) where the leaf nodes are tetrahedralized for a decent ray‐surface intersection performance. We use the hybrid acceleration structure (BTH) in a two‐level acceleration structure for rendering animated scenes. There is a BVH at the top level in this two‐level structure and the proposed hybrid structure (BTH) at the bottom level. We test the proposed two‐level structure (BVH‐BTH) for various animated scenes and obtained promising results against other acceleration structures in terms of rendering times. The two‐level BVH‐BTH structure outperforms the two‐level BVH structure for the tested dynamic scenes.

Augmentation of virtual agents in real crowd videos is an important task for different applications from design simulations of social environments to modeling abnormalities in crowd behavior. We propose a framework for this task, namely for augmenting virtual agents in real crowd videos. Our framework utilizes homography-based video stabilization, Dalal-Triggs detector [1] for pedestrian detection and state-based tracking algorithms to automatically locate the pedestrians in video frames and project them into our 3D simulated environment, where the navigable area of the simulated environment is available as a manually designed and located navigation mesh. We represent the real pedestrians in the video as simple three-dimensional (3D) models in our simulation environment. 3D models representing real, projected agents and the augmented virtual agents are simulated using local path planning coupled with a collision detection and avoidance algorithm, called Reciprocal Velocity Obstacles (RVO) [2]. The virtual agents augmented into the video move plausibly without colliding with static and dynamic obstacles, including other virtual agents and real pedestrians. We provide an extensive graphical user interface for controlling the virtual agents in the scene, including collision avoidance parameters, adjusting the camera in the scene and some standard video player options.