Image-space decomposition algorithms for sort-first parallel volume rendering of unstructured grids

Twelve adaptive image-space decomposition algorithms are presented for sort-first parallel direct volume rendering (DVR) of unstructured grids on distributed-memory architectures. The algorithms are presented under a novel taxonomy based on the dimension of the screen decomposition, the dimension of the workload arrays used in the decomposition, and the scheme used for workload-array creation and querying the workload of a region. For the 2D decomposition schemes using 2D workload arrays, a novel scheme is proposed to query the exact number of screen-space bounding boxes of the primitives in a screen region in constant time. A probe-based chains-on-chains partitioning algorithm is exploited for load balancing in optimal 1D decomposition and iterative 2D rectilinear decomposition (RD). A new probe-based optimal 2D jagged decomposition (OJD) is proposed which is much faster than the dynamic-programming based OJD scheme proposed in the literature. The summed-area table is successfully exploited to query the workload of a rectangular region in constant time in both OJD and RD schemes for the subdivision of general 2D workload arrays. Two orthogonal recursive bisection (ORB) variants are adapted to relax the straight-line division restriction in conventional ORB through using the medians-of-medians approach on regular mesh and quadtree superimposed on the screen. Two approaches based on the Hilbert space-filling curve and graph-partitioning are also proposed. An efficient primitive classification scheme is proposed for redistribution in 1D, and 2D rectilinear and jagged decompositions. The performance comparison of the decomposition algorithms is modeled by establishing appropriate quality measures for load-balancing, amount of primitive replication and parallel execution time. The experimental results on a Parsytec CC system using a set of benchmark volumetric datasets verify the validity of the proposed performance models. The performance evaluation of the decomposition algorithms is also carried out through the sort-first parallelization of an efficient DVR algorithm

Interactive ray tracing of massive and deformable models

Ray tracing is a fundamental algorithm used for many applications such as computer graphics, geometric simulation, collision detection and line-of-sight computation. Even though the performance of ray tracing algorithms scales with the model complexity, the high memory requirements and the use of static hierarchical structures pose problems with massive models and dynamic data-sets. We present several approaches to address these problems based on new acceleration structures and traversal algorithms. We introduce a compact representation for storing the model and hierarchy while ray tracing triangle meshes that can reduce the memory footprint by up to 80%, while maintaining high performance. As a result, can ray trace massive models with hundreds of millions of triangles on workstations with a few gigabytes of memory. We also show how to use bounding volume hierarchies for ray tracing complex models with interactive performance. In order to handle dynamic scenes, we use refitting algorithms and also present highly-parallel GPU-based algorithms to reconstruct the hierarchies. In practice, our method can construct hierarchies for models with hundreds of thousands of triangles at interactive speeds. Finally, we demonstrate several applications that are enabled by these algorithms. Using deformable BVH and fast data parallel techniques, we introduce a geometric sound propagation algorithm that can run on complex deformable scenes interactively and orders of magnitude faster than comparable previous approaches. In addition, we also use these hierarchical algorithms for fast collision detection between deformable models and GPU rendering of shadows on massive models by employing our compact representations for hybrid ray tracing and rasterization