In the last decades cosmological N-body dark matter simulations have enabled ab initio studies of the formation of structure in the Universe. Gravity amplified small density fluctuations generated shortly after the Big Bang, leading to the formation of galaxies in the cosmic web. These calculations have led to a growing demand for methods to analyze time-dependent particle based simulations. Rendering methods for such N-body simulation data usually employ some kind of splatting approach via point based rendering primitives and approximate the spatial distributions of physical quantities using kernel interpolation techniques, common in SPH (Smoothed Particle Hydrodynamics)-codes. This paper proposes three GPU-assisted rendering approaches, based on a new, more accurate method to compute the physical densities of dark matter simulation data. It uses full phase-space information to generate a tetrahedral tessellation of the computational domain, with mesh vertices defined by the simulation's dark matter particle positions. Over time the mesh is deformed by gravitational forces, causing the tetrahedral cells to warp and overlap. The new methods are well suited to visualize the cosmic web. In particular they preserve caustics, regions of high density that emerge, when several streams of dark matter particles share the same location in space, indicating the formation of structures like sheets, filaments and halos. We demonstrate the superior image quality of the new approaches in a comparison with three standard rendering techniques for N-body simulation data.
In this paper we propose a technique for resampling scalar fields given on unstructured tetrahedral grids. This technique takes advantage of hardware accelerated polygon rendering and 2D texture mapping and thus avoids any sorting of the tetrahedral elements. Using this technique, we have built a visualization tool that enables us to either resample the data onto arbitrarily sized Cartesian grids, or to directly render the data on a slice-by-slice basis. Since our approach does not rely on any pre-processing of the data, it can be utilized efficiently for the display of time-dependent unstructured grids where geometry as well as topology change over time. In this paper we address the problem of interactively resampling unstructured grids. Three algorithms are presented. They all allow adaptive resampling of an unstructured grid on a multiresolution hierarchy of arbitrarily sized cartesian grids according to a varying element size. Two of the algorithms presented take advantage of hardware accelerated polygon rendering and 2D texture mapping. In exploiting new features of modern PC graphics adapters, the first algorithm tries to significantly minimize the number of polygons to be rendered. Reducing rasterization requirements is the main goal of the second algorithm, which distributes the computational workload differently between the main processor and the graphics chip. By comparing them to a new pure software approach, an optimal software-hardware balance is studied. We end up with a hybrid approach which greatly improves the performance of hardware-assisted resampling by involving the main processor to a higher degree and thus enabling resampling at nearly interactive rates.