Kavli Affiliate: Ralf Kaehler
| First 5 Authors: Ralf Kaehler, Tom Abel, , ,
| Summary:
Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique
to study processes with high spatial and temporal dynamic range. It reduces
computational requirements by adapting the lattice on which the underlying
differential equations are solved to most efficiently represent the solution.
Particularly in astrophysics and cosmology such simulations now can capture
spatial scales ten orders of magnitude apart and more. The irregular locations
and extensions of the refined regions in the SAMR scheme and the fact that
different resolution levels partially overlap, poses a challenge for GPU-based
direct volume rendering methods. kD-trees have proven to be advantageous to
subdivide the data domain into non-overlapping blocks of equally sized cells,
optimal for the texture units of current graphics hardware, but previous
GPU-supported raycasting approaches for SAMR data using this data structure
required a separate rendering pass for each node, preventing the application of
many advanced lighting schemes that require simultaneous access to more than
one block of cells. In this paper we present a single-pass GPU-raycasting
algorithm for SAMR data that is based on a kD-tree. The tree is efficiently
encoded by a set of 3D-textures, which allows to adaptively sample complete
rays entirely on the GPU without any CPU interaction. We discuss two different
data storage strategies to access the grid data on the GPU and apply them to
several datasets to prove the benefits of the proposed method.
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