high performance computing on graphics processing units: hgpu.org

The main question explored in this thesis is how to define novel parallel random-access data structures for surface and image spatial data with efficient construction, storage, and query memory access patterns. Our main contribution is a set of parallel-efficient methods to evaluate irregular, sparse or even implicit geometries and textures in different applications: a method to decouple shape and shading details from high-resolution meshes, mapping them interactively onto lower resolution simpler domains; an editable framework to map highresolution meshes to simpler cube-based domains, generating a parallel-friendly quad-based representation; a new parallel hashing scheme compacting spatial data with high load factors, which has the unique advantage of exploiting spatial coherence in input data and access patterns.