Linear spatial trees are typically constructed in two discrete, consecutive stages: calculating location codes, and sorting the spatial data according to the codes. Additionally, a GPU R-tree construction algorithm exists which likewise consists of sorting the spatial data and calculating nodes' bounding boxes. Current GPUs are approximately three orders of magnitude faster than CPUs for perfectly vectorizable problems. However, the best known GPU sorting algorithms only achieve 10-20 times speedup over sequential CPU algorithms. Both calculating location codes and bounding boxes are perfectly vectorizable problems. We thus investigate the construction of linear quadtrees, R-trees, and linear k-d trees using the GPU for location code and bounding box calculation, and parallel CPU algorithms for sorting. In this endeavor, we show how existing GPU linear quadtree and R-tree construction algorithms may be modified to be heterogeneous, and we develop a novel linear k-d tree construction algorithm which uses an existing parallel CPU quicksort partition algorithm. We implement these heterogeneous construction algorithms, and we show that heterogeneous construction of spatial data structures can approach the speeds of homogeneous GPU algorithms, while freeing the GPU to be used for better vectorizable problems.
|Commitee:||Altman, Tom, Ra, Ilkyeun|
|School:||University of Colorado at Denver|
|School Location:||United States -- Colorado|
|Source:||MAI 54/04M(E), Masters Abstracts International|
|Keywords:||Graphics processing units, Heterogeneous, Quadtrees, R-trees, Spatial trees|
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