Spatial Join with R-Tree on Graphics Processing Units
Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand
8th International Conference on Computing and Information Technology, 2012
@article{yampaka2012spatial,
title={Spatial Join with R-Tree on Graphics Processing Units},
author={Yampaka, T. and Chongstitvatana, P.},
year={2012}
}
Spatial operations such as spatial join combine two objects on spatial predicates. It is different from relational join because objects have multi dimensions and spatial join consumes large execution time. Recently, many researches tried to find methods to improve the execution time. Parallel spatial join is one method to improve the execution time. Comparison between objects can be done in parallel. Spatial datasets are large. R-Tree data structure can improve the performance of spatial join. In this paper, a parallel spatial join on Graphic processor unit (GPU) is introduced. The capacity of GPU which has many processors to accelerate the computation is exploited. The experiment is carried out to compare the spatial join between a sequential implementation with C language on CPU and a parallel implementation with CUDA C language on GPU. The result shows that the spatial join on GPU is faster than on a conventional processor.
March 19, 2012 by hgpu
