Distributed Quadtree Processing

Quadtrees have been widely used in computer vision, spatial database, and related area due to their compactness and regularity. It has long been claimed that quadtree related algorithms are suitable for parallel and distributed implementation, but only little work has been done to justify this claim. The simple input partitioning method used in low level image processing could not be equally applied to distributed quadtree processing since it suffers the problem of load imbalance. Load balancing is one of the most crucial issues in distributed processing. In the context of distributed quadtree processing, it appears at various stages of processing in different forms; each requires its own solutions. The diversity in approaches to load balancing is further multiplied by the differences in the characteristics of types of data represented by,and spatial operations performed on quadtrees. In this paper, we propose a new approach to distributed quadtree processing using a task queue mechanism. We discuss dynamic load balancing and related issues in the context of distributed quadtree processing, and provide possible solutions. The proposed algorithms have been implemented on the Nectar system (currently being developed at Carnegie Mellon). Experimental results are also included in the paper.