Parallelizing Spatial Databases on Shared-Memory Multiprocessors

Several emerging visualization applications such as ight simulators, distributed interactive simulation (DIS), and virtual reality are using geographic information systems (GISs) for high-delity representation of actual terrains. These applications impose stringent performance and response-time restrictions which require parallelization of the GIS and shared-memory multiprocessors (SMPs) are well suited for parallelizing the GIS. Currently, we are developing a high performance GIS on an SMP (Silicon Graphics \Power Challenge") as a part of the virtual GIS project of Army Research Laboratories. Our experimental results with range-query operation show that data-partitioning is an eeective approach towards achieving high performance in GIS. As partitioning extended spatial objects is diicult, special techniques such as systematic declustering are needed to parallelize the range-query operation. Systematic declustering methods outperform random declustering methods for range query problems. Even random declustering coupled with dynamic load-balancing (DLB) is outper-formed by static load-balancing (SLB) methods with systematic static declustering. In addition, the performance of DLB methods can be improved by using the declustering methods for determining the subsets of polygons to be transferred during run-time.