Declustering and load-balancing methods for parallelizing geographic information systems

Declustering and load balancing are important issues in designing a high performance geographic information system (HPGIS), which is a central component of many interactive applications such as real time terrain visualization. The current literature provides efficient methods for declustering spatial point data. However, there has been little work toward developing efficient declustering methods for collections of extended objects, like chains of line segments and polygons. We focus on the data partitioning approach to parallelizing GIS operations. We provide a framework for declustering collections of extended spatial objects by identifying the following key issues: (1) work load metric; (2) spatial extent of the work load; (3) distribution of the work load over the spatial extent; and (4) declustering method. We identify and experimentally evaluate alternatives for each of these issues. In addition, we also provide a framework for dynamically balancing the load between different processors. We experimentally evaluate the proposed declustering and load balancing methods on a distributed memory MIMD machine (Cray T3D). Experimental results show that the spatial extent and the work load metric are important issues in developing a declustering method. Experiments also show that the replication of data is usually needed to facilitate dynamic load balancing, since the cost of local processing is often less than the cost of data transfer for extended spatial objects. In addition, we also show that the effectiveness of dynamic load balancing techniques can be improved by using declustering methods to determine the subsets of spatial objects to be transferred during runtime.