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. In this paper, 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) the work-load metric, 2) the spatial-extent of the work-load, 3) the distribution of the work-load over the spatial-extent, and 4) the 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.