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.
|Original language||English (US)|
|Number of pages||24|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - 1998|
Bibliographical noteFunding Information:
This project is sponsored, in part, by the Army High-Performance Computing Research Center under the auspices of the Department of the Army, the Army Research Laboratory cooperative agreement No. DAAH04-95-2-0003, Contract No. DAAH04-95-C-0008, the content of which does not necessarily reflect the position or the policy of the government of the United States of America, and no official endorsement should be inferred.
- Declustering methods
- Geographic information systems
- High performance
- Polygon clipping
- Range query