Analyzing fuzzy surface modeling using load-balanced computation

Models that can code and render uncertainty support users doing spatial analysis, because they provide direct access to and visualization of the imprecision induced by data compression. Interval or fuzzy models provide an important alternative to traditional approaches, especially for scientists and decision makers eager to explore different scenarios, possibly using different parameters. We found parallel computing to be a key for using advanced modeling techniques. In many cases, however, the high computational cost of even simple interrogation processes might prohibit using fuzzy models: In this context, we present a load-balanced solution that efficiently uses parallel computing to analyze surface models based on the fuzzy B-spline model. We obtained efficient portability of the algorithm by introducing a dynamic and flexible load-balancing strategy that provides good results on different architectures, including a Cray-T3D