It has been observed that in recent years Geographical Information Systems (GIS) and Spatial Information Systems have gone through substantial changes with respect to users, problems, problem domains, and data. The GIS community is rapidly expanding to include users from different sectors of the economy; along with this come different demands regarding the type, required speed, scope and scale of applications. Users in decision making positions require rapid, close to instantaneous responses even to complex queries. In addition, users today have access to an unprecedented amount of high resolution and high-quality data through scanners, satellites, range finders, medical equipment and other devices.

The effect of these changes is a rapid and huge increase in the computational demands placed on GIS. Processing a large raster often takes hours or even days;(processing a large raster, say of size 6000x6000 cells, at a speed of 1000 cells per second, would take 10 hours). Lengthy computation times in different applications have been reported as stated eg, in [22]. To keep up with the computational demands without sacrifice (ic, reduction in resolution or scope of model), parallel computing appears to be the only solution. Parallel hardware is readily available at a good price-toperformance ratio (in the small to medium range).