The global warming has an effect on changes of a snow cover over wintertime. This effect is observed in Slovak ski resorts, too. A prediction of these trends is important to build new and keep the existing ski resorts. We are able to analyze the snow cover depth in detail. This analysis is based on many continuous observations and measurements at the specific climatologic stations of Slovak Hydrometeorogical Institute. However, these climatologic stations do not render accurately all ski places, which could be examined. The aim of this work is the depth of the snow cover computing in the desired point based on the geographical characteristics of a specific geographical point in a modeled area. The main characteristics of the computing is the fact that it is time-consuming. One solution is a utilization of graphics processing units (GPUs) where the availability of enormous computational performance of easily programmable GPUs can rapidly decrease time of computing.In our article we demonstrate how to deploy the CUDA architecture, which utilizes the powerful parallel computation capacity of GPU, to accelerate computational process of snow cover depth using the inverse-distance weighting (IDW) method. The outputs are visualized by the Grass GIS tool.
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