Towards a GPU Parallel Software for Environmental Data Fitting

In this paper we are interested in fitting data arising from environmental problems. To this aim, several procedures and methods are available in literature, and all of them involve high computational complexity when real dataset are considered. In this work, we propose a novel GPU parallel algorithm, specifically designed for fitting environmental and bathymetric data, which is based on the Kriging method. The implementation exploits the capabilities of advanced parallel computing architectures for efficiently solving large size problems. We obtain remarkable gain in terms of execution times and memory usage, as confirmed by experimental tests, by combining suitable parallel numerical libraries and ad hoc parallel kernels in CUDA environment.