A Lightweight CUDA-Based Parallel Map Reprojection Method for Raster Datasets of Continental to Global Extent.

Geospatial transformations in the form of reprojection calculations for large datasets can be computationally intensive; as such, finding better, less expensive ways of achieving these computations is desired. In this paper, we report our efforts in developing a Compute Unified Device Architecture (CUDA)-based parallel algorithm to perform map reprojections for raster datasets on personal computers using Graphics Processing Units (GPUs). This algorithm has two unique features: a) an output-space-based parallel processing strategy to handle transformations more rigorously, and b) a chunk-based data decomposition method for projected space in conjunction with an on-the-fly data retrieval mechanism to avoid memory overflow. To demonstrate the performance of our CUDA-based map reprojection approaches, we have conducted tests between this method and the traditional serial version using the Central Processing Unit (CPU). The results show that speedup ratios range from 10 times to 100 times in all test scenarios. The lessons learned from the tests are summarized.