high performance computing on graphics processing units: hgpu.org

Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis. IDW interpolation assigns geographical values to unknown spatial points by using values from a usually scattered set of known points, and viewshed identifies the cells in a spatial raster that can be seen by observers. Although the implementations of both algorithms are available for different scales of input data, the computation for a large-scale domain requires a mass amount of cycles that limits their usage. Due to the growing popularity of Graphics Processing Unit (GPU) for general purpose applications, we aim to accelerate geospatial analysis via a GPU based parallel computing approach. In this paper, we propose a generic methodological framework for geospatial analysis based on GPU and its programming model CUDA, and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework, which gives rise to a high computational throughput. The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable to parallelize the algorithms of geospatial analysis. The experimental results show that the computation time of both CUDA-based parallel versions can be reduced by an order of magnitude compared to classical sequential versions, without losing their accuracy of interpolation and visibility judgement.