An MPI-based Framework for Proessing Spatial Vector Data on Heterogeneous Distributed Systems

Geographic information system (GIS) is utilized in geomorphic analysis, hazard mapping, evacuation route planning and so on. Some GISs employ heterogeneous distributed systems consisting of dissimilar machines and cloud infrastructures because spatial vector data, which has the large number of vertex data, requires heavy spatial processing. However, it is difficult for spatial analysts and researchers to efficiently perform the spatial processing by such GISs because they need to consider load balance. Additionally, learning parallel programming, such as message passing interface (MPI), also is required. In this paper, to alleviate such burdens, we present an MPI-based framework that performs the spatial processing for the spatial vector data in the heterogeneous distributed systems. Our framework consists of an execution time predictor, schedulers and a wrapper library for hiding MPI programming. Our experimental results show that our framework is 12.9 times faster than sequential processing in our GIS consisting Amazon EC2 and a local cluster while the number of source code steps with our library is almost identical to that of the sequential version.