Towards Personal High-Performance Geospatial Computing (HPC-G): Perspectives and a Case Study

摘要

Cluster computing, Cloud computing and GPU computing play overlapping and complementary roles in parallel processing of geospatial data within the general HPC framework. The fast increasing hardware capacities of modern personal computers equipped with chip multiprocessor CPUs and massively parallel GPUs have made high performance computing of large-scale geospatial data in a personal computing environment possible. We discuss the framework of Personal HPC-G and compare it with traditional Cluster computing and the newly emerging Cloud computing. We consider Personal HPC-G possesses many favorable features: low initial and operational costs, good support for data management and excellent support for both numeric modeling and interactive visualization. A case study on developing a parallel spatial statistics module for visual explorations on top of Personal HPC-G is subsequently presented.