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Geospatial Processing, such as queries based on point-to-polyline shortestdistance and point-in-polygon test, are fundamental to many scientific andengineering applications, including post-processing large-scale environmentaland climate model outputs and analyzing traffic and travel patterns frommassive GPS collections in transportation engineering and urban studies.Commodity parallel hardware, such as multi-core CPUs, many-core GPUs and IntelMIC accelerators, provide enormous computing power which can potentiallyachieve significant speedups on existing geospatial processing and open theopportunities for new applications. However, the realizable potential forgeospatial processing on these new hardware devices is largely unknown due tothe complexity in porting serial algorithms to diverse parallel hardwareplatforms. In this study, we aim at experimenting our data-parallel designs andimplementations of point-to-polyline shortest distance computation (P2P) andpoint-in-polygon topological test (PIP) on different commodity hardware usingreal large-scale geospatial data, comparing their performance and discussingimportant factors that may significantly affect the performance. Ourexperiments have shown that, while GPUs can be several times faster thanmulti-core CPUs without utilizing the increasingly available SIMD computingpower on Vector Processing Units (VPUs) that come with multi-core CPUs andMICs, multi-core CPUs and MICs can be several times faster than GPUs when VPUsare utilized. By adopting a Domain Specific Language (DSL) approach toexploiting the VPU computing power in geospatial processing, we are free fromprogramming SIMD intrinsic functions directly which makes the new approach moreeffective, portable and scalable. Our designs, implementations and experimentscan serve as case studies for parallel geospatial computing on modern commodityparallel hardware.

Large-Scale Geospatial Processing on Multi-Core and Many-Core Processors: Evaluations on CPUs, GPUs and MICs