This study presents a massively parallel spatial computing approach that uses generalpurpose graphics processing units (GPUs) to accelerate Ripley's K function for univariate spatial point pattern analysis. Ripley's K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley's K function. In this study, we developed a massively parallel approach of Ripley's K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley's K function. Variablegrained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley's K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley's K function parallelized within GPU environments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.