%AWang, Yiqiu%AYu, Shangdi%ADhulipala, Laxman%AGu, Yan%AShun, Julian%BJournal Name: ACM SIGOPS Operating Systems Review; Journal Volume: 55; Journal Issue: 1 %D2021%I %JJournal Name: ACM SIGOPS Operating Systems Review; Journal Volume: 55; Journal Issue: 1 %K %MOSTI ID: 10317713 %PMedium: X %TGeoGraph: A Framework for Graph Processing on Geometric Data %XIn many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the Geo- Graph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and b-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing. %0Journal Article