GeoGraph

Author:

Wang Yiqiu1,Yu Shangdi1,Dhulipala Laxman1,Gu Yan2,Shun Julian1

Affiliation:

1. MIT CSAIL

2. UC Riverside

Abstract

In 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.

Publisher

Association for Computing Machinery (ACM)

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Energy-Efficient In-Memory Accelerator for Graph Construction and Updating;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-06

2. Parallel Integer Sort: Theory and Practice;Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming;2024-02-20

3. Fast and Space-Efficient Parallel Algorithms for Influence Maximization;Proceedings of the VLDB Endowment;2023-11

4. High-Performance and Flexible Parallel Algorithms for Semisort and Related Problems;Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures;2023-06-17

5. Parallel Strong Connectivity Based on Faster Reachability;Proceedings of the ACM on Management of Data;2023-06-13

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