Contour Algorithm for Connectivity

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm with two or higher order operators can identify all connected components of an undirected graph within O (log dmax) iterations, with each iteration involving O(m) work, where dmax represents the largest diameter among all components in the given graph, and m is the total number of edges in the graph. Importantly, each iteration is highly parallelizable, making use of the efficient minimum-mapping operator applied to all edges. To further enhance its practical performance, we optimize the Contour algorithm through asynchronous updates, early convergence checking, eliminating atomic operations, and choosing more efficient mapping operators. Our implementation of the Contour algorithm has been integrated into the open-source framework Arachne. Arachne extends Arkouda for large-scale interactive graph analytics, providing a Python API powered by the high-productivity parallel language Chapel. Experimental results on both real-world and synthetic graphs demonstrate the superior performance of our proposed Contour algorithm compared to state-of-the-art large-scale parallel algorithm FastSV and the fastest shared memory algorithm ConnectIt. On average, Contour achieves a speedup of 7.3x and 1.4x compared to FastSV and ConnectIt, respectively. All code for the Contour algorithm and the Arachne framework is publicly available on GitHub11https://githuh.comJBears-R-Us/arkouda-njit, ensuring transparency and reproducibility of our work.

Identifier

85190583419 (Scopus)

ISBN

[9798350383225]

Publication Title

Proceedings 2023 IEEE 30th International Conference on High Performance Computing Data and Analytics Hipc 2023

External Full Text Location

https://doi.org/10.1109/HiPC58850.2023.00022

First Page

66

Last Page

75

Grant

CCF-2109988

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS