Shared-Memory Parallel Algorithms for Fully Dynamic Maintenance of 2-Connected Components

Finding the biconnected components of a graph has a large number of applications in many other graph problems including planarity testing, computing the centrality metrics, finding the (weighted) vertex cover, coloring, and the like. Recent years saw the design of efficient algorithms for this probl...

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Bibliographic Details
Published in:Proceedings - IEEE International Parallel and Distributed Processing Symposium pp. 1195 - 1205
Main Authors: Haryan, Chirayu Anant, Ramakrishna, G., Kothapalli, Kishore, Banerjee, Dip Sankar
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2022
Subjects:
biconnected components
Computational modeling
cut edges
cut vertices
Distributed processing
dynamic algorihtm
Heuristic algorithms
Instruction sets
Maintenance engineering
Measurement
multi core
parallel
Semiconductor device modeling
ISSN:1530-2075
Online Access:Get full text
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Summary:Finding the biconnected components of a graph has a large number of applications in many other graph problems including planarity testing, computing the centrality metrics, finding the (weighted) vertex cover, coloring, and the like. Recent years saw the design of efficient algorithms for this problem across sequential and parallel computational models. However, current algorithms do not work in the setting where the underlying graph changes over time in a dynamic manner via the insertion or deletion of edges. Dynamic algorithms in the sequential setting that obtain the biconnected components of a graph upon insertion or deletion of a single edge are known from over two decades ago. Parallel algorithms for this problem are not heavily studied. In this paper, we design shared-memory parallel algorithms that obtain the biconnected components of a graph subsequent to the insertion or deletion of a batch of edges. Our algorithms hence will be capable of exploiting the parallelism adduced due to a batch of updates. We implement our algorithms on an AMD EPYC 7742 CPU having 128 cores. Our experiments on a collection of 10 real-world graphs from multiple classes indicate that our algorithms outperform parallel state-of-the-art static algorithms. 1 1 The implementation and an extended version of this paper is at [5].
ISSN:1530-2075
DOI:10.1109/IPDPS53621.2022.00119