An optimized label-broadcast parallel algorithm for connected components labeling

2010 ◽  
Author(s):  
Joao Marcelo Xavier Natario Teixeira ◽  
Bernardo Reis ◽  
Veronica Teichrieb ◽  
Judith Kelner
Keyword(s):  
Parallel Algorithm ◽  
Connected Components

A PARALLEL ALGORITHM FOR ANALYZING CONNECTED COMPONENTS IN BINARY IMAGES

1992 ◽  
Vol 06 (02n03) ◽  
pp. 315-333 ◽  
Author(s):  
FRANCO CHIAVETTA ◽  
VITO DI GESÙ ◽  
ROSALIA RENDA
Keyword(s):  
Parallel Algorithm ◽  
Parallel Implementation ◽  
Discrete Space ◽  
Space Complexity ◽  
Connected Components ◽  
Two Dimensional ◽  
Binary Images ◽  
Cylindrical Algebraic Decomposition ◽  
Time Space ◽  
Connectivity Degree

In this paper, a parallel algorithm for analyzing connected components in binary images is described. It is based on the extension of the Cylindrical Algebraic Decomposition (CAD) to a two-dimensional (2D) discrete space. This extension allows us to find the number of connected components, to determine their connectivity degree, and to solve the visibility problem. The parallel implementation of the algorithm is outlined and its time/space complexity is given.

scholarly journals Designing and analyzing highly scalable and reliable of full fledged parallel algorithm for computing strongly connected com-ponents to analyze social graphs

2018 ◽  
Vol 7 (2.12) ◽  
pp. 374
Author(s):  
Dr Lokesh A ◽  
Mr Maria Navin J R ◽  
Mr Balaji K ◽  
Mr Pradeep M
Keyword(s):  
Parallel Algorithm ◽  
Communication Networks ◽  
Programming Model ◽  
Fault Tolerant ◽  
Connected Components ◽  
New Paradigm ◽  
Strongly Connected Components ◽  
Bulk Synchronous Parallel ◽  
Strongly Connected ◽  
Social Graphs

With the recent advent of Big Data, developing efficient distributed algorithms for computing Strongly Connected Components of a large dataset has received increasing interests. For example, social networks, information networks and communication networks such as the communities of people that have formed on those networks, what community a person belongs or finding cyclic de-pendencies in the graph.Apache Giraph is an open-source implementation of Google’s Pregel. It is an iterative and real-time graph processing engine designed to be scalable, fault tolerant and highly efficient. This framework provides an accurate platform for the development of parallel algorithms in a distributed environ-ment. It adopts a vertex-centric programming model inspired by Bulk Synchronous Parallel model. A strongly connected component is a maximal sub graph in which all vertices are reachable from every other vertex. Maximal means that it is the largest possible sub graph. It is not possible to find another vertex anywhere in the graph such that it could be added to the sub graph and all the verti-ces in the sub graph would still be connected. In a directed graph G, a pair of vertices u and v are said to be strongly connected to each other if there is a path in each direction between them. Here, we have implemented a parallel algorithm which is based on the new paradigm of graph decomposi-tion for computing strongly connected components. The final outcome mainly focuses on the reduc-tion of total communication costs. 

scholarly journals An Adaptive Parallel Algorithm for Computing Connected Components

2017 ◽  
Vol 28 (9) ◽  
pp. 2428-2439 ◽  
Author(s):  
Chirag Jain ◽  
Patrick Flick ◽  
Tony Pan ◽  
Oded Green ◽  
Srinivas Aluru
Keyword(s):  
Parallel Algorithm ◽  
Connected Components
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