Experimental results of a coarse-grained parallel algorithm for spanning tree and connected components

2010 ◽  
Author(s):  
Edson Norberto Cacer ◽  
Henrique Mongelli ◽  
Christiane Nishibe ◽  
Siang Wun Song
Keyword(s):  
Parallel Algorithm ◽  
Spanning Tree ◽  
Experimental Results ◽  
Coarse Grained ◽  
Connected Components

PARALLEL PATTERN MATCHING WITH SCALING

2001 ◽  
Vol 11 (01) ◽  
pp. 125-138 ◽  
Author(s):  
H. MONGELLI ◽  
S. W. SONG
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Pattern Matching ◽  
Computing Time ◽  
Parallel Machine ◽  
Experimental Results ◽  
Coarse Grained ◽  
Matching Problem ◽  
Communication Round ◽  
Parallel Pattern

Given a text and a pattern, the problem of pattern matching consists of determining all the positions of the text where the pattern occurs. When the text and the pattern are matrices, the matching is termed bidimensional. There are variations of this problem where we allow the matching using a somehow modified pattern. A modification that we will allow is that the pattern can be scaled. We propose a new parallel algorithm for this problem, under the CGM (Coarse Grained Multicomputer) model. This algorithm requires linear local computing time in the input, linear memory and uses only one communication round, during which at most a linear amount of data is exchanged. To be the best of our knowledge, there are no known parallel algorithms for the bidimensional pattern matching problem with scaling in the literature. This proposed algorithm was implemented in C, using the PVM interface and was executed on a Parsytec PowerXplorer parallel machine. The experimental results obtained were very promising and showed significant speedups.

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. 

A Parallel Algorithm for Mining Non-Redundant Recurrent Rules from a Sequence Database

2019 ◽  
Vol 23 (5) ◽  
pp. 956-961
Author(s):  
Seung-Yong Yoon ◽  
◽  
Hirohisa Seki
Keyword(s):  
Parallel Algorithm ◽  
Processing Time ◽  
Experimental Results ◽  
Task Parallelism ◽  
Sequence Database ◽  
Computation Cost ◽  
Software Specification ◽  
Specification And Verification

We propose a parallel algorithm for mining non-redundant recurrent rules from a sequence database. Recurrent rules, proposed by Lo et al. [1], can express “Whenever a series of precedent events occurs, eventually a series of consequent events occurs,” and they have shown the usefulness of recurrent rules in various domains, including software specification and verification. Although some algorithms such as NR3 have been proposed, mining non-redundant recurrent rules still requires considerable processing time. To reduce the computation cost, we present a parallel approach to mining non-redundant recurrent rules, which fully utilizes the task-parallelism in NR3. We also give some experimental results, which show the effectiveness of our proposed method.

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