An Optimisation Approach for Construction of a Distributed Minimum Spanning Tree (DMST) Using MPI

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Md. Akkas Ali
Keyword(s):  
Parallel Computing ◽  
Parallel Programming ◽  
Single Machine ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Large Graphs

The present paper determines Distributed Minimum Spanning Tree (DMST) of very large graphs. It is very time consuming to calculate in a single machine. So the researcher has used parallel programming. One of the DMST algorithms that support parallel computing is Boruvkas algorithm. The researcher has used this algorithm. To avail the parallelism, we have used the MPI architecture.

Improve and Optimize Query Recommendation System by MST Algorithm and its MapReduce Implementation

2014 ◽  
Vol 701-702 ◽  
pp. 50-53
Author(s):  
Jian Liang Meng ◽  
Da Wei Li
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Spanning Tree ◽  
Recommendation System ◽  
Minimum Spanning Tree ◽  
Programming Model ◽  
Search Efficiency ◽  
Tree Algorithm ◽  
Query Recommendation ◽  
Parallel Query

Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.

scholarly journals AN APPROACH TO GENERATE MST WITHOUT CHECKING CYCLE

2005 ◽  
Vol 4 (2) ◽  
pp. 408-418
Author(s):  
Sharadindu Roy ◽  
Prof. Samar Sen Sarma
Keyword(s):  
Spanning Tree ◽  
Time Complexity ◽  
Minimum Spanning Tree ◽  
Undirected Graph ◽  
Good Time ◽  
Internet Service ◽  
Large Graphs ◽  
Real World Applications ◽  
Np Hard Problem ◽  
Better Than

Abstract: A minimum spanning tree of an undirected graph can be easily obtained using classical algorithms by Prim or Kruskal. MST generation is a NP hard problem. Now this paper represents an algorithm to find minimum spanning tree without checking cycle. Good time and space complexities are the major concerns of this algorithm. Running Time (complexity) of this algorithm = O(E*log V) (E = edges, V = nodes),which is obviously better than prim’s algorithm(complexity- E +Vlog V) .  This algorithms operate at O(E * log(V)) time, though Prim’s can be optimized to O(E + V log V) by using specialized data structures(heap). For large graphs, these algorithms can take significant amount of time to complete. This algorithm is important in many real world applications. One example is an internet service provider determining the best way to install underground wires in a neighbourhood in order to use the least amount of wire and dig the least amount of ground.

Parallel Computing Using the Prefix Problem

1994 ◽  
Author(s):  
S. Lakshmivarahan ◽  
Sudarshan K. Dhall
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithms ◽  
Parallel Programming ◽  
Building Blocks ◽  
Efficient Solutions ◽  
Computer Scientists ◽  
Important Approach

The prefix operation on a set of data is one of the simplest and most useful building blocks in parallel algorithms. This introduction to those aspects of parallel programming and parallel algorithms that relate to the prefix problem emphasizes its use in a broad range of familiar and important problems. The book illustrates how the prefix operation approach to parallel computing leads to fast and efficient solutions to many different kinds of problems. Students, teachers, programmers, and computer scientists will want to read this clear exposition of an important approach.

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