Parallelizing and Distributing the Random Graph Generation Algorithms

Vanam, V K (2015) Parallelizing and Distributing the Random Graph Generation Algorithms. Masters thesis, Indian Institute of Technology Hyderabad.

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Random Graphs evolved as a major tool for modelling the complex net works. Random Graphs have wide range of applications. Random Graph can be defined as a probability distribution over graph. Erdos Renyi Random Graph generation model is one of the most popular and best studied models of a network. Erdos Renyi Random Graph model G(n,p) generates random graph with n vertices where each edge appears with probability p. Despite the fact that the evolution of random graphs as data representation and modelling tool, the previous research hasn’t focused on the efficiency in generating random graphs. The Random Graph generation algorithms perform poor when generating massively large graphs and fails to use the parallel processing capabilities of modern hardware. The goal of my Thesis work is to parallelize the Random Graph generation models using GPGPU (General Purpose Graphics Processing Unit)to improve the performance.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: Random Graphs, Erdos Renyi Random Graph, Parallelizing Random Graph Generation, TD356
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 03 Jul 2015 05:59
Last Modified: 14 May 2019 11:10
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