Document Type
Thesis
Date of Award
12-31-2020
Degree Name
Master of Science in Computer Science - (M.S.)
Department
Computer Science
First Advisor
David A. Bader
Second Advisor
Ioannis Koutis
Third Advisor
Dimitri Theodoratos
Abstract
Finding the transitive closure of a graph is a fundamental graph problem where another graph is obtained in which an edge exists between two nodes if and only if there is a path in our graph from one node to the other. The reachability matrix of a graph is its transitive closure. This thesis describes a novel approach that uses anti-sections to obtain the transitive closure of a graph. It also examines its advantages when implemented in parallel on a CPU using the Hornet graph data structure.
Graph representations of real-world systems are typically sparse in nature due to lesser connectivity between nodes. The anti-section approach is designed specifically to improve performance for large scale sparse graphs. The NVIDIA Titan V CPU is used for the execution of the anti-section parallel implementations. The Dual-Round and Hash-Based implementations of the Anti-Section transitive closure approach provide a significant speedup over several parallel and sequential implementations.
Recommended Citation
Patel, Sanyamee Milindkumar, "Accelerating transitive closure of large-scale sparse graphs" (2020). Theses. 1806.
https://digitalcommons.njit.edu/theses/1806
