Conference

A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs

Bibliographic Details
Title: A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs
Authors: CECCARELLO, MATTEO, PIETRACAPRINA, ANDREA ALBERTO, PUCCI, GEPPINO, Upfal, Eli
Contributors: Ceccarello, Matteo, Pietracaprina, ANDREA ALBERTO, Pucci, Geppino, Upfal, Eli
Publication Year: 2016
Collection: Padua Research Archive (IRIS - Università degli Studi di Padova)
Subject Terms: Graph Analytic, Parallel Graph Algorithm, Weighted Graph Decomposition, Weighted Diameter Approximation, MapReduce
Description: We present a space and time efficient practical parallel algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. The core of the algorithm is a weighted graph decomposition strategy generating disjoint clusters of bounded weighted radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; moreover, for important practical classes of graphs, it runs in a number of rounds asymptotically smaller than those required by the natural approximation provided by the state-of-the-art -stepping SSSP algorithm, which is its only practical linear-space competitor in the aforementioned computational scenario. We complement our theoretical findings with an extensive experimental analysis on large benchmark graphs, which demonstrates that our algorithm attains substantial improvements on a number of key performance indicators with respect to the aforementioned competitor, while featuring a similar approximation ratio (a small constant less than 1.4, as opposed to the polylogarithmic theoretical bound).
Document Type: conference object
File Description: CD-ROM
Language: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000391251800003; ispartofbook:Proceedings of the 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); firstpage:1; lastpage:10; numberofpages:10; https://hdl.handle.net/11577/3191093
DOI: 10.1109/IPDPS.2016.61
Availability: https://hdl.handle.net/11577/3191093
https://doi.org/10.1109/IPDPS.2016.61
Accession Number: edsbas.352885FD
Database: BASE
Description
DOI:10.1109/IPDPS.2016.61