Parallel two-stage algorithms for solving the PageRank problem

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Title: Parallel two-stage algorithms for solving the PageRank problem
Authors: Migallón Gomis, Héctor | Migallón, Violeta | Penadés, Jose
Research Group/s: Computación de Altas Prestaciones y Paralelismo (gCAPyP)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: PageRank | Parallel algorithms | Two-stage methods | Shared memory | Distributed memory
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: Nov-2018
Publisher: Elsevier
Citation: Advances in Engineering Software. 2018, 125: 188-199. doi:10.1016/j.advengsoft.2018.03.002
Abstract: In this work we present parallel algorithms based on the use of two-stage methods for solving the PageRank problem as a linear system. Different parallel versions of these methods are explored and their convergence properties are analyzed. The parallel implementation has been developed using a mixed MPI/OpenMP model to exploit parallelism beyond a single level. In order to investigate and analyze the proposed parallel algorithms, we have used several realistic large datasets. The numerical results show that the proposed algorithms can speed up the time to converge with respect to the parallel Power algorithm and behave better than other well-known techniques.
Sponsor: This research was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Commission (FEDER funds) under Grant Number TIN2015-66972-C5-4-R.
ISSN: 0965-9978 (Print) | 1873-5339 (Online)
DOI: 10.1016/j.advengsoft.2018.03.002
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier Ltd.
Peer Review: si
Publisher version:
Appears in Collections:INV - gCAPyP - Artículos de Revistas

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