Publication Type

Conference Proceeding Article

Publication Date

9-2017

Abstract

Personalized PageRank (PPR) is a well-known proximitymeasure in graphs. To meet the need for dynamic PPRmaintenance, recent works have proposed a local updatescheme to support incremental computation. Nevertheless,sequential execution of the scheme is still too slow for highspeedstream processing. Therefore, we are motivated todesign a parallel approach for dynamic PPR computation.First, as updates always come in batches, we devise a batchprocessing method to reduce synchronization cost among everysingle update and enable more parallelism for iterativeparallel execution. Our theoretical analysis shows that theparallel approach has the same asymptotic complexity asthe sequential approach. Second, we devise novel optimizationtechniques to e↵ectively reduce runtime overheads forparallel processes. Experimental evaluation shows that ourparallel algorithm can achieve orders of magnitude speedupson GPUs and multi-core CPUs compared with the state-ofthe-artsequential algorithm.

Discipline

Graphics and Human Computer Interfaces

Publication

Proceedings of the 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil, 2017 September 1

Volume

11

Issue

1

First Page

93

Last Page

106

ISBN

2150-8097

Identifier

10.14778/3151113.3151121

City or Country

Rio de Janeiro, Brazil

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.14778/3151113.3151121

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