Parallelizing pruned landmark labeling: Dealing with dependencies in graph algorithms

Ruoming Jin, Zhen Peng, Wendell Wu, Feodor Dragan, Gagan Agrawal, Bin Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To help compute shortest path distances over large graphs efficiently, 2-hop labeling has emerged as a major tool, with Pruned Landmark Labeling (PPL) as a popular algorithm. This paper demonstrates the first scalable parallel implementation of the PPL algorithm that produces the same results as the sequential algorithm. Based on theoretical analysis, we show how computations on each vertex can be performed in parallel while maintaining correctness, resulting in the Vertex-Centrix PLL (VC-PLL) algorithm. We also show a formulation of this algorithm based on linear algebra and argue why the use of a library based on linear algebra operations will not produce an efficient implementation. Next, we introduce a batched VC-PLL (BVC-PLL) algorithm to reduce the computational inefficiency in VC-PLL. We have carried out a parallel implementation of this method for modern clusters, combining shared memory and distributed memory parallelism, that can efficiently execute on graphs with more than a billion edges. We also demonstrate how BVC-PLL algorithm can be extended to handle directed graphs and weighted graphs and how the version for weighted graphs can benefit from SIMD parallelization.

Original languageEnglish (US)
Title of host publicationProceedings of the 34th ACM International Conference on Supercomputing, ICS 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450379830
DOIs
StatePublished - Jun 29 2020
Event34th ACM International Conference on Supercomputing, ICS 2020 - Barcelona, Spain
Duration: Jun 29 2020Jul 2 2020

Publication series

NameProceedings of the International Conference on Supercomputing

Conference

Conference34th ACM International Conference on Supercomputing, ICS 2020
CountrySpain
CityBarcelona
Period6/29/207/2/20

Keywords

  • dependency resolving
  • multi-level parallelization
  • parallel graph algorithms

ASJC Scopus subject areas

  • Computer Science(all)

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