Fast Spectral Graph Layout on Multicore Platforms

Ashirbad Mishra, Shad Kirmani, Kamesh Madduri

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

Abstract

We present ParHDE, a shared-memory parallelization of the High-Dimensional Embedding (HDE) graph algorithm. Originally proposed as a graph drawing algorithm, HDE characterizes the global structure of a graph and is closely related to spectral graph computations such as computing the eigenvectors of the graph Laplacian. We identify compute- and memory-intensive steps in HDE and parallelize these steps for efficient execution on shared-memory multicore platforms. ParHDE can process graphs with billions of edges in minutes, is up to 18 × faster than a prior parallel implementation of HDE, and achieves up to a 24 × relative speedup on a 28-core system. We also implement several extensions of ParHDE and demonstrate its utility in diverse graph computation-related applications.

Original languageEnglish (US)
Title of host publicationProceedings of the 49th International Conference on Parallel Processing, ICPP 2020
PublisherICST
ISBN (Electronic)9781450388160
DOIs
StatePublished - Aug 17 2020
Event49th International Conference on Parallel Processing, ICPP 2020 - Virtual, Online, Canada
Duration: Aug 17 2020Aug 20 2020

Publication series

NamePervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (Print)2153-1633

Conference

Conference49th International Conference on Parallel Processing, ICPP 2020
Country/TerritoryCanada
CityVirtual, Online
Period8/17/208/20/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications
  • Health Informatics

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