Performance-portable graph coarsening for efficient multilevel graph analysis

Michael S. Gilbert, Seher Acer, Erik G. Boman, Kamesh Madduri, Sivasankaran Rajamanickam

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

Abstract

The multilevel heuristic is an effective strategy for speeding up graph analytics, and graph coarsening is an integral step of multilevel methods. We perform a comprehensive study of multilevel coarsening in this work. We primarily focus on the graphics processing unit (GPU) parallelization of the Heavy Edge Coarsening (HEC) method executed in an iterative setting. We present optimizations for the two phases of coarsening, a fine-to-coarse vertex mapping phase, and a coarse graph construction phase. We also express several other coarsening algorithms using the Kokkos framework and discuss their parallelization. We demonstrate the efficacy of parallelized HEC on an NVIDIA Turing GPU and a 32-core AMD Ryzen processor using multilevel spectral graph partitioning as the primary case study.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-222
Number of pages10
ISBN (Electronic)9781665440660
DOIs
StatePublished - May 2021
Event35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021 - Virtual, Online
Duration: May 17 2021May 21 2021

Publication series

NameProceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021

Conference

Conference35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021
CityVirtual, Online
Period5/17/215/21/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

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