Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with Vertex Delegates

Roger Pearce, Maya Gokhale, Nancy M. Amato

Research output: Contribution to journalConference article

Abstract

At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices. We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and Page-Rank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%

Original languageEnglish (US)
Article number7013032
Pages (from-to)549-559
Number of pages11
JournalInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
Volume2015-January
Issue numberJanuary
DOIs
StatePublished - Jan 16 2014
Externally publishedYes
EventInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2014 - New Orleans, United States
Duration: Nov 16 2014Nov 21 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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