Anti-Section Transitive Closure

Oded Green, Zhihui Du, Sanyamee Patel, Zehui Xie, Hang Liu, David A. Bader

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

4 Scopus citations

Abstract

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requires a large memory footprint as the output is typically larger than the input. Some of the original research on transitive closures assumed that graphs were dense and used dense adjacency matrices. We have since learned that many real-world networks are extremely sparse, and the existing methods do not scale. In this work, we introduce a new algorithm called Anti-section Transitive Closure (ATC) for finding the transitive closure of a graph. We present a new parallel edges operation - anti-sections - for finding new edges to reachable vertices. ATC scales to massively multi-threaded systems such as NVIDIA's GPU with tens of thousands of threads. We show that the anti-section operation shares some traits with the triangle counting intersection operation in graph analysis. Lastly, we view the transitive closure problem as a dynamic graph problem requiring edge insertions. By doing this, our memory footprint is smaller. We also show a method for creating the batches in parallel using two different techniques: dual-round and hash. Using these techniques and the Hornet dynamic graph data structure, we show our new algorithm on an NVIDIA Titan V GPU. We compare with other packages such as NetworkX, SEI-GBTL, SuiteSparse, and cuSparse.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics, HiPC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-201
Number of pages10
ISBN (Electronic)9781665410168
DOIs
StatePublished - 2021
Event28th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2021 - Virtual, Bangalore, India
Duration: Dec 17 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics, HiPC 2021

Conference

Conference28th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2021
Country/TerritoryIndia
CityVirtual, Bangalore
Period12/17/2112/18/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems

Keywords

  • Dynamic graph
  • GPU
  • Parallel graph algorithm
  • Transitive closure

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