cuSTINGER: Supporting dynamic graph algorithms for GPUs

Oded Green, David A. Bader

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

45 Scopus citations

Abstract

cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed for streaming graphs that evolve over time. cuSTINGER enables algorithm designers greater productivity and efficiency for implementing GPU-based analytics, relieving programmers of managing memory and data placement. In comparison with static graph data structures, which may require transferring the entire graph back and forth between the device and the host memories for each update or require reconstruction on the device, cuSTINGER only requires transferring the updates themselves; reducing the total amount of data transferred. cuSTINGER gives users the flexibility, based on application needs, to update the graph one edge at a time or through batch updates. cuSTINGER supports extremely high update rates, over 1 million updates per second for mid-size batched with 10k updates and 10 million updates per second for large batches with millions of updates.

Original languageEnglish (US)
Title of host publication2016 IEEE High Performance Extreme Computing Conference, HPEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509035250
DOIs
StatePublished - Nov 28 2016
Externally publishedYes
Event2016 IEEE High Performance Extreme Computing Conference, HPEC 2016 - Waltham, United States
Duration: Sep 13 2016Sep 15 2016

Publication series

Name2016 IEEE High Performance Extreme Computing Conference, HPEC 2016

Conference

Conference2016 IEEE High Performance Extreme Computing Conference, HPEC 2016
Country/TerritoryUnited States
CityWaltham
Period9/13/169/15/16

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Hardware and Architecture
  • Computational Mathematics

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