Update on k-truss Decomposition on GPU

Mohammad Almasri, Omer Anjum, Carl Pearson, Zaid Qureshi, Vikram S. Mailthody, Rakesh Nagi, Jinjun Xiong, Wen Mei Hwu

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

Abstract

In this paper, we present an update to our previous submission on k-truss decomposition from Graph Challenge 2018. For single k k-truss implementation, we propose multiple algorithmic optimizations that significantly improve performance by up to 35.2x (6.9x on average) compared to our previous GPU implementation. In addition, we present a scalable multi-GPU implementation in which each GPU handles a different 'k' value. Compared to our prior multi-GPU implementation, the proposed approach is faster by up to 151.3x (78.8x on average). In case when the edges with only maximal k-truss are sought, incrementing the 'k' value in each iteration is inefficient particularly for graphs with large maximum k-truss. Thus, we propose binary search for the 'k' value to find the maximal k-truss. The binary search approach on a single GPU is up to 101.5 (24.3x on average) faster than our 2018 k-truss submission. Lastly, we show that the proposed binary search finds the maximum k-truss for 'Twitter' graph dataset having 2.8 billion bidirectional edges in just 16 minutes on a single V100 GPU.

Original languageEnglish (US)
Title of host publication2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150208
DOIs
StatePublished - Sep 2019
Event2019 IEEE High Performance Extreme Computing Conference, HPEC 2019 - Waltham, United States
Duration: Sep 24 2019Sep 26 2019

Publication series

Name2019 IEEE High Performance Extreme Computing Conference, HPEC 2019

Conference

Conference2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
CountryUnited States
CityWaltham
Period9/24/199/26/19

Keywords

  • Binary search
  • CUDA
  • GPU
  • K-truss decomposition
  • Multi-GPU
  • Multi-node

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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  • Cite this

    Almasri, M., Anjum, O., Pearson, C., Qureshi, Z., Mailthody, V. S., Nagi, R., Xiong, J., & Hwu, W. M. (2019). Update on k-truss Decomposition on GPU. In 2019 IEEE High Performance Extreme Computing Conference, HPEC 2019 [8916285] (2019 IEEE High Performance Extreme Computing Conference, HPEC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPEC.2019.8916285