Using RAPIDS AI to Accelerate Graph Data Science Workflows

Todd Hricik, David Bader, Oded Green

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

9 Scopus citations


Scale free networks are abundant in many natural, social, and engineering phenomena for which there exists a substantial corpus of theory able to elucidate many of their underlying properties. In this paper we study the scalability of some widely available Python-based tools for the empirical investigation of scale free network data in a typical early stage analysis pipeline. We demonstrate how porting serial implementations of commonly used pipeline data structures and methods to parallel hardware via the NVIDIA RAPIDS AI API requires minimal rewriting of code. As a utility for each pipeline we recorded the time required to complete the analysis for both the serial and parallelized workflows on a task-wise basis. Furthermore, we review a statistically based methodology for fitting a power-law to empirical data. Maximum likelihood estimations for scale were inferred after using Kolmogorov-Smirnov based methods to determine location estimates. Our serial implementation of a typical early stage network analysis workflow uses a combination of widely used data structures and algorithms provided by the NumPy, Pandas and NetworkX frameworks. We then parallelized our workflow using the APIs provided by NVIDIA's RAPIDS AI open data science libraries and measured the relative time to completion for the tasks of ingesting raw data, creating a graph representation of the data and finally fitting a power-law distribution to the empirical observations. The results of our experiments, run on graphs ranging in size from 1 million to 20 million edges, demonstrate that significantly less time is required to complete the tasks of generating a graph from an edge list, computing the degree of all nodes in the graph and fitting the scale and location parameters to the observed data.

Original languageEnglish (US)
Title of host publication2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192192
StatePublished - Sep 22 2020
Externally publishedYes
Event2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States
Duration: Sep 21 2020Sep 25 2020

Publication series

Name2020 IEEE High Performance Extreme Computing Conference, HPEC 2020


Conference2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Country/TerritoryUnited States
CityVirtual, Waltham

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture


  • GPU computing
  • data science
  • graph analytics


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