How Well Do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis

Yong Guo, Marcin Biczak, Ana Lucia Varbanescu, Alexandru Iosup, Claudio Martella, Theodore L. Willke

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Graph-processing platforms are increasingly used in a variety of domains. Although both industry and academia are developing and tuning graph-processing algorithms and platforms, the performance of graph-processing platforms has never been explored or compared in-depth. Thus, users face the daunting challenge of selecting an appropriate platform for their specific application. To alleviate this challenge, we propose an empirical method for benchmarking graph-processing platforms. We define a comprehensive process, and a selection of representative metrics, datasets, and algorithmic classes. We implement a benchmarking suite of five classes of algorithms and seven diverse graphs. Our suite reports on basic (user-lever) performance, resource utilization, scalability, and various overhead. We use our benchmarking suite to analyze and compare six platforms. We gain valuable insights for each platform and present the first comprehensive comparison of graph-processing platforms. © 2014 IEEE.
Original languageEnglish
Title of host publication2014 IEEE 28th International Parallel and Distributed Processing Symposium, Phoenix, AZ, USA, May 19-23, 2014
PublisherACM, IEEE Computer Society
Pages395-404
Number of pages10
ISBN (Print)9780769552071
DOIs
Publication statusPublished - 2014
Event28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014 - Phoenix, United States
Duration: 19 May 201423 May 2014

Conference

Conference28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014
CountryUnited States
CityPhoenix
Period19/05/1423/05/14

Keywords

  • benchmark
  • graph processing
  • performance evaluation

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