LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms

Alexandru Iosup, Tim Hegeman, Wing Lung Ngai, Stijn Heldens, Arnau Prat-Pérez, Thomas Manhardto, Hassan Chafio, Mihai Capotă, Narayanan Sundaram, More Authors

Research output: Contribution to journalArticleScientificpeer-review

64 Citations (Scopus)
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Abstract

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.
Original languageEnglish
Pages (from-to)1317-1328
Number of pages12
JournalProceedings of the VLDB Endowment
Volume9
Issue number13
DOIs
Publication statusPublished - 2016
EventVLDB 2016: 42th International Conference on Very Large Data Bases - New Delhi, India
Duration: 5 Sep 20169 Sep 2016

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