LDBC Graphalytics
A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms
Alex Iosup (TU Delft - Data-Intensive Systems)
T.M. Hegeman (TU Delft - Data-Intensive Systems)
Wing Ngai (TU Delft - Data-Intensive Systems)
S.J. Heldens (TU Delft - Data-Intensive Systems)
A. Prat-Perez (Universitat Politecnica de Catalunya)
Thomas Manhardto (Oracle)
Hassan Chafio (Oracle)
M. Capotă (Intel Corporation)
N. Sundaram (Intel Corporation)
More Authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline