Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems

Authors

  • Boris Motik Oxford University
  • Yavor Nenov Oxford University
  • Robert Piro Oxford University
  • Ian Horrocks Oxford University
  • Dan Olteanu Oxford University

DOI:

https://doi.org/10.1609/aaai.v28i1.8730

Keywords:

datalog, materialization, multi-core, parallel algorithms

Abstract

We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, 'mostly' lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.

AAAI 2014 Proceedings Cover

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Published

2014-06-19

How to Cite

Motik, B., Nenov, Y., Piro, R., Horrocks, I., & Olteanu, D. (2014). Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8730

Issue

Vol. 28 No. 1 (2014): Twenty-Eighth AAAI Conference on Artificial Intelligence

Section

AAAI Technical Track: AI and the Web