Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems
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.
Cite
Text
Motik et al. "Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8730Markdown
[Motik et al. "Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/motik2014aaai-parallel/) doi:10.1609/AAAI.V28I1.8730BibTeX
@inproceedings{motik2014aaai-parallel,
title = {{Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems}},
author = {Motik, Boris and Nenov, Yavor and Piro, Robert and Horrocks, Ian and Olteanu, Dan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {129-137},
doi = {10.1609/AAAI.V28I1.8730},
url = {https://mlanthology.org/aaai/2014/motik2014aaai-parallel/}
}