Parallel materialisation of datalog programs in centralised, main-memory RDF systems
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,...
| Principais autores: | , , , , |
|---|---|
| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
Association for the Advancement of Artificial intelligence
2014
|
| Assuntos: |
| Resumo: | 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. |
|---|