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,...
| প্রধান লেখক: | , , , , |
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| বিন্যাস: | Conference item |
| ভাষা: | English |
| প্রকাশিত: |
Association for the Advancement of Artificial intelligence
2014
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| বিষয়গুলি: |
| সংক্ষিপ্ত: | 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. |
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