A new paper partially funded by K-Drive – “RDFS Reasoning on Massively Parallel Hardware” is published and presented on this year’s International Semantic Web Conference.
Abstract. Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for ﬁne-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can beneﬁt even more from careful optimizations that take into account intricacies of modern parallel hardware.
You can view a draft of this paper on our Publications page.