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MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU

Jiaying Feng, Xiaowang Zhang, Zhiyong Feng
School of Computer Science and Technology,Tianjin University, Tianjin 300350, P.R.China
arXiv:1702.03484 [cs.DB], (12 Feb 2017)

@article{feng2017mapsq,

   title={MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU},

   author={Feng, Jiaying and Zhang, Xiaowang and Feng, Zhiyong},

   year={2017},

   month={feb},

   archivePrefix={"arXiv"},

   primaryClass={cs.DB}

}

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In this paper, we present a MapReduce-based framework for evaluating SPARQL queries on GPU (named MapSQ) to large-scale RDF datesets efficiently by applying both high performance. Firstly, we develop a MapReduce-based Join algorithm to handle SPARQL queries in a parallel way. Secondly, we present a coprocessing strategy to manage the process of evaluating queries where CPU is used to assigns subqueries and GPU is used to compute the join of subqueries. Finally, we implement our proposed framework and evaluate our proposal by comparing with two popular and latest SPARQL query engines gStore and gStoreD on the LUBM benchmark. The experiments demonstrate that our proposal MapSQ is highly efficient and effective (up to 50% speedup).
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