  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).
