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

Jiaming Song, Xiaowang Zhang, Peng Peng, Zhiyong Feng, Lei Zou

Abstract: In this paper, we present a plugin-based framework (MapSQ) with three parts for SPARQL queries utilizing high-performance of GPU to accelerate answering in a convenient way. Selector chooses suitable join order according to characteristics of data and queries. Executor answers subqueries and returns intermediate solutions and GPU Computing obtains the join result of intermediate solutions through MapReduce. Finally, we evaluate MapSQ bulit on gStore and RDF-3X on the LUBM benchmark and YAGO datasets (over 200 million triples). The experimental results show that MapSQ significantly improves the performance of SPARQL query engines with speedup up to 33.