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

TL;DR

MapSQ is a GPU-accelerated MapReduce framework for SPARQL queries on large RDF datasets, achieving up to 50% speedup over existing engines by combining parallel join algorithms and CPU-GPU co-processing.

Contribution

The paper introduces a novel MapReduce-based GPU framework for SPARQL query evaluation, integrating a parallel join algorithm and CPU-GPU co-processing strategy.

Findings

MapSQ achieves up to 50% speedup over gStore and gStoreD.

The framework effectively handles large-scale RDF datasets.

Experimental results validate the efficiency of the proposed approach.

Abstract

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).