TripleID-Q: RDF Query Processing Framework using GPU

TL;DR

This paper introduces TripleID-Q, a GPU-based framework for efficient RDF query processing that significantly accelerates query execution by utilizing a compact data representation and parallel algorithms.

Contribution

The work presents a novel GPU-optimized framework with a compact RDF data representation and parallel algorithms for faster query processing, outperforming traditional RDF tools.

Findings

TripleID representation reduces data size 3-4 times.

GPU query processing is up to 108 times faster than traditional tools.

Complex queries with unions and joins are over 1,000 times faster.

Abstract

Resource Description Framework (RDF) data represents information linkage around the Internet. It uses Inter- nationalized Resources Identifier (IRI) which can be referred to external information. Typically, an RDF data is serialized as a large text file which contains millions of relationships. In this work, we propose a framework based on TripleID-Q, for query processing of large RDF data in a GPU. The key elements of the framework are 1) a compact representation suitable for a Graphics Processing Unit (GPU) and 2) its simple representation conversion method which optimizes the preprocessing overhead. Together with the framework, we propose parallel algorithms which utilize thousands of GPU threads to look for specific data for a given query as well as to perform basic query operations such as union, join, and filter. The TripleID representation is smaller than the original representation 3-4 times. Querying from TripleID using a GPU is up to 108 times faster than using the traditional RDF tool. The speedup can be more than 1,000 times over the traditional RDF store when processing a complex query with union and join of many subqueries.