Accelerating keyword search for big RDF web data on many-core systems

Abstract

Resource Description Framework (RDF) is the commonly used format for Semantic Web data. Nowadays, huge amounts of data on the Internet in the RDF format are used by search engines for providing answers to the queries of users. Querying through big data needs suitable searching methods supported by a very high processing power, because the traditional, sequential keyword matching on a semantic web server may take a prohibitively long time. In this paper, we aim at accelerating the search in big RDF data by exploiting modern many-core architectures based on Graphics Processing Units (GPUs). We develop several implementations of the RDF search for many-core architectures using two programming approaches: OpenMP for systems with CPUs and CUDA for systems comprising CPUs and GPUs. Experiments show that our approach is 20.5 times faster than the sequential search.