Improving the performance of graph database queries using linear algebra operations

The application of graph databases to different domains is gaining momentum. The Resource Description Framework (RDF) is one of the data models supported by graph databases, and SPARQL is the standard query language for RDF graphs. These databases are also known as RDF triplestores. Many triplestores are implemented over the relational data model, using tables to store graphs and translating SPARQL queries into SQL queries, and this approach can lead to unnecessary overheads. On the other hand, in the context of High-Performance Computing (HPC), implementations over hybrid hardware platforms using Numerical Linear Algebra (NLA) operations have become an effective and efficient computing strategy in the last decade. In particular, Graphics Processing Units (GPUs) have been adopted to perform general-purpose computations due to their high performance, reasonable prices, and an attractive relationship between computing capacity and energy consumption. In the context described above, this paper presents an initial study on the efficient implementation of a set of SPARQL queries in terms of NLA operations. Additionally, we evaluate the performance of implementing these operations on GPUs.