A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries

Fuad Tarek Jamour, Ibrahim Abdelaziz, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Existing RDF engines follow one of two design paradigms: relational or graph-based. Such engines are typically designed for specific hardware architectures, mainly CPUs, and are not easily portable to new architectures. Porting an existing engine to a different architecture (e.g., many-core architectures) entails almost redesign from scratch. We explore sparse matrix algebra as a third paradigm for designing a portable, scalable, and efficient RDF engine. We demonstrate MAGiQ; a matrix algebra approach for evaluating complex SPARQL queries over large RDF datasets. MAGiQ represents an RDF graph as a sparse matrix, and translates SPARQL queries to matrix algebra programs. MAGiQ takes advantage of the existing rich software infrastructure for processing sparse matrices, optimized for many architectures (e.g., CPUs, GPUs, distributed), effortlessly. This demo motivates the adoption of matrix algebra in RDF graph processing by showing MAGiQ's performance with different matrix algebra backend engines. MAGiQ, using a GPU, is orders of magnitude faster in solving complex queries on a billion edge graph than state-of-the-art RDF systems.
Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherVLDB Endowment
Number of pages4
StatePublished - Sep 10 2018


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