A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
InfoCloud Research Group
Date
2018-09-10Online Publication Date
2018-09-10Print Publication Date
2018-08-01Permanent link to this record
http://hdl.handle.net/10754/631495
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Show full item recordAbstract
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.Citation
Jamour F, Abdelaziz I, Kalnis P (2018) A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries. Proceedings of the VLDB Endowment 11: 1978–1981. Available: http://dx.doi.org/10.14778/3229863.3236239.Publisher
VLDB EndowmentConference/Event name
44th International Conference on Very Large Data Bases, VLDB 2018Additional Links
https://dl.acm.org/citation.cfm?doid=3229863.3275576ae974a485f413a2113503eed53cd6c53
10.14778/3229863.3236239
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