TY - GEN

T1 - A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries

AU - Jamour, Fuad Tarek

AU - Abdelaziz, Ibrahim

AU - Kalnis, Panos

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2018/9/10

Y1 - 2018/9/10

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/631495

UR - https://dl.acm.org/citation.cfm?doid=3229863.3275576

UR - http://www.scopus.com/inward/record.url?scp=85058881285&partnerID=8YFLogxK

U2 - 10.14778/3229863.3236239

DO - 10.14778/3229863.3236239

M3 - Conference contribution

SP - 1978

EP - 1981

BT - Proceedings of the VLDB Endowment

PB - VLDB Endowment

ER -