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Efficient execution of SPARQL queries over large RDF datasets is a topic ofconsiderable interest due to increased use of RDF to encode data. Most of thiswork has followed either relational or graph-based approaches. In this paper,we propose an alternative query engine, called gSmart, based on matrix algebra.This approach can potentially better exploit the computing power ofhigh-performance heterogeneous architectures that we target. gSmartincorporates: (1) grouped incident edge-based SPARQL query evaluation, in whichall unevaluated edges of a vertex are evaluated together using a series ofmatrix operations to fully utilize query constraints and narrow down thesolution space; (2) a graph query planner that determines the order in whichvertices in query graphs should be evaluated; (3) memory- andcomputation-efficient data structures including the light-weight sparse matrix(LSpM) storage for RDF data and the tree-based representation for evaluationresults; (4) a multi-stage data partitioner to map the incident edge-basedquery evaluation into heterogeneous HPC architectures and develop multi-levelparallelism; and (5) a parallel executor that uses the fine-grained processingscheme, pre-pruning technique, and tree-pruning technique to lower inter-nodecommunication and enable high throughput. Evaluations of gSmart on a CPU+GPUHPC architecture show execution time speedups of up to 46920.00x compared tothe existing SPARQL query engines on a single node machine. Additionally,gSmart on the Tianhe-1A supercomputer achieves a maximum speedup of 6.90xscaling from 2 to 16 CPU+GPU nodes.

GSmart: An Efficient SPARQL Query Engine Using Sparse Matrix Algebra -- Full Version