Accelerating semantic graph databases on commodity clusters
We are developing a full software system for accelerating semantic graph databases on commodity cluster that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to C++ compiler, a library of parallel graph methods and a custom multithreaded runtime layer, which provides a Partitioned Global Address Space (PGAS) programming model with fork/join parallelism and automatic load balancing over a commodity clusters. We present preliminary results for the compiler and for the runtime.
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- Conference: IEEE International Conference on Big Data (Big Data 2013), October 6-9, 2013, Silicon Valley, California, 768-772
- Institute of Electrical and Electronics Engineers , Piscataway, NJ, United States(US).
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- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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- Country of Publication:
- United States
- SGEM; SGLib; GMT; Semantic graph databases; commodity clusters; multithreading; data aggreggation