Abstract
We present the design and evaluation of a Datalog engine for execution in Graphics Processing Units (GPUs). The engine evaluates recursive and non-recursive Datalog queries using a bottom-up approach based on typical relational operators. It includes a memory management scheme that automatically swaps data between memory in the host platform (a multicore) and memory in the GPU in order to reduce the number of memory transfers. To evaluate the performance of the engine, four Datalog queries were run on the engine and on a single CPU in the multicore host. One query runs up to 200 times faster on the (GPU) engine than on the CPU.
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Acknowledgments
CMA thanks the support during his MSc studies from: the University of Porto, the Centre for Research and Postgraduate Studies of the National Polytechnic Institute (CINVESTAV-IPN) of Mexico, and the Mexican Council of Science and Technology (CONACyT). ICD and VSC were partially supported by: the European Regional Development Fund (ERDF), COMPETE Programme; the Portuguese Foundation for Science and Technology (FCT), project LEAP FCOMP-01-0124-FEDER-015008; and project ABLe PTDC/EEI-SII/2094/2012.
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Martínez-Angeles, C.A., Dutra, I., Costa, V.S., Buenabad-Chávez, J. (2014). A Datalog Engine for GPUs. In: Hanus, M., Rocha, R. (eds) Declarative Programming and Knowledge Management. INAP WLP WFLP 2013 2013 2013. Lecture Notes in Computer Science(), vol 8439. Springer, Cham. https://doi.org/10.1007/978-3-319-08909-6_10
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DOI: https://doi.org/10.1007/978-3-319-08909-6_10
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