Abstract
We describe a novel, systematic approach to efficiently parallelizing data mining algorithms: starting with the representation of an algorithm as a sequential composition of functions, we formally transform it into a parallel form using higher-order functions for specifying parallelism. We implement the approach as an extension of the industrial-strength Java-based library Xelopes, and we illustrate its use by developing a multi-threaded Java program for the popular naive Bayes classification algorithm. In comparison with the popular MapReduce programming model, our resulting programs enable not only data-parallel, but also task-parallel implementation and a combination of both. Our experiments demonstrate an efficient parallelization and good scalability on multi-core processors.
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Acknowledgements
This work was supported by the Ministry of Education and Science of the Russian Federation in the framework of the state order “Organization of Scientific Research,” task 2.6113.2017/6.7, and by the German Ministry of Education and Research (BMBF) in the framework of the HPC2SE project at the University of Muenster.
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Kholod, I., Shorov, A., Titkov, E. et al. A formally based parallelization of data mining algorithms for multi-core systems. J Supercomput 75, 7909–7920 (2019). https://doi.org/10.1007/s11227-018-2473-8
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DOI: https://doi.org/10.1007/s11227-018-2473-8