A formally based parallelization of data mining algorithms for multi-core systems.

Autor:	Kholod, Ivan, Shorov, Andrey, Titkov, Evgenii, Gorlatch, Sergei
Předmět:	DATA mining PARALLEL programming NAIVE Bayes classification CLASSIFICATION algorithms MULTICORE processors ALGORITHMS PROGRAM transformation
Zdroj:	Journal of Supercomputing; Dec2019, Vol. 75 Issue 12, p7909-7920, 12p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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