Parallel-FST: A feature selection library for multicore clusters

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Parallel-FST: A feature selection library for multicore clustersDate
2022-11Citation
B. Beceiro, J. González-Domínguez & J. Touriño, "Parallel-FST: A feature selection library for multicore clusters", Journal of Parallel and Distributed Computing, 169, 2022, pp. 106-116. doi: 10.1016/j.jpdc.2022.06.012
Abstract
[Abstract]: Feature selection is a subfield of machine learning focused on reducing the dimensionality of datasets by performing a computationally intensive process. This work presents Parallel-FST, a publicly available parallel library for feature selection that includes seven methods which follow a hybrid MPI/multithreaded approach to reduce their runtime when executed on high performance computing systems. Performance tests were carried out on a 256-core cluster, where Parallel-FST obtained speedups of up to 229x for representative datasets and it was able to analyze a 512 GB dataset, which was not previously possible with a sequential counterpart library due to memory constraints.
Keywords
Feature selection
Mutual information
MPI
HyperThreading
High performance computing
Mutual information
MPI
HyperThreading
High performance computing
Description
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
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Atribución-NoComercial-SinDerivadas 3.0 España