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Parallel-FST: A feature selection library for multicore clusters
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DOI：10.1016/j.jpdc.2022.06.012.png)
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Abstract
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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. (C) 2022 The Author(s). Published by Elsevier Inc.
Keywords:
Feature selection
Mutual information
MPI
Hyper Threading
High performance computing
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