CUDA acceleration of MI-based feature selection methods

Abstract

Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of subsequent analyses. The problem with current feature selection approaches is that they are computationally expensive when processing large datasets. This work presents parallel implementations for Nvidia GPUs of three highly-used feature selection methods based on the Mutual Information (MI) metric: mRMR, JMI and DISR. Publicly available code includes not only CUDA implementations of the general methods, but also an adaptation of them to work with low-precision fixed point in order to further increase their performance on GPUs. The experimental evaluation was carried out on two modern Nvidia GPUs (Turing T4 and Ampere A100) with highly satisfactory results, achieving speedups of up to 283x when compared to state-of-the-art C implementations.