High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison

• URL: http://arxiv.org/abs/2308.10037v1
• Date: Sat, 19 Aug 2023 14:49:37 GMT
• Title: High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison
• Authors: Nechba Mohammed, Mouhajir Mohamed, Sedjari Yassine
• Abstract summary: We present a versatile GPU-based parallel version of Logistic Regression (LR) Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al. Our method is particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a versatile GPU-based parallel version of Logistic Regression (LR), aiming to address the increasing demand for faster algorithms in binary classification due to large data sets. Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al. [12]. Our experiments demonstrate that our GPU-based LR outperforms existing CPU-based implementations in terms of execution time while maintaining comparable f1 score. The significant acceleration of processing large datasets makes our method particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection. Our algorithm is implemented in a ready-to-use Python library available at : https://github.com/NechbaMohammed/SwiftLogisticReg

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