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

arXiv: https://arxiv.org/abs/2308.10037 · HTML: https://mlsanity.com/paper/2308.10037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Strategies and Principles of Distributed Machine Learning on Big Data](https://mlsanity.com/paper/1512.09295/) (2015)

## Abstract (arXiv)

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