{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-performance-computing-applied-to","title":"High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison","arxiv_id":"2308.10037","date":"2023-08-19","proceeding":null,"authors":["Nechba Mohammed","Mouhajir Mohamed","Sedjari Yassine"],"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","url_abs":"https://arxiv.org/abs/2308.10037v1","url_pdf":"https://arxiv.org/pdf/2308.10037v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-performance-computing-applied-to","repo_url":"https://github.com/nechbamohammed/swiftlogisticreg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":null,"task_name":"CPU"},{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"spam-detection","task_name":"Spam detection"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":null,"method_name":"Library"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}