{"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/xgboost-scalable-gpu-accelerated-learning","title":"XGBoost: Scalable GPU Accelerated Learning","arxiv_id":"1806.11248","date":"2018-06-29","proceeding":null,"authors":["Rory Mitchell","Andrey Adinets","Thejaswi Rao","Eibe Frank"],"abstract":"We describe the multi-GPU gradient boosting algorithm implemented in the\nXGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast,\nscalable training on multi-GPU systems with all of the features of the XGBoost\nlibrary. We employ data compression techniques to minimise the usage of scarce\nGPU memory while still allowing highly efficient implementation. Using our\nalgorithm we show that it is possible to process 115 million training instances\nin under three minutes on a publicly available cloud computing instance. The\nalgorithm is implemented using end-to-end GPU parallelism, with prediction,\ngradient calculation, feature quantisation, decision tree construction and\nevaluation phases all computed on device.","url_abs":"http://arxiv.org/abs/1806.11248v1","url_pdf":"http://arxiv.org/pdf/1806.11248v1.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":"xgboost-scalable-gpu-accelerated-learning","repo_url":"https://github.com/dmlc/xgboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"data-compression","task_name":"Data Compression"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.11248","atlas_url":"https://app.syntology.ai/?focus=1806.11248","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}