{"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/a-reliable-effective-terascale-linear","title":"A Reliable Effective Terascale Linear Learning System","arxiv_id":"1110.4198","date":"2011-10-19","proceeding":null,"authors":["Alekh Agarwal","Olivier Chapelle","Miroslav Dudik","John Langford"],"abstract":"We present a system and a set of techniques for learning linear predictors\nwith convex losses on terascale datasets, with trillions of features, {The\nnumber of features here refers to the number of non-zero entries in the data\nmatrix.} billions of training examples and millions of parameters in an hour\nusing a cluster of 1000 machines. Individually none of the component techniques\nare new, but the careful synthesis required to obtain an efficient\nimplementation is. The result is, up to our knowledge, the most scalable and\nefficient linear learning system reported in the literature (as of 2011 when\nour experiments were conducted). We describe and thoroughly evaluate the\ncomponents of the system, showing the importance of the various design choices.","url_abs":"http://arxiv.org/abs/1110.4198v3","url_pdf":"http://arxiv.org/pdf/1110.4198v3.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":"a-reliable-effective-terascale-linear","repo_url":"https://github.com/VowpalWabbit/neurips2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-reliable-effective-terascale-linear","repo_url":"https://github.com/vijaysharmapc/BTRE_PROJECT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1110.4198","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}