{"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-scalable-asynchronous-distributed-algorithm","title":"A Scalable Asynchronous Distributed Algorithm for Topic Modeling","arxiv_id":"1412.4986","date":"2014-12-16","proceeding":null,"authors":["Hsiang-Fu Yu","Cho-Jui Hsieh","Hyokun Yun","S. V. N. Vishwanathan","Inderjit S. Dhillon"],"abstract":"Learning meaningful topic models with massive document collections which\ncontain millions of documents and billions of tokens is challenging because of\ntwo reasons: First, one needs to deal with a large number of topics (typically\nin the order of thousands). Second, one needs a scalable and efficient way of\ndistributing the computation across multiple machines. In this paper we present\na novel algorithm F+Nomad LDA which simultaneously tackles both these problems.\nIn order to handle large number of topics we use an appropriately modified\nFenwick tree. This data structure allows us to sample from a multinomial\ndistribution over $T$ items in $O(\\log T)$ time. Moreover, when topic counts\nchange the data structure can be updated in $O(\\log T)$ time. In order to\ndistribute the computation across multiple processor we present a novel\nasynchronous framework inspired by the Nomad algorithm of\n\\cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform\nstate-of-the-art on massive problems which involve millions of documents,\nbillions of words, and thousands of topics.","url_abs":"http://arxiv.org/abs/1412.4986v1","url_pdf":"http://arxiv.org/pdf/1412.4986v1.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-scalable-asynchronous-distributed-algorithm","repo_url":"https://github.com/dmlc/experimental-lda","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}