{"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/rankmap-a-platform-aware-framework-for","title":"RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets","arxiv_id":"1503.08169","date":"2015-03-27","proceeding":null,"authors":["Azalia Mirhoseini","Eva L. Dyer","Ebrahim. M. Songhori","Richard G. Baraniuk","Farinaz Koushanfar"],"abstract":"This paper introduces RankMap, a platform-aware end-to-end framework for\nefficient execution of a broad class of iterative learning algorithms for\nmassive and dense datasets. Our framework exploits data structure to factorize\nit into an ensemble of lower rank subspaces. The factorization creates sparse\nlow-dimensional representations of the data, a property which is leveraged to\ndevise effective mapping and scheduling of iterative learning algorithms on the\ndistributed computing machines. We provide two APIs, one matrix-based and one\ngraph-based, which facilitate automated adoption of the framework for\nperforming several contemporary learning applications. To demonstrate the\nutility of RankMap, we solve sparse recovery and power iteration problems on\nvarious real-world datasets with up to 1.8 billion non-zeros. Our evaluations\nare performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores.\nThe results demonstrate up to two orders of magnitude improvements in memory\nusage, execution speed, and bandwidth compared with the best reported prior\nwork, while achieving the same level of learning accuracy.","url_abs":"http://arxiv.org/abs/1503.08169v2","url_pdf":"http://arxiv.org/pdf/1503.08169v2.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":"rankmap-a-platform-aware-framework-for","repo_url":"https://github.com/azalia/RankMap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}