{"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/graphlab-a-new-framework-for-parallel-machine","title":"GraphLab: A New Framework for Parallel Machine Learning","arxiv_id":"1006.4990","date":"2010-06-25","proceeding":null,"authors":["Yucheng Low","Joseph Gonzalez","Aapo Kyrola","Danny Bickson","Carlos Guestrin","Joseph M. Hellerstein"],"abstract":"Designing and implementing efficient, provably correct parallel machine\nlearning (ML) algorithms is challenging. Existing high-level parallel\nabstractions like MapReduce are insufficiently expressive while low-level tools\nlike MPI and Pthreads leave ML experts repeatedly solving the same design\nchallenges. By targeting common patterns in ML, we developed GraphLab, which\nimproves upon abstractions like MapReduce by compactly expressing asynchronous\niterative algorithms with sparse computational dependencies while ensuring data\nconsistency and achieving a high degree of parallel performance. We demonstrate\nthe expressiveness of the GraphLab framework by designing and implementing\nparallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and\nCompressed Sensing. We show that using GraphLab we can achieve excellent\nparallel performance on large scale real-world problems.","url_abs":"http://arxiv.org/abs/1006.4990v1","url_pdf":"http://arxiv.org/pdf/1006.4990v1.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":"graphlab-a-new-framework-for-parallel-machine","repo_url":"https://github.com/graphlab-code/graphlab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"graphlab-a-new-framework-for-parallel-machine","repo_url":"https://github.com/jegonzal/PowerGraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}