{"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/cyclades-conflict-free-asynchronous-machine","title":"CYCLADES: Conflict-free Asynchronous Machine Learning","arxiv_id":"1605.09721","date":"2016-05-31","proceeding":"NeurIPS 2016 12","authors":["Xinghao Pan","Maximilian Lam","Stephen Tu","Dimitris Papailiopoulos","Ce Zhang","Michael. I. Jordan","Kannan Ramchandran","Chris Re","Benjamin Recht"],"abstract":"We present CYCLADES, a general framework for parallelizing stochastic\noptimization algorithms in a shared memory setting. CYCLADES is asynchronous\nduring shared model updates, and requires no memory locking mechanisms, similar\nto HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts\nduring the parallel execution, and offers a black-box analysis for provable\nspeedups across a large family of algorithms. Due to its inherent conflict-free\nnature and cache locality, our multi-core implementation of CYCLADES\nconsistently outperforms HOGWILD!-type algorithms on sufficiently sparse\ndatasets, leading to up to 40% speedup gains compared to the HOGWILD!\nimplementation of SGD, and up to 5x gains over asynchronous implementations of\nvariance reduction algorithms.","url_abs":"http://arxiv.org/abs/1605.09721v1","url_pdf":"http://arxiv.org/pdf/1605.09721v1.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":"cyclades-conflict-free-asynchronous-machine","repo_url":"https://github.com/amplab/cyclades","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":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[{"method_slug":"sgd","method_name":"SGD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}