{"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/gradient-coding","title":"Gradient Coding","arxiv_id":"1612.03301","date":"2016-12-10","proceeding":null,"authors":["Rashish Tandon","Qi Lei","Alexandros G. Dimakis","Nikos Karampatziakis"],"abstract":"We propose a novel coding theoretic framework for mitigating stragglers in\ndistributed learning. We show how carefully replicating data blocks and coding\nacross gradients can provide tolerance to failures and stragglers for\nSynchronous Gradient Descent. We implement our schemes in python (using MPI) to\nrun on Amazon EC2, and show how we compare against baseline approaches in\nrunning time and generalization error.","url_abs":"http://arxiv.org/abs/1612.03301v2","url_pdf":"http://arxiv.org/pdf/1612.03301v2.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":"gradient-coding","repo_url":"https://github.com/hwang595/ErasureHead","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"gradient-coding","repo_url":"https://github.com/rashisht1/gradient_coding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}