{"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/parallelizing-over-artificial-neural-network","title":"Parallelizing Over Artificial Neural Network Training Runs with Multigrid","arxiv_id":"1708.02276","date":"2017-08-07","proceeding":null,"authors":["Jacob B. Schroder"],"abstract":"Artificial neural networks are a popular and effective machine learning\ntechnique. Great progress has been made parallelizing the expensive training\nphase of an individual network, leading to highly specialized pieces of\nhardware, many based on GPU-type architectures, and more concurrent algorithms\nsuch as synthetic gradients. However, the training phase continues to be a\nbottleneck, where the training data must be processed serially over thousands\nof individual training runs. This work considers a multigrid reduction in time\n(MGRIT) algorithm that is able to parallelize over the thousands of training\nruns and converge to the exact same solution as traditional training would\nprovide. MGRIT was originally developed to provide parallelism for time\nevolution problems that serially step through a finite number of time-steps.\nThis work recasts the training of a neural network similarly, treating neural\nnetwork training as an evolution equation that evolves the network weights from\none step to the next. Thus, this work concerns distributed computing approaches\nfor neural networks, but is distinct from other approaches which seek to\nparallelize only over individual training runs. The work concludes with\nsupporting numerical results for two model problems.","url_abs":"http://arxiv.org/abs/1708.02276v2","url_pdf":"http://arxiv.org/pdf/1708.02276v2.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":"parallelizing-over-artificial-neural-network","repo_url":"https://github.com/cunialino/PACS-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.02276","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}