{"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/a-parallel-fortran-framework-for-neural","title":"A parallel Fortran framework for neural networks and deep learning","arxiv_id":"1902.06714","date":"2019-02-18","proceeding":null,"authors":["Milan Curcic"],"abstract":"This paper describes neural-fortran, a parallel Fortran framework for neural\nnetworks and deep learning. It features a simple interface to construct\nfeed-forward neural networks of arbitrary structure and size, several\nactivation functions, and stochastic gradient descent as the default\noptimization algorithm. Neural-fortran also leverages the Fortran 2018 standard\ncollective subroutines to achieve data-based parallelism on shared- or\ndistributed-memory machines. First, I describe the implementation of neural\nnetworks with Fortran derived types, whole-array arithmetic, and collective sum\nand broadcast operations to achieve parallelism. Second, I demonstrate the use\nof neural-fortran in an example of recognizing hand-written digits from images.\nFinally, I evaluate the computational performance in both serial and parallel\nmodes. Ease of use and computational performance are similar to an existing\npopular machine learning framework, making neural-fortran a viable candidate\nfor further development and use in production.","url_abs":"http://arxiv.org/abs/1902.06714v2","url_pdf":"http://arxiv.org/pdf/1902.06714v2.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":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/milancurcic/neural-fortran-paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/modern-fortran/neural-fortran","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/INOS-soft/AI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/INOS-soft/Bro-coil","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/INOS-soft/KuE.electo-waffle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-parallel-fortran-framework-for-neural","repo_url":"https://github.com/INOS-soft/Python-Tools","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}