{"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/development-of-mc-dc-a-performant-scalable","title":"Development of MC/DC: a performant, scalable, and portable Python-based Monte Carlo neutron transport code","arxiv_id":"2305.07636","date":"2023-05-12","proceeding":null,"authors":["Ilham Variansyah","J. P. Morgan","Jordan Northrop","Kyle E. Niemeyer","Ryan G. McClarren"],"abstract":"We discuss the current development of MC/DC (Monte Carlo Dynamic Code). MC/DC is primarily designed to serve as an exploratory Python-based MC transport code. However, it seeks to offer improved performance, massive scalability, and backend portability by leveraging Python code-generation libraries and implementing an innovative abstraction strategy and compilation scheme. Here, we verify MC/DC capabilities and perform an initial performance assessment. We found that MC/DC can run hundreds of times faster than its pure Python mode and about 2.5 times slower, but with comparable parallel scaling, than the high-performance MC code Shift for simple problems. Finally, to further exercise MC/DC's time-dependent MC transport capabilities, we propose a challenge problem based on the C5G7-TD benchmark model.","url_abs":"https://arxiv.org/abs/2305.07636v1","url_pdf":"https://arxiv.org/pdf/2305.07636v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"development-of-mc-dc-a-performant-scalable","repo_url":"https://github.com/jpmorgan98/MCDC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"development-of-mc-dc-a-performant-scalable","repo_url":"https://github.com/cement-psaap/mcdc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"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}