{"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/epirust-towards-a-framework-for-large-scale","title":"EpiRust: Towards A Framework For Large-scale Agent-based Epidemiological Simulations Using Rust Language","arxiv_id":null,"date":"2020-09-22","proceeding":"SIMS Conference on Simulation and Modeling 2020 9","authors":["Jayanta Kshirsagar","Akshay Dewan","Harshal Ganpatrao Hayatnagarkar"],"abstract":"To implement large-scale agent-based simulations, developers historically relied on C and C++ due to performance, while struggling to deal with tedious explicit memory management. This struggle translates into software defects and lower developer productivity. More recently, desire to harness multi-core systems via concurrent software complicates design and implementation when memory is shared among compute cores. When we faced this situation , we were looking for a system programming language as fast as C and C++ but without caveats around memory management. Between Go and Rust, we chose Rust language which guarantees safety in memory management even for concurrency, without a run-time or garbage collector. In this paper, we have shared our experience with Rust to build a framework named as EpiRust for agent-based epidemiological simulations. Our simulation experiments have shown some promising results for a population of a few million agents, using commodity-class hardware. Key outcomes of this whole exercise are that EpiRust could achieve following quality goals: 1. Robustness 2. Flexibility 3. Performance.","url_abs":"https://ep.liu.se/en/conference-article.aspx?series=ecp&issue=176&Article_No=67","url_pdf":"http://www.ep.liu.se/ecp/176/067/SIMS2020_article_ecp20176475.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":"epirust-towards-a-framework-for-large-scale","repo_url":"https://github.com/thoughtworks/epirust","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}