{"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/geantv-results-from-the-prototype-of","title":"GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP","arxiv_id":"2005.00949","date":"2020-05-03","proceeding":null,"authors":["G. Amadio","A. Ananya","J. Apostolakis","M. Bandieramonte","S. Banerjee","A. Bhattacharyya","C. Bianchini","G. Bitzes","P. Canal","F. Carminati","O. Chaparro-Amaro","G. Cosmo","J. C. De Fine Licht","V. Drogan","L. Duhem","D. Elvira","J. Fuentes","A. Gheata","M. Gheata","M. Gravey","I. Goulas","F. Hariri","S. Y. Jun","D. Konstantinov","H. Kumawat","J. G. Lima","A. Maldonado-Romo","J. Martínez-Castro","P. Mato","T. Nikitina","S. Novaes","M. Novak","K. Pedro","W. Pokorski","A. Ribon","R. Schmitz","R. Seghal","O. Shadura","E. Tcherniaev","S. Vallecorsa","S. Wenzel","Y. Zhang"],"abstract":"Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.","url_abs":"http://arxiv.org/abs/2005.00949v3","url_pdf":"http://arxiv.org/pdf/2005.00949v3.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":"geantv-results-from-the-prototype-of","repo_url":"https://github.com/scikit-hep/mplhep","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}