{"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/pyexafmm-an-exercise-in-designing-high","title":"PyExaFMM: an exercise in designing high-performance software with Python and Numba","arxiv_id":"2303.08394","date":"2023-03-15","proceeding":null,"authors":["Srinath Kailasa","Tingyu Wang","Lorena A. Barba","Timo Betcke"],"abstract":"Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter and that fully utilizes the resources of modern CPUs. This means support for parallel multithreading and auto vectorization if available, as with compiled languages such as C++ or Fortran. In this article we document our experience developing PyExaFMM, a multithreaded Numba implementation of the Fast Multipole Method, an algorithm with a non-linear data structure and a large amount of data organization. We find that designing performant Numba code for complex algorithms can be as challenging as writing in a compiled language.","url_abs":"https://arxiv.org/abs/2303.08394v3","url_pdf":"https://arxiv.org/pdf/2303.08394v3.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":"pyexafmm-an-exercise-in-designing-high","repo_url":"https://github.com/exafmm/pyexafmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}