# PyExaFMM: an exercise in designing high-performance software with Python and Numba

arXiv: https://arxiv.org/abs/2303.08394 · HTML: https://mlsanity.com/paper/2303.08394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)

## Abstract (arXiv)

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.