Numba: a LLVM-based Python JIT compiler. Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. The Numba project is supported by Anaconda, Inc. (formerly known as Continuum Analytics) and The Gordon and Betty Moore Foundation (Grant GBMF5423).

References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  2. Jannik Michelfeit: multivar_horner: a python package for computing Horner factorisations of multivariate polynomials (2020) arXiv
  3. Sousa, Eduardo Vera; Fernandes, Leandro A. F.: TbGAL: a tensor-based library for geometric algebra (2020)
  4. D. Huppenkothen, M. Bachetti, A. L. Stevens, S. Migliari, P. Balm, O. Hammad, U. M. Khan, H. Mishra, H. Rashid, S. Sharma, R. V. Blanco, E. M. Ribeiro: Stingray: A Modern Python Library For Spectral Timing (2019) arXiv
  5. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  6. Leo C. Stein: qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients (2019) arXiv
  7. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  8. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH
  9. Bortolussi, Luca; Silvetti, Simone: Bayesian statistical parameter synthesis for linear temporal properties of stochastic models (2018)
  10. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  11. Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko: AutoGraph: Imperative-style Coding with Graph-based Performance (2018) arXiv
  12. David Topping; Paul Connolly; Jonathan Reid: PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations (2018) not zbMATH
  13. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv
  14. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017) not zbMATH
  15. Leon Thurner, Alexander Scheidler, Florian Schaefer, Jan-Hendrik Menke, Julian Dollichon, Friederike Meier, Steffen Meinecke, Martin Braun: Pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems (2017) arXiv
  16. Sebastian Krämer, David Plankensteiner, Laurin Ostermann, Helmut Ritsch: QuantumOptics.jl: A Julia framework for simulating open quantum systems (2017) arXiv
  17. Mortensen, Mikael; Langtangen, Hans Petter: High performance python for direct numerical simulations of turbulent flows (2016)