Numba

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 30 articles )

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  1. Caio Felippe Curitiba Marcellos, Gerson Francisco da Silva Junior, Elvis do Amaral Soares, Fabio Ramos, Amaro G. Barreto Jr: PyEquIon: A Python Package For Automatic Speciation Calculations of Aqueous Electrolyte Solutions (2021) arXiv
  2. Piotr Bartman, Sylwester Arabas, Kamil Górski, Anna Jaruga, Grzegorz Łazarski, Michael Olesik, Bartosz Piasecki, Aleksandra Talar: PySDM v1: particle-based cloud modelling package for warm-rain microphysics and aqueous chemistry (2021) arXiv
  3. Timo Betcke; Matthew W. Scroggs: Bempp-cl: A fast Python based just-in-time compiling boundary element library (2021) not zbMATH
  4. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  5. Dempster, Angus; Petitjean, François; Webb, Geoffrey I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (2020)
  6. Faouzi, Johann; Janati, Hicham: pyts: a Python package for time series classification (2020)
  7. Gevorkyan, M. N.; Korolkova, A. V.; Kulyabov, D. S.; Sevast’yanov, L. A.: A modular extension for a computer algebra system (2020)
  8. Jannik Michelfeit: multivar_horner: a python package for computing Horner factorisations of multivariate polynomials (2020) arXiv
  9. Jonathan Demaeyer, Lesley De Cruz, Stéphane Vannitsem: qgs: A flexible Python framework of reduced-order multiscale climate models (2020) not zbMATH
  10. Kshitij Aggarwal; Devansh Agarwal; Joseph W Kania; William Fiore; Reshma Anna Thomas; Scott M. Ransom; Paul B. Demorest; Robert S. Wharton; Sarah Burke-Spolaor; Duncan R. Lorimer; Maura A. Mclaughlin; Nathaniel Garver-Daniels: Your: Your Unified Reader (2020) not zbMATH
  11. Leevi Kerkelä; Fabio Nery; Matt G. Hall; Chris A. Clark: Disimpy: A massively parallel Monte Carlo simulator for generating diffusion-weighted MRI data in Python (2020) not zbMATH
  12. Massias, Mathurin; Vaiter, Samuel; Gramfort, Alexandre; Salmon, Joseph: Dual extrapolation for sparse GLMs (2020)
  13. Schreiber, Jacob; Bilmes, Jeffrey; Noble, William Stafford: apricot: submodular selection for data summarization in Python (2020)
  14. Sousa, Eduardo Vera; Fernandes, Leandro A. F.: TbGAL: a tensor-based library for geometric algebra (2020)
  15. Tavenard, Romain; Faouzi, Johann; Vandewiele, Gilles; Divo, Felix; Androz, Guillaume; Holtz, Chester; Payne, Marie; Yurchak, Roman; Rußwurm, Marc; Kolar, Kushal; Woods, Eli: tslearn, a machine learning toolkit for time series data (2020)
  16. 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
  17. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  18. Leo C. Stein: qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients (2019) arXiv
  19. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  20. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH

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