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 38 articles )

Showing results 1 to 20 of 38.
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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. Felix Matuschke; Katrin Amunts; Markus Axer: fastPLI: A Fiber Architecture Simulation Toolbox for 3D-PLI (2021) not zbMATH
  3. Luciano G. Silvestri, Lucas J. Stanek, Gautham Dharuman, Yongjun Choi, Michael S. Murillo: Sarkas: A Fast Pure-Python Molecular Dynamics Suite for Plasma Physics (2021) arXiv
  4. Mika, Michal L.; Hughes, Thomas J. R.; Schillinger, Dominik; Wriggers, Peter; Hiemstra, René R.: A matrix-free isogeometric Galerkin method for Karhunen-Loève approximation of random fields using tensor product splines, tensor contraction and interpolation based quadrature (2021)
  5. Nightingale, J. W., Hayes, R., Kelly, A., Amvrosiadis, A., Etherington, A., He, Q., Li, N., Cao, X., Frawley, J., Cole, S., Enia, A., Frenk, C., Harvey, D., Li, R., Massey, R., Negrello, M., Robertson, A: PyAutoLens: Open-Source Strong Gravitational Lensing (2021) not zbMATH
  6. Noyola-García, Benjamín Salomón; Rodriguez-Romo, Suemi: Simulations of GA melting based on multiple-relaxation time lattice Boltzmann method performed with CUDA in Python (2021)
  7. Peter R. Wiecha, Clément Majorel, Arnaud Arbouet, Adelin Patoux, Yoann Brûlé, Gérard Colas des Francs, Christian Girard: pyGDM - new functionalities and major improvements to the python toolkit for nano-optics full-field simulations (2021) arXiv
  8. 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
  9. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)
  10. Timo Betcke; Matthew W. Scroggs: Bempp-cl: A fast Python based just-in-time compiling boundary element library (2021) not zbMATH
  11. Adam, Nicolas; Le Tallec, Patrick; Zarroug, Malek: Multipatch isogeometric mortar methods for thick shells (2020)
  12. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  13. Dempster, Angus; Petitjean, François; Webb, Geoffrey I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (2020)
  14. Faouzi, Johann; Janati, Hicham: pyts: a Python package for time series classification (2020)
  15. Gevorkyan, M. N.; Korolkova, A. V.; Kulyabov, D. S.; Sevast’yanov, L. A.: A modular extension for a computer algebra system (2020)
  16. Jannik Michelfeit: multivar_horner: a python package for computing Horner factorisations of multivariate polynomials (2020) arXiv
  17. Jonathan Demaeyer, Lesley De Cruz, Stéphane Vannitsem: qgs: A flexible Python framework of reduced-order multiscale climate models (2020) not zbMATH
  18. 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
  19. 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
  20. Massias, Mathurin; Vaiter, Samuel; Gramfort, Alexandre; Salmon, Joseph: Dual extrapolation for sparse GLMs (2020)

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