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 22 articles )

Showing results 1 to 20 of 22.
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  1. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  2. Faouzi, Johann; Janati, Hicham: pyts: a Python package for time series classification (2020)
  3. Jannik Michelfeit: multivar_horner: a python package for computing Horner factorisations of multivariate polynomials (2020) arXiv
  4. 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
  5. 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
  6. Sousa, Eduardo Vera; Fernandes, Leandro A. F.: TbGAL: a tensor-based library for geometric algebra (2020)
  7. 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)
  8. 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
  9. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  10. Leo C. Stein: qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients (2019) arXiv
  11. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  12. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH
  13. Bortolussi, Luca; Silvetti, Simone: Bayesian statistical parameter synthesis for linear temporal properties of stochastic models (2018)
  14. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  15. 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
  16. David Topping; Paul Connolly; Jonathan Reid: PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations (2018) not zbMATH
  17. Leland McInnes, John Healy, James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018) arXiv
  18. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv
  19. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017) not zbMATH
  20. 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

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