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 57 articles )

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  1. Alex Tritt, Joshua Morris, Joel Hochstetter, R. P. Anderson, James Saunderson, L. D. Turner: Spinsim: a GPU optimized python package for simulating spin-half and spin-one quantum systems (2022) arXiv
  2. Breuils, Stephane; Tachibana, Kanta; Hitzer, Eckhard: New applications of Clifford’s geometric algebra (2022)
  3. Buffoni, Lorenzo; Campisi, Michele: Spontaneous fluctuation-symmetry breaking and the Landauer principle (2022)
  4. Škrlj, Blaž; Džeroski, Sašo; Lavrač, Nada; Petković, Matej: ReliefE: feature ranking in high-dimensional spaces via manifold embeddings (2022)
  5. van ’t Wout, Elwin: Stable and efficient FEM-BEM coupling with OSRC regularisation for acoustic wave transmission (2022)
  6. Andrea Zonca, Ben Thorne, Nicoletta Krachmalnicoff, Julian Borrill: The Python Sky Model 3 software (2021) arXiv
  7. Andrew J. Moodie, Jayaram Hariharan, Eric Barefoot, Paola Passalacqua: pyDeltaRCM: a exible numerical delta model (2021) not zbMATH
  8. Brown et al.: libCEED: Fast algebra for high-order element-based discretizations (2021) not zbMATH
  9. 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
  10. Felix Matuschke; Katrin Amunts; Markus Axer: fastPLI: A Fiber Architecture Simulation Toolbox for 3D-PLI (2021) not zbMATH
  11. Gareth D. Simons: The cityseer Python package for pedestrian-scale network-based urban analysis (2021) arXiv
  12. Gürcan, Ö. D.: Dynamical network models of the turbulent cascade (2021)
  13. Jacquier, Pierre; Abdedou, Azzedine; Delmas, Vincent; Soulaïmani, Azzeddine: Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling (2021)
  14. Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev: Scikit-dimension: a Python package for intrinsic dimension estimation (2021) arXiv
  15. Kamarianakis, Manos; Papagiannakis, George: An all-in-one geometric algorithm for cutting, tearing, and drilling deformable models (2021)
  16. 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
  17. Manuel Meyer, James Davies, Julian Kuhlmann: gammaALPs: An open-source python package for computing photon-axion-like-particle oscillations in astrophysical environments (2021) arXiv
  18. Matthias Drvoderic, Matthias Rettl, Martin Pletz, Clara Schuecker: CrackDect: Detecting crack densities in images of fiber-reinforced polymers (2021) not zbMATH
  19. 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)
  20. 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

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