Julia: A fast dynamic language for technical computing. Dynamic languages have become popular for scientific computing. They are generally considered highly productive, but lacking in performance. This paper presents Julia, a new dynamic language for technical computing, designed for performance from the beginning by adapting and extending modern programming language techniques. A design based on generic functions and a rich type system simultaneously enables an expressive programming model and successful type inference, leading to good performance for a wide range of programs. This makes it possible for much of the Julia library to be written in Julia itself, while also incorporating best-of-breed C and Fortran libraries.

References in zbMATH (referenced in 306 articles , 1 standard article )

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  1. Alessandro Cheli: Metatheory.jl: Fast and Elegant Algebraic Computation in Julia with Extensible Equality Saturation (2021) not zbMATH
  2. Ali Bagci: JRAF: A Julia Package for Computation of the Relativistic Molecular Auxiliary Functions (2021) arXiv
  3. Anton Plietzsch, Raphael Kogler, Sabine Auer, Julia Merino, Asier Gil-de-Muro, Jan Liße, Christina Vogel, Frank Hellmann: PowerDynamics.jl - An experimentally validated open-source package for the dynamical analysis of power grids (2021) arXiv
  4. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  5. Calandra, Henri; Gratton, Serge; Riccietti, Elisa; Vasseur, Xavier: On high-order multilevel optimization strategies (2021)
  6. Colera, Manuel; Carpio, Jaime; Bermejo, Rodolfo: A nearly-conservative, high-order, forward Lagrange-Galerkin method for the resolution of scalar hyperbolic conservation laws (2021)
  7. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  8. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  9. Eder, Christian; Hofmann, Tommy: Efficient Gröbner bases computation over principal ideal rings (2021)
  10. Francis Banville; Steve Vissault; Timothée Poisot: Mangal.jl and EcologicalNetworks.jl: Two complementary packages for analyzing ecological networks in Julia (2021) not zbMATH
  11. Gabriel Dansereau; Timothée Poisot: SimpleSDMLayers.jl and GBIF.jl: A Framework for Species Distribution Modeling in Julia (2021) not zbMATH
  12. Gael Forget: IndividualDisplacements.jl: a Julia package to simulate and study particle displacements within the climate system (2021) not zbMATH
  13. García, Hernán; Hernández, Camilo; Junca, Mauricio; Velasco, Mauricio: Approximate super-resolution of positive measures in all dimensions (2021)
  14. Huo, Zenan; Mei, Gang; Xu, Nengxiong: JuSFEM: a Julia-based open-source package of parallel smoothed finite element method (S-FEM) for elastic problems (2021)
  15. Kaluba, Marek; Kielak, Dawid; Nowak, Piotr: On property (T) for (\mathrmAut(F_n)) and (\mathrmSL_n(\mathbbZ)) (2021)
  16. Kamenski, Lennard: Sharp bounds on the smallest eigenvalue of finite element equations with arbitrary meshes without regularity assumptions (2021)
  17. Klüners, Jürgen; Komatsu, Toru: Imaginary multiquadratic number fields with class group of exponent (3) and (5) (2021)
  18. Köbler, Jonathan; Magino, Nicola; Andrä, Heiko; Welschinger, Fabian; Müller, Ralf; Schneider, Matti: A computational multi-scale model for the stiffness degradation of short-fiber reinforced plastics subjected to fatigue loading (2021)
  19. López, Oscar; Oleaga, Gerardo; Sánchez, Alejandra: Markov-modulated jump-diffusion models for the short rate: pricing of zero coupon bonds and convexity adjustment (2021)
  20. Mathieu Besancon, Alejandro Carderera, Sebastian Pokutta: FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients (2021) arXiv

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Further publications can be found at: http://julialang.org/publications/