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 19 articles )

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  1. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  2. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  3. Anderes, Ethan; Borgwardt, Steffen; Miller, Jacob: Discrete Wasserstein barycenters: optimal transport for discrete data (2016)
  4. Auzinger, Winfried; Hofstätter, Harald; Koch, Othmar: Symbolic manipulation of flows of nonlinear evolution equations, with application in the analysis of split-step time integrators (2016)
  5. Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
  6. Bertsimas, Dimitris; Dunning, Iain: Multistage robust mixed-integer optimization with adaptive partitions (2016)
  7. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  8. Chen, Huajie; Ortner, Christoph: QM/MM methods for crystalline defects. I: Locality of the tight binding model (2016)
  9. Gaudreau, P.; Slevinsky, R.; Safouhi, H.: The double exponential sinc collocation method for singular Sturm-Liouville problems (2016)
  10. Geiersbach, Caroline; Heitzinger, Clemens; Tulzer, Gerhard: Optimal approximation of the first-order corrector in multiscale stochastic elliptic PDE (2016)
  11. Gose, Alexander H.; Denton, Brian T.: Sequential bounding methods for two-stage stochastic programs (2016)
  12. Higham, Nicholas J.; Noferini, Vanni: An algorithm to compute the polar decomposition of a $3 \times 3$ matrix (2016)
  13. Kraemer, Atahualpa S.; Kryukov, Nikolay; Sanders, David P.: Efficient algorithms for general periodic Lorentz gases in two and three dimensions (2016)
  14. Knopp, T.; Weber, A.: Local system matrix compression for efficient reconstruction in magnetic particle imaging (2015)
  15. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  16. Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas: $\Bbb S_n$FFT: a Julia toolkit for Fourier analysis of functions over permutations (2015)
  17. Romano, Paul K.: An algorithm for generating random variates from the Madland-Nix fission energy spectrum (2015)
  18. Slevinsky, Richard Mikael; Olver, Sheehan: On the use of conformal maps for the acceleration of convergence of the trapezoidal rule and sinc numerical methods (2015)
  19. Townsend, Alex; Olver, Sheehan: The automatic solution of partial differential equations using a global spectral method (2015)

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