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.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
- Chen, Huajie; Ortner, Christoph: QM/MM methods for crystalline defects. I: Locality of the tight binding model (2016)
- Gaudreau, P.; Slevinsky, R.; Safouhi, H.: The double exponential sinc collocation method for singular Sturm-Liouville problems (2016)
- Gose, Alexander H.; Denton, Brian T.: Sequential bounding methods for two-stage stochastic programs (2016)
- Kraemer, Atahualpa S.; Kryukov, Nikolay; Sanders, David P.: Efficient algorithms for general periodic Lorentz gases in two and three dimensions (2016)
- Knopp, T.; Weber, A.: Local system matrix compression for efficient reconstruction in magnetic particle imaging (2015)
- Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
- Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas: $\Bbb S_n$FFT: a Julia toolkit for Fourier analysis of functions over permutations (2015)
- 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)
Further publications can be found at: http://julialang.org/publications/