Julia Programming Language
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A Julia Programming Language is a high-level, high-performance dynamic programming language for technical computing developed and stewarded by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors.
- Context:
- It can (typically) be used to define Julia Code (such as Julia program or a Julia library) with Julia statements (for Julia variables, Julia data structures, and Julia conditional statements) that can be interpreted by a Julia Interpreter.
- It can (typically) be an Interpreted Programming Language.
- It can (typically) include Python Standard Libraries.
- It can (often) be used as a Data Analysis Language.
- It can be parsed by a Julia Parser (e.g. in a Julia implementation).
- It can support several Julia Built-in Data Types.
- …
- Example(s):
- Counter-Example(s):
- See: Perl, Fortran, Octave, Mathematica, IJulia, Scientific Computing, Foreign Function Interface.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Julia_(programming_language) Retrieved:2014-8-2.
- Julia is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming. Unusual aspects of Julia's design include having a type system with parametric types in a fully dynamic programming language and adopting multiple dispatch as its core programming paradigm. It allows for parallel and distributed computing; and direct calling of C and Fortran libraries without glue code. Julia includes efficient libraries for floating point, linear algebra, random number generation, fast Fourier transforms, and regular expression matching. Julia's core is implemented in C and C++, its parser in Scheme, and the LLVM compiler framework is used for just-in-time generation of machine code for x86(-64) with it almost working on ARM (build failure). [1] [2] The standard library is implemented in Julia itself, using Node.js's libuv library for efficient, cross-platform I/O. The most notable aspect of Julia's implementation is its speed, which is often within a factor of two relative to fully optimized C code. Development of Julia began in 2009 and an open-source version was publicized in February 2012.
2012
- http://julialang.org/
- QUOTE: Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, mostly written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, FFTs, and string processing
- http://strata.oreilly.com/2012/10/matlab-r-julia-languages-for-data-analysis.html
- Julia’s weakness, however, is its libraries. R has CRAN, certainly the most impressive collection of statistical libraries available anywhere. MATLAB also has a wide range of toolboxes available, for a price. Julia also lacks a rich development environment, like RStudio, and has only rudimentary support for plotting, which is a pretty critical part of most exploratory data analysis.