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Julia was designed for high performance The following sections highlight the recommended method for each of the main supported platforms, and then present alternative ways that might be useful in specialized situations. Julia programs automatically compile to efficient native code via llvm, and support multiple platforms
Julia is dynamically typed, feels like a scripting language, and has good support for interactive use, but can also optionally be separately compiled. There are many ways to install julia Similar to r programming language, julia is used for statistical computations and data analysis.
The main homepage for julia can be found at julialang.org
This is the github repository of julia source code, including instructions for compiling and installing julia, below. Juliaup is the recommended way to install julia It automatically installs the latest stable julia binary and helps keep it up to date It also supports installing and using different versions of julia simultaneously
The official website for the julia language Julia is a language that is fast, dynamic, easy to use, and open source Click here to learn more. An expanding series of short tutorials about julia, starting from the beginner level and going up to deal with the more advanced topics.
The julia blog discusses issues of numerical, technical, distributed and parallel computing, as well as programming language design, and how these issues touch upon the design and implementation of the julia programming language.
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