The Rundown on Julia Programming

Have you heard of Julia programming? If not, you probably will soon. This dynamic high-level programming language is able to meet the unique needs of scientific computing while still being an effective tool for general-purpose coding, web development, and specification language.

The Scoop on Julia Programming

First appearing four years ago, Julia programming is the creation of Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman. IT features syntax based on other languages and computing environments, allowing for a seamless transition. So whether you are familiar with Julia programming or not, you should have no problem using this language. It offers an advanced compiler, high level of numerical accuracy, distributed parallel execution and more. The library for Julia was mostly written in Julia, featuring open-source C libraries as well as other libraries.

As explained by Evan Miller, one of the problems with traditional programming languages is steep learning curve associated with learning them. Julia seeks to eliminate these technical hurdles by featuring familiar syntax that’s used in many other languages. This dynamic language is built using great performance, breaking down the constraints between “high-level code and native assembly.” With that said, speed is one of the greatest benefits associated with Julia. According to Wikipedia, its speed is within a factor of relative two optimized C code.

New Julia updates are released on a monthly basis, patching bugs and implementing new features.

The core of Julia is implemented in C and C++. It uses parsers in Scheme, along with an LLVM compiler for just-in-time generation of 32/64-bit machine code (varies depending on the platform on which Julia runs.

Julia Programming Features:

We could write an entire blog post strictly on the features of Julia programming, and we may later, but here are some of the most notable ones:

  • Package manager built into the language.
  • Multiple dispatch, allowing users to define the behavior of functions on combinations of argument types.
  • Types for optimization, dispatch and documentation.
  • High performance, rivaling the likes of C programming.
  • Ideal for parallelism and distributed computation.
  • Call functions of Python programming.
  • Macros and metaprogramming facilities.
  • Shell-like interface for managing processes.
  • Unicode support, including UTF-8.
  • Free and open source.
  • MIT-licensed.

If you’re interested in learning more about Julia programming, head over to the language’s official website at You can download the full-installer package by clicking the “Download” link at the top of the website. The package includes the terminal using the Julia command line; the Juno development environment, and the browser with IJulia notebooks.

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