What is Julia?
Julia is a high-level, just-in-time (JIT) compiled, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. Developed at MIT by Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson and Stefan Karpinski. in 2012.
How is Julia different from other languages?
Julia is relatively new language as compared to well established python, C++, JavaScript etc. This lets Julia developers use the best from all the existing languages and at the same time drop some inconvenient features. Julia has the speed of compiled language like C/C++ and a syntax as simple as interpreted language like Python. This is possible because Julia uses JIT (Just In Time) compiler, a combination of AOT (Ahead Of Time) compilation and interpretation. It also support REPL (Read-Eval-Print loop), where code lines are interpreted at the same time they are written making it convenient for programmers. Julia is fairly compatible with other languages such as C/C++, Java, Python using foreign functions.
Please visit compiler, interpreter, JIT, AOT and REPL for more information.
Why is it used?
Julia's faster LLVM (low level virtual machine) based JIT compilation and better memory management makes it faster and efficient for numerical computation suitable for data science and machine learning applications. Julia is versatile and is designed with technical and scientific users in mind and provides easy interoperability with other programming languages like C/C++ and Python. It works on any operating system like Windows, mac, Unix, etc. The Julia user base and popularity is growing fast due to its appealing features.
Due to its math friendly syntax and fast speed, Julia has become first choice for
Please visit JuliaLang for more information.
Julia uses an JIT compiler to execute the code. The JIT compiler generates byte code for the interpreter just when it is required rather than generating complete byte code for whole program. This strategy provide much required speedup as compared to purely interpreted programming languages and reduce startup time as compared to pure compiled languages. Julia programming code can be written in multiple ways. Some them are listed below:
Julia is supported on all the major operating systems. Click the following links to download the latest release for the respective operating systems.
A text editor is used to write the long Julia scripts with multiple functions and save them (with a .jl extension) for future use. The list of all available text editors for Julia can be found here.
Visual Studio Code is a code editor redefined and optimized for building and debugging modern web and cloud applications. Visual Studio Code is free and open source IDE with extension based support for most languages.
Jupyter Notebook is a web-based tool to run live code including Python. Install Python before installing the stand-alone Jupyter Notebook. After the Python's installation, type one of the following commands in the command terminal.
'pip' is the keyword that is used to install missing libraries/packages in Python. Please visit this page to know more about 'pip'.
Note: Make sure the PATH variable is set properly before installing any Python related package. Click the following links to learn how to set the PATH variable in the respective operating systems.
Add Julia to Jupyter notebooks.
