Over the years, with the advancement in compiler techniques and language design, it is possible to eliminate the trade-off between performance and dynamic prototyping. So, the scientific computing required was a good dynamic language like Python together with performance like C. And then came Julia, a general purpose programming language designed according to the requirements of scientific and technical computing, providing performance comparable to C/C++, and with an environment productive enough for prototyping like the high-level dynamic language of Python. The key to Julia's performance is its design and Low Level Virtual Machine (LLVM) based Just-in-Time compiler which enables it to approach the performance of C and Fortran.
The key features offered by Julia are:
A general purpose high-level dynamic programming language designed to be effective for numerical and scientific computing
A Low-Level Virtual Machine (LLVM) based Just-in-Time (JIT) compiler that enables Julia to approach the performance of statically-compiled languages like C/C++
The following quote is from the development team of Julia—Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman:
Note
We are greedy: we want more.
We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.
(Did we mention it should be as fast as C?)
It is quite often compared with Python, R, MATLAB, and Octave. These have been around for quite some time and Julia is highly influenced by them, especially when it comes to numerical and scientific computing. Although Julia is really good at it, it is not restricted to just scientific computing as it can also be used for web and general purpose programming.
The development team of Julia aims to create a remarkable and never done before combination of power and efficiency without compromising the ease of use in one single language. Most of Julia's core is implemented in C/C++. Julia's parser is written in Scheme. Julia's efficient and cross-platform I/O is provided by the Node.js's libuv.
Features and advantages of Julia can be summarized as follows:
It's designed for distributed and parallel computation.
Julia provides an extensive library of mathematical functions with great numerical accuracy.
Julia gives the functionality of multiple dispatch. Multiple dispatch refers to using many combinations of argument types to define function behaviors.
The Pycall package enables Julia to call Python functions in its code and Matlab packages using Matlab.jl. Functions and libraries written in C can also be called directly without any need for APIs or wrappers.
Julia provides powerful shell-like capabilities for managing other processes in the system.
Unlike other languages, user-defined types in Julia are compact and quite fast as built-ins.
Data analysis makes great use of vectorized code to gain performance benefits. Julia eliminates the need to vectorize code to gain performance. De-vectorized code written in Julia can be as fast as vectorized code.
It uses lightweight "green" threading also known as tasks or coroutines, cooperative multitasking, or one-shot continuations.
Julia has a powerful type system. The conversions provided are elegant and extensible.
It has efficient support for Unicode.
It has facilities for metaprogramming and Lisp-like macros.
It has a built-in package manager. (Pkg)
Julia provides efficient, specialized and automatic generation of code for different argument types.
It's free and open source with an MIT license.
