Julia implements LLVM-based just-in-time (JIT) compiler. Combined with the language’s design allow it to approach and often match the performance of C. That means we have a dynamic language, that uses native computer types to create a fast and optimized program for different platforms. Brilliant !!!
|Performance||Approaching that of statically-compiled languages like C and Fortran|
|Co-routines||Lightweight threading designed for parallelism and distributed computation|
|Multiple dispatch||Ability to define function behavior across many combinations of argument types|
|Dynamic type system||Define advanced types for documentation, optimization, and dispatch|
|User-defined types||These types are as fast and compact as built-in types|
|Type inference||Type is created determined automatically from literals|
|Meta-programming||Lisp-like macros and other meta-programming facilities|
|Call Python||Can be used to call python functions using PyCall package|
|Call C functions||Direct C call with no wrappers or special APIs|
|Interpreter||Powerful shell-like capabilities for managing other processes|
|Unicode Strings||Efficient support for Unicode including but not limited to UTF-8|
|Unicode Operators||We can use Unicode symbols as operators|
|Julia hoisting||The ability to use a type or a function before it is defined|
Dispatch technique is the capability of a language to allow creation of polymorphic functions or methods. This is a function that can act on different argument types. In Julia we can create multiple versions of the same function for each argument type or combination of types. These sub-functions are called methods.
A function can have parameters with no type. In this case a dispatch is not possible and the arguments will have type "Any". The function has only one method. To make dispatch possible we must use a type annotation for one or more arguments using two column symbol "::" that is named "is an instance of type …".
In multiple dispatch we use all parameters to identify a method. In Object Oriented programming single dispatch is used. Only one parameter is used to identify a method. The methods are attached to the first argument that is the type. In Java this parameter is invisible (default) and is the current object "this". In python this parameter is called "self". In Julia we do not have a similar default parameter.
Before we can use multiple dispatch we have to understand the types. Once we define types we can create multiple methods for these types. Therefore in Julia types are the corner stone of Julia language. Is the foundation Julia philosophy.
Julia is a high-performance programming language for scientific and numerical computing. It is used by a wide range of companies, including:
Julia is also used by a number of universities, including MIT, Stanford, Cornell, and UC Berkeley.
Read next: Syntax Overview