Julia features

Let see some unique and popular features of Julia in details

Julia compilation..........JIT Compilation:
There is an expense to the JIT (Just-in-time) compiler approach rather than an unadulterated translator: The compiler needs to parse the source code and create machine code before your code can run. That can mean a perceptible beginning up time for Julia programs the first run through each capacity and full scale runs in a meeting. In this way, in the screen capture beneath, we see that the second time we produce a million irregular gliding point numbers, the time taken is a request for extent not exactly on the principal execution. Both the @time large scale and the rand() work should have been incorporated the first run through the code, in light of the fact that the Julia libraries are written in Julia.

Julia Parallelism:
Julia is explicitly intended with the end goal of disseminated calculation and parallelism, utilizing two natives, for example, remote calls and remote references. Remote references is of two sorts: future and remote channel. 

A future is like javascript guarantee though a remote channel can be rewritable and utilized for interprocess correspondence, for example, Go channel or Unix pipe.

Julia type-system:
Julia has an inconspicuous yet incredible sort framework that is dynamic with run-time type inference by default, and optional type annotations is also allowed. This is like TypeScript. For instance:

julia> (1+2)::AbstractFloat

  ERROR: TypeError: typeassert: expected AbstractFloat, got Int64

julia> (1+2)::Int

Julia String:
Julia has support for Unicode which is stores in UTF-8 format and Julia support ASCII character as well.

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