Why AI Engineers Are Growingly Preferring Julia Over Python?

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MIT (Massachusetts Institute of Technology) has declared Julia as the only existing high-level dynamic programming language qualified to enter the coveted “petaflop club”. The said language has been applied to facilitate simulation of 188 million galaxies and stars, besides other astronomical bodies on Cori, the 5th fastest supercomputer in the world.

Julia is a considerably new programming language developed at MIT in 2009 and is being used in data analytics since 2012. It has got its obvious advantages over Python, a relatively old programming language in data science. Julia was developed in the first place to merge the speed of C with the applicability of Python. Besides, it encapsulates the mathematical prowess of MATLAB, the dynamism of Ruby, and the statistical capabilities of R.

Julia has been termed by the developers as a language for those “who want it all”.

Julia’s Supremacy Over Python

The Language Incepted with Added Computational Features

Julia was created to empower numerical and scientific computation. And therefore, it automatically takes the lead with added features incorporated into it.

Mathematical Syntax

  • AI Professionals these days have increased using scientific computing environments and languages, such as R, Octave, Mathematica, and MATLAB.
  • Julia’s mathematical syntax is designed similarly to how mathematical formulas are put down in the world outside of computing, thus making things simpler for those with no coding and programming background.

Julia Empowers AI Engineers Offering Advanced Memory Management

Unlike Python, Julia provides for manual control on data garbage collection. It doesn’t make the user deal with the details relating to the allocation and releasing of memory. Besides, when one shifts to Julia, he doesn’t lose any of the Python’s benefits.

Julia Presents Better Operations-Parallelism

However, both Python and Julia allows running operations parallelly. But, while Python’s methodologies for operations’ parallelization demand data to be deserialized and serialized among nodes and threads, Julia’s parallel operations are much refined.

Further, Julia’s syntax for parallel operation processing is comparatively less top-heavy.

Language Characteristics That Make Julia Superior to Python at Data Analysis

  • To achieve faster performance, Julia compiles just-in-time and exploits the LLVM compiler framework for the same. It matches the speed of C when at its best.
  • Julia is highly interactive. Either, it utilizes REPL or an interactive command line. One-off scripts can be punched in anytime as and when needed with Julia.
  • Julia offers a straightforward syntax just like Python, but more powerful and expressive.
  • Julia merges the advantages of static and dynamic typing. One can mention kinds of variables such as “unsigned 32-bit integer”. Additionally, one can design hierarchies of different types in Julia that can comfortably handle diverse variables, e.g. writing a function that can take up integers without determining the signing or length of the integer.
  • Julia can access libraries of C, Python, and Fortran. The language can interact seamlessly with external libraries written in Fortran & C. Furthermore, Julia can integrate Python code utilizing PyCall library.
  • Julia assists metaprogramming. Its programs can help develop other Julia programs, and can even alter their own coding.
  • Julia comprises a full-featured debugger. The 1.1 version of Julia brought a debugging suite that helps process code in local REPL, and facilitates variable inspection and adding of breakpoints in code. Further, one can execute precision tasks using Julia such as stepping across a function developed by code.

Considering the above-presented arguments in favor of Julia, it certainly seems like the future belongs to Julia, and slowly and gradually, it will take over Python & R that is termed as the most preferred languages in data science.