High-Performance Dynamic Programming Language For Numerical Computing Julia

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Julia is one of the upcoming programming language which is three years old. Julia has been  Developed at CSAIL, the MIT Department of Mathematics, and throughout the Julia community, it is a fast-maturing programming language developed to be simple to learn, highly dynamic, operational at the speed of C, and ranging in use from general programming to highly quantitative uses such as scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing. The language was launched open-source in 2012 and has begun to amass a large following of users and contributors.

 

Julia is being used to solve complex problems in areas as diverse as economic modeling, spaceflight, bioinformatics, and many others. It is very easy to write the problem, however the issue is to solve it, especially when the problem is cursed with high dimensionality. Julia is programming language that helps to crack the complicated models into easy modules and solve them easily. Julia is important because the next generation of macroeconomic models is very computationally intensive, using high-dimensional models and fitting them over extremely large data sets. Julia- and Python-based learning platform has been successfully been used for quantitative economics focusing on algorithms and numerical methods for studying economic problems as well as coding skills.

 

Julia combines the functionality of quantitative environments such as Matlab, R, SPSS, Stata, SAS, and Python with the speed of production programming languages like Java and C++ to solve big data and analytics problems. It delivers dramatic improvements in simplicity, speed, capacity, and productivity for data scientists, algorithmic traders, quants, scientists, and engineers who need to solve massive computation problems quickly and accurately. The number of Julia users has grown dramatically doubling every nine months. Julia 1.0 has been launched this year.

Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebrarandom number generationsignal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. IJulia, a collaboration between the Jupyter and Julia communities, provides a powerful browser-based graphical notebook interface to Julia.

Julia programs are organized around multiple dispatch; by defining functions and overloading them for different combinations of argument types, which can also be user-defined.

 

Poonam Verma

 

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