Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
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• Dynamic type system
• Multiple dispatch
• Built-in package manger
• Call Python functions
• Call C functions directly
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• Provides distributed parallel execution
• Provides library for random number generation
• Ability to overload different combinations of argument types
• Good performance approaching that of statistically compiled languages like C
• Elegant and extensible for numeric and other types
Julia is a sophisticated programming language that is of high performance used for numerical computation. Julia provides a comprehensive compiler, parallel execution that is distributed, a function library that is extensive mathematically and numerical accuracy. All of Julia programs encircle several dispatches by defining and compiling up functions used in argument types of different combinations which in other cases can be defined by the user.
The multiple dispatch provides scientists with the ability of defining function behaviors across several combinations of arguments. Julia also features a dynamic type system which is able to deal with various types of documentation, dispatch, and optimization.
The powerful shell like capabilities provided by Julia are ideal for managing other processes in numerical computation. The lisp-like macros together with metaprogramming facilities are ideal also in numerical computation. Julia also enables generation of efficient and codes automatically and in a special way used in different argument types.
The just in time Julia compiler is combined with other processing languages that match the performance of C allowing it to be used for scientific and numerical computing. Julia is also designed for parallelism and distributed computation. The call C functions provided by Julia need no wrappers or special APIs to function.
The Call Python functions available use the PyCall package for numerical computation. The key building blocks provided by Julia for computation that is distributed making it ideal enough to offer support to several styles of and allowing users to add more parallelism features. This allows data scientists to express the complex algorithms easily.
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