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Julia
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Julia

Overview
Synopsis

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.

Category

Data Analysis Software Free

Features

• Dynamic type system
• Multiple dispatch
• Built-in package manger
• Call Python functions
• Call C functions directly

License

Proprietary

Price

Contact for Pricing

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

Julia

What are the benefits ?

• 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

Rating
Our Rating
User Rating
Ease of use
7.6
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
Customer Support
7.6
Performance
7.6
Implementation
Renew & Recommend
Bottom Line

Julia provides a sophisticated programming language that is of high level and performance used in distributed parallel execution, extensive mathematical calculations, in getting numerical accuracy, and as a sophisticated compiler.

7.6
Our Rating
User Rating
You have rated this

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.

Julia
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Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Implementation
Renew & Recommend

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