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  5. Julia vs MATLAB

Julia vs MATLAB

OverviewDecisionsComparisonAlternatives

Overview

MATLAB
MATLAB
Stacks1.1K
Followers702
Votes37
Julia
Julia
Stacks666
Followers677
Votes171
GitHub Stars47.9K
Forks5.7K

Julia vs MATLAB: What are the differences?

Introduction

In this article, we will discuss the key differences between Julia and MATLAB, two popular programming languages commonly used for scientific and numerical computing.

  1. Syntax and Ease of Use: Julia is known for its simple and readable syntax, which closely resembles traditional mathematical notation. The language is designed to be approachable and intuitive, making it easier for new users to understand and write code. On the other hand, MATLAB has a more traditional programming syntax, which may require some learning curve for new users.

  2. Performance and Speed: Julia is renowned for its exceptional performance and speed. It is designed to execute code at near-native speeds, utilizing just-in-time (JIT) compilation to optimize performance. This computational efficiency makes Julia ideal for complex calculations and large-scale simulations. In contrast, MATLAB is not as efficient as Julia when it comes to computational speed, particularly for intensive scientific and numerical computations.

  3. Open-source vs Proprietary: Julia is an open-source programming language, allowing users to freely access and modify its source code. This open nature fosters a strong community of contributors, leading to continuous development and improvement of the language and its ecosystem. Conversely, MATLAB is a proprietary software developed by MathWorks. While it provides comprehensive functionality and a vast range of toolboxes, users have limited visibility and control over the inner workings of the language.

  4. Interoperability and Integration: Julia is designed to seamlessly integrate and interact with other programming languages such as Python, R, and C. This interoperability allows users to leverage the strengths of different languages while working on complex projects. In contrast, MATLAB does have some interaction capabilities with other languages through additional toolboxes, but it is not as flexible or comprehensive as Julia in this regard.

  5. Parallel Computing: Julia has built-in parallel computing capabilities, which enable users to execute code across multiple processors and distributed computing clusters. This parallelism is especially beneficial for executing computationally intensive tasks concurrently, resulting in significant performance improvements. In comparison, MATLAB requires additional toolboxes and specific code modifications to achieve parallel computing, making it less straightforward to use and optimize for parallelism.

  6. Community and Documentation: Julia's community is rapidly growing, and it has an active user base that actively contributes to its development. The language has extensive documentation and support resources, including online forums, tutorials, and user-contributed packages. MATLAB, being a mature and widely used language, also has a significant community and offers comprehensive documentation and support resources. However, Julia's community and support ecosystem are relatively more dynamic and evolving.

In summary, Julia stands out for its simple yet powerful syntax, superior performance, open-source nature, rich interoperability, built-in parallel computing capabilities, and a growing community. MATLAB, on the other hand, offers a more traditional syntax, extensive functionality through various toolboxes, and a well-established user base.

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Advice on MATLAB, Julia

Alexander
Alexander

Senior researcher at MIPT

Oct 27, 2020

Decided

After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.

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Comments

Detailed Comparison

MATLAB
MATLAB
Julia
Julia

Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

Statistics
GitHub Stars
-
GitHub Stars
47.9K
GitHub Forks
-
GitHub Forks
5.7K
Stacks
1.1K
Stacks
666
Followers
702
Followers
677
Votes
37
Votes
171
Pros & Cons
Pros
  • 20
    Simulink
  • 5
    Functions, statements, plots, directory navigation easy
  • 5
    Model based software development
  • 3
    S-Functions
  • 2
    REPL
Cons
  • 2
    Doesn't allow unpacking tuples/arguments lists with *
  • 2
    Does not support named function arguments
  • 2
    Parameter-value pairs syntax to pass arguments clunky
  • 1
    Costs a lot
Pros
  • 25
    Fast Performance and Easy Experimentation
  • 22
    Designed for parallelism and distributed computation
  • 19
    Free and Open Source
  • 17
    Dynamic Type System
  • 17
    Calling C functions directly
Cons
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
Integrations
No integrations available
GitHub
GitHub
Azure Web App for Containers
Azure Web App for Containers
GitLab
GitLab
Slack
Slack
C++
C++
Rust
Rust
C lang
C lang
Stack Overflow
Stack Overflow
vscode.dev
vscode.dev
Python
Python

What are some alternatives to MATLAB, Julia?

JavaScript

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Meteor

Meteor

A Meteor application is a mix of JavaScript that runs inside a client web browser, JavaScript that runs on the Meteor server inside a Node.js container, and all the supporting HTML fragments, CSS rules, and static assets.

Scala

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

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