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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.

Decisions about Julia and MATLAB
Alexander Nozik
Senior researcher at MIPT · | 3 upvotes · 172.2K views
Migrated
from
JuliaJulia
to
KotlinKotlin

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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Pros of Julia
Pros of MATLAB
  • 24
    Fast Performance and Easy Experimentation
  • 21
    Designed for parallelism and distributed computation
  • 18
    Free and Open Source
  • 17
    Dynamic Type System
  • 16
    Multiple Dispatch
  • 16
    Calling C functions directly
  • 16
    Lisp-like Macros
  • 10
    Powerful Shell-like Capabilities
  • 9
    Jupyter notebook integration
  • 8
    REPL
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
    Interoperability
  • 20
    Simulink
  • 5
    Model based software development
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix

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Cons of Julia
Cons of MATLAB
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
  • 2
    No static compilation
  • 2
    Parameter-value pairs syntax to pass arguments clunky
  • 2
    Doesn't allow unpacking tuples/arguments lists with *
  • 2
    Does not support named function arguments

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What is Julia?

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.

What is MATLAB?

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.

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What companies use Julia?
What companies use MATLAB?
See which teams inside your own company are using Julia or MATLAB.
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What tools integrate with Julia?
What tools integrate with MATLAB?

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What are some alternatives to Julia and MATLAB?
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.
R Language
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
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.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
See all alternatives