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The Julia Programming Language<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia Programming Language</span>
</span>

The official website for the Julia Language<span itemscope itemtype="http://schema.org/Person">
        <span itemprop="name">Julia Language</span>
</span>. Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.

Julia Programming Language<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia Programming Language</span>
</span>

Download Documentation Star

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> in a Nutshell

Fast

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> was designed for high performance. Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> programs automatically compile to efficient native code via LLVM, and support multiple platforms.

Dynamic

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> is dynamically typed, feels like a scripting language, and has good support for interactive use, but can also optionally be separately compiled.

Reproducible

Reproducible environments make it possible to recreate the same Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> environment every time, across platforms, with pre-built binaries.

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. The talk on the Unreasonable Effectiveness of Multiple Dispatch explains why it works so well.

General

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. One can build entire Applications and Microservices in Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span>.

Open source

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> is an open source project with over 1,000 contributors. It is made available under the MIT license. The source code is available on GitHub.

See Julia Code Examples Try Julia In Your Browser

General Computing

Build, Deploy or Embed Your Code

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> makes it possible to build complete applications. Write web UIs with Dash.jl and Genie.jl or native UIs with GTK.jl. Pull data from a variety of databases. Build shared libraries and executables with PackageCompiler. Deploy on a webserver with HTTP.jl or embedded devices. Powerful shell integration make it easy to managing other processes.

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> has foreign function interfaces for C, Fortran, C++, Python, R, Java, Mathematica, Matlab, and many other languages. Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> can also be embedded in other programs through its embedding API. Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span>'s PackageCompiler makes it possible to build binaries from Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> programs that can be integrated into larger projects. Python programs can call Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> using PyJulia. R programs can do the same with R's JuliaCall, which is demonstrated by calling MixedModels.jl from R. Mathematica supports calling Julia through its External Evaluation System.

Parallel Computing

Parallel and Heterogeneous Computing

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> is designed for parallelism, and provides built-in primitives for parallel computing at every level: instruction level parallelism, multi-threading, GPU computing, and distributed computing. The Celeste.jl project achieved 1.5 PetaFLOP/s on the Cori supercomputer at NERSC using 650,000 cores.

The Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> compiler can also generate native code for GPUs. Packages such as DistributedArrays.jl and Dagger.jl provide higher levels of abstraction for parallelism. Distributed Linear Algebra is provided by packages like Elemental.jl and TSVD.jl. MPI style parallelism is also available through MPI.jl.

Machine Learning

Scalable Machine Learning

The MLJ.jl package provides a unified interface to common machine learning algorithms, which include generalized linear models, decision trees, and clustering. Flux.jl and Knet.jl are powerful packages for Deep Learning. Packages such as Metalhead, ObjectDetector, and TextAnalysis.jl provide ready to use pre-trained models for common tasks. AlphaZero.jl provides a high performance implementation of the reinforcement learning algorithms from AlphaZero. Turing.jl is a best in class package for probabilistic programming.

Scientific Computing

Rich Ecosystem for Scientific Computing

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> is designed from the ground up to be very good at numerical and scientific computing. This can be seen in the abundance of scientific tooling written in Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span>, such as the state-of-the-art differential equations ecosystem (DifferentialEquations.jl), optimization tools (JuMP.jl and Optim.jl), iterative linear solvers (IterativeSolvers.jl), Fast Fourier transforms (AbstractFFTs.jl), and much more. General purpose simulation frameworks are available for Scientific Machine Learning, Quantum computing and much more.

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> also offers a number of domain-specific ecosystems, such as in biology (BioJulia), operations research (JuMP Dev), image processing (JuliaImages), quantum physics (QuantumBFS), nonlinear dynamics (JuliaDynamics), quantitative economics (QuantEcon), astronomy (JuliaAstro) and ecology (EcoJulia). With a set of highly enthusiastic developers and maintainers, the scientific ecosystem in Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> continues to grow rapidly.

Interact with your Data

The Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> data ecosystem provides DataFrames.jl to work with datasets, and perform common data manipulations. CSV.jl is a fast multi-threaded package to read CSV files and integration with the Arrow ecosystem is in the works with Arrow.jl. Online computations on streaming data can be performed with OnlineStats.jl. The Queryverse provides query, file IO and visualization functionality. In addition to working with tabular data, the JuliaGraphs packages make it easy to work with combinatorial data.

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> can work with almost all databases using JDBC.jl and ODBC.jl drivers. In addition, it also integrates with the Spark ecosystem through Spark.jl.

Data Visualization and Plotting

Data visualization has a complicated history. Plotting software makes trade-offs between features and simplicity, speed and beauty, and a static and dynamic interface. Some packages make a display and never change it, while others make updates in real-time.

Plots.jl is a visualization interface and toolset. It provides a common API across various backends, like GR.jl, PyPlot.jl, and PlotlyJS.jl. Makie.jl is a sophisticated package for complex graphics and animations. Users who are used to "grammar of graphics" plotting APIs should take a look at Gadfly.jl. VegaLite.jl provides the Vega-Lite grammar of interactive graphics interface as a Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> package. For those who do not wish to leave the comfort of the terminal, there is also UnicodePlots.jl.

Lorenz Attractor<span itemscope itemtype="http://schema.org/Person">
        <span itemprop="name">Lorenz Attractor</span>
</span> in Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span>

"Watch what unfolded at JuliaCon 2023 here. The latest developments, optimizations, and features happen right here, at JuliaCon.

Julia Channel on YouTube JuliaCon 2023 videos

Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> has been downloaded over 45 million times and the Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> community has registered over 10,000 Julia packages for community use. These include various mathematical libraries, data manipulation tools, and packages for general purpose computing. In addition to these, you can easily use libraries from Python, R, C/Fortran, C++, and Java. If you do not find what you are looking for, ask on Discourse, or even better, contribute one!

JuliaHub: Package Search JuliaPackages: Trending Feed of new Julia packages

Recent Blog Posts

Julia 1.10 Highlights

Highlights of the Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> 1.10 release.

PSA: Thread-local state is no longer recommended

PSA: Thread-local state is no longer recommended; Common misconceptions about threadid and nthreads

Julia 1.9 Highlights

Highlights of the Julia<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">Julia</span>
</span> 1.9 release.

Visit Blog View Community Posts

Discourse

Discourse

GitHub

Source code

Zulip

Zulip

Slack

Slack

Twitter

#JuliaLang

Videos

YouTube

LinkedIn

LinkedIn

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Twitch

Editors and IDEs

VS Code

VS Code Extension

Jupyter

Jupyter kernel

Pluto.jl

Simple reactive notebooks

Vim

Vim plugin

Emacs

Emacs plugin

Debugger

Debugger.jl

Profiler

Profile (Stdlib)

Revise

Revise.jl

GPUs

JuliaGPU

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2023 JuliaLang.org<span itemscope itemtype="http://schema.org/Organization">
        <span itemprop="name">JuliaLang.org</span>
        <link itemprop="url" href="http://JuliaLang.org">
</span> contributors. The content on this website is made available under the MIT license.