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<!doctype html>
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<title>The Julia Programming Language</title>
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<a href="https://pretalx.com/juliacon2023/cfp">JuliaCon 2023 Call for Proposals is live now!</a>
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<!--
Splash 'THE JULIA PROGRAMMING LANGUAGE'
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<h1 class="main-heading">
The Julia Programming Language
</h1>
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<a class="btn btn-success btn-lg" href="/downloads/" role="button">Download</a>
<a class="btn btn-primary btn-lg" href="https://docs.julialang.org" role="button">Documentation</a>
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Containers: JULIA IN A NUTSHELL
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<h2 class="lead secondary-heading">
Julia in a Nutshell
</h2>
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<div class="col-lg-4 col-md-3 language-features"><hr/></div>
</div>
<br>
<div class="row">
<!-- 1 -->
<div class="col-lg-4 col-md-6 feature">
<h3>Fast</h3>
<p>Julia was designed from the beginning for <a href="/benchmarks/">high performance</a>.
Julia programs compile to efficient native code for <a href="/downloads/#currently_supported_platforms">multiple platforms</a> via LLVM.</p>
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<!-- 2 -->
<div class="col-lg-4 col-md-6 feature">
<h3>Dynamic</h3>
<p>Julia is <a href="https://docs.julialang.org/en/v1/manual/types/">dynamically typed</a>, feels like a scripting language, and has good support for <a href="https://docs.julialang.org/en/v1/stdlib/REPL/">interactive</a> use.</p>
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<!-- 3 -->
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<h3>Reproducible</h3>
<p><a href="https://julialang.github.io/Pkg.jl/v1/environments/">Reproducible environments</a> make it possible to recreate the same Julia environment every time, across platforms, with <a href="https://binarybuilder.org">pre-built binaries</a>.</p>
</div>
<!-- 4 -->
<div class="col-lg-4 col-md-6 feature">
<h3>Composable</h3>
<p>Julia uses <a href="https://docs.julialang.org/en/v1/manual/methods/">multiple dispatch</a> as a paradigm, making it easy to express many object-oriented and <a href="https://docs.julialang.org/en/v1/manual/functions/">functional</a> programming patterns. The talk on the <a href="https://www.youtube.com/watch?v=kc9HwsxE1OY">Unreasonable Effectiveness of Multiple Dispatch</a> explains why it works so well.
</div>
<!-- 5 -->
<div class="col-lg-4 col-md-6 feature">
<h3>General</h3>
<p>Julia provides <a href="https://docs.julialang.org/en/v1/manual/networking-and-streams/">asynchronous I/O</a>, <a href="https://docs.julialang.org/en/v1/manual/metaprogramming/">metaprogramming</a>, <a href="https://github.com/JuliaDebug/Debugger.jl">debugging</a>, <a href="https://docs.julialang.org/en/v1/stdlib/Logging/">logging</a>, <a href="https://docs.julialang.org/en/v1/manual/profile/">profiling</a>, a <a href="https://docs.julialang.org/en/v1/stdlib/Pkg/index.html">package manager</a>, and more. One can build entire <a href="https://www.youtube.com/watch?v=uLhXgt_gKJc">Applications and Microservices</a> in Julia.</p>
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<div class="col-lg-4 col-md-6 feature">
<h3>Open source</h3>
<p>Julia is an open source project with over 1,000 contributors. It is made available under the <a href="https://github.com/JuliaLang/julia/blob/master/LICENSE.md">MIT license</a>. The <a href="https://github.com/JuliaLang/julia">source code</a> is available on GitHub.</p>
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<a class="btn btn-sm btn-outline-primary" href="/learning/code-examples/">See Julia Code Examples</a>
<a class="btn btn-sm btn-outline-primary" href="/learning/tryjulia">Try Julia In Your Browser</a>
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<h2 class="lead secondary-heading">Ecosystem</h2>
</div>
<div class="col-lg-4 col-md-3 ecosystem"><hr/></div>
</div>
<br>
<div class="row">
<ul class="nav nav-tabs nav-justified" id="ecosystem-tab" role="tablist">
<!-- Tab 1 -->
<li class="nav-item">
<a class="nav-link active" id="viz-tab" data-toggle="tab" href="#tab-viz" role="tab" aria-controls="viz" aria-selected="true">Visualization</a>
</li>
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<!-- Tab 3 -->
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<a class="nav-link" id="ds-tab" data-toggle="tab" href="#tab-ds" role="tab" aria-controls="ds" aria-selected="false">Data Science</a>
</li>
<!-- Tab 4 -->
<li class="nav-item">
<a class="nav-link" id="math-tab" data-toggle="tab" href="#tab-math" role="tab" aria-controls="math" aria-selected="false">Machine Learning</a>
</li>
<!-- Tab 5 -->
<li class="nav-item">
<a class="nav-link" id="sci-tab" data-toggle="tab" href="#tab-sci" role="tab" aria-controls="sci" aria-selected="false">Scientific Domains</a>
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<!-- Tab 6 -->
<li class="nav-item">
<a class="nav-link" id="computing-tab" data-toggle="tab" href="#tab-computing" role="tab" aria-controls="computing" aria-selected="false">Parallel Computing</a>
</li>
</ul>
</div>
<!-- TAB DETAILS -- GENERAL -->
<div class="tab-content" id="ecosystem-tab-content">
<div class="tab-pane" id="tab-general" role="tabpanel" aria-labelledby="general-tab">
<h3 class="tab-title">General Computing</h3>
<img src="/assets/infra/minesweeper.png" class="pb-2 ecosystem-image" style="height: 280px" alt="minesweeper gameover" />
<div class="container-fluid">
<h4>Build, Deploy or Embed Your Code</h4>
<p>
Julia makes it possible to build complete applications. Write web UIs with <a href="https://github.com/plotly/Dash.jl">Dash.jl</a> and <a href="https://github.com/GenieFramework/Genie.jl">Genie.jl</a> or native UIs with <a href="https://github.com/JuliaGraphics/Gtk.jl">GTK.jl</a>. Pull data from <a href="https://juliadatabases.org">a variety of databases</a>. Build shared libraries and executables with <a href="https://github.com/JuliaLang/PackageCompiler.jl">PackageCompiler</a>. Deploy on a webserver with <a href="https://github.com/JuliaWeb/HTTP.jl">HTTP.jl</a> or embedded devices. Powerful shell integration make it easy to <a href="https://docs.julialang.org/en/v1/manual/running-external-programs/">managing other processes</a>.
</p>
<p>
Julia has foreign function interfaces for
<a href = "https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/">C, Fortran</a>,
<a href = "https://github.com/JuliaInterop/CxxWrap.jl">C++</a>,
<a href = "https://github.com/JuliaPy/PyCall.jl">Python</a>,
<a href = "https://github.com/JuliaInterop/RCall.jl">R</a>,
<a href = "https://github.com/JuliaInterop/JavaCall.jl">Java</a>,
<a href = "https://github.com/JuliaInterop/MathLink.jl">Mathematica</a>,
<a href = "https://github.com/JuliaInterop/MATLAB.jl">Matlab</a>,
and many other languages. Julia can also be embedded in other programs through its <a href="https://docs.julialang.org/en/v1/manual/embedding/">embedding API</a>. Julia's <a href="https://github.com/JuliaLang/PackageCompiler.jl">PackageCompiler</a> makes it possible to build binaries from Julia programs that can be integrated into larger projects. Python programs can call Julia using <a href="https://github.com/JuliaPy/pyjulia">PyJulia</a>. R programs can do the same with <a href="https://cran.r-project.org/web/packages/JuliaCall/index.html">R's JuliaCall</a>, which is demonstrated by <a href="https://rpubs.com/dmbates/377897">calling MixedModels.jl from R</a>. Mathematica supports <a href="https://reference.wolfram.com/language/ref/externalevaluationsystem/Julia.html">calling Julia through its External Evaluation System</a>.
</p>
</div>
</div>
<!-- TAB DETAILS -- PARALLEL -->
<div class="tab-pane" id="tab-computing" role="tabpanel" aria-labelledby="computing-tab">
<h3 class="tab-title">Parallel Computing</h3>
<div class="row">
<div class="col-lg-12">
<img src="/assets/infra/parallel-prefix.png" class="pb-2 ecosystem-image" style="height: 280px" alt="parallel prefix graphical result" />
<div class="container-fluid">
<h4>
Parallel and Heterogeneous Computing
</h4>
<p>
Julia is designed for parallelism, and provides built-in primitives for parallel computing at every level:
<a href = "https://github.com/chriselrod/LoopVectorization.jl"> instruction level parallelism</a>,
<a href = "https://docs.julialang.org/en/v1/manual/multi-threading/">multi-threading</a>,
<a href = "https://juliagpu.org">GPU computing</a>, and
<a href = "https://docs.julialang.org/en/v1/manual/distributed-computing/">distributed computing</a>.
The <a href="https://github.com/jeff-regier/Celeste.jl">Celeste.jl </a> project <a href = "https://arxiv.org/pdf/1801.10277.pdf" title = "PDF paper on Cataloging the Visible Universe through Bayesian Inference at Petascale hosted on arxiv.org"> achieved 1.5 PetaFLOP/s</a> on the <a href="https://cs.lbl.gov/news-media/news/2016/celeste-enhancements-create-new-opportunities-in-sky-surveys/">Cori supercomputer at NERSC</a> using 650,000 cores.
</p>
<p>
The Julia compiler can also generate native code for <a href="https://juliagpu.org">GPUs</a>. Packages such as <a href="https://github.com/JuliaParallel/DistributedArrays.jl">DistributedArrays.jl</a> and <a href="https://github.com/JuliaParallel/Dagger.jl">Dagger.jl</a> provide higher levels of abstraction for parallelism.
Distributed Linear Algebra is provided by packages like <a href="https://github.com/JuliaParallel/Elemental.jl">Elemental.jl</a> and <a href="https://github.com/JuliaLinearAlgebra/TSVD.jl">TSVD.jl</a>. MPI style parallelism is also available through <a href="https://github.com/JuliaParallel/MPI.jl">MPI.jl</a>.
</p>
</div>
</div>
</div>
</div>
<!-- TAB DETAILS -- MACHINE LEARNING -->
<div class="tab-pane" id="tab-math" role="tabpanel" aria-labelledby="math-tab">
<h3 class="tab-title">Machine Learning</h3>
<div class="row">
<div class="col-lg-12">
<img src="/assets/infra/cartpole.gif" class="pb-2 ecosystem-image" style="width: 400px; height: 267px" alt="cartpole reinforcement learning problem visualization" />
<div class="container">
<h4>
Scalable Machine Learning
</h4>
<p>
The <a href="https://github.com/alan-turing-institute/MLJ.jl">MLJ.jl</a> package provides a unified interface to common machine learning algorithms, which include
<a href = "https://github.com/JuliaStats/GLM.jl">generalized linear models</a>, <a href = "https://github.com/dmlc/XGBoost.jl">decision trees</a>, and <a href = "https://github.com/JuliaStats/Clustering.jl">clustering</a>.
<a href = "https://github.com/FluxML/Flux.jl">Flux.jl</a> and <a href = "https://github.com/denizyuret/Knet.jl">Knet.jl</a> are powerful packages for Deep Learning.
Packages such as <a href="https://github.com/FluxML/Metalhead.jl">Metalhead</a>, <a href="https://github.com/r3tex/ObjectDetector.jl">ObjectDetector</a>, and <a href="https://github.com/JuliaText/TextAnalysis.jl">TextAnalysis.jl</a> provide ready to use pre-trained models for common tasks.
<a href="https://github.com/jonathan-laurent/AlphaZero.jl">AlphaZero.jl</a> provides a high peformance implementation of the reinforcement learning algorithms from AlphaZero. <a href="https://turing.ml">Turing.jl</a> is a best in class package for probabilistic programming.
</p>
</div>
</div>
</div>
</div>
<!-- TAB DETAILS -- SCICOMP -->
<div class="tab-pane" id="tab-sci" role="tabpanel" aria-labelledby="sci-tab">
<h3 class="tab-title">Scientific Computing</h3>
<div class="row">
<div class="col-lg-12">
<img src="/assets/infra/lorenz.gif" class="pb-2 ecosystem-image" style="width: 400px;" alt="Lorenz Attractor visualization" />
<div class="container">
<h4>
Rich Ecosystem for Scientific Computing
</h4>
<p>Julia 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, such as the state-of-the-art differential equations ecosystem <a href = "https://github.com/SciML/DifferentialEquations.jl">(DifferentialEquations.jl)</a>, optimization tools (<a href = "https://github.com/jump-dev/JuMP.jl">JuMP.jl</a> and <a href = "https://github.com/JuliaNLSolvers/Optim.jl">Optim.jl</a>), iterative linear solvers <a href = "https://github.com/JuliaMath/IterativeSolvers.jl">(IterativeSolvers.jl)</a>, Fast Fourier transforms <a href = "https://github.com/JuliaMath/AbstractFFTs.jl">(AbstractFFTs.jl)</a>, and much more. General purpose simulation frameworks are available for <a href="https://sciml.ai">Scientific Machine Learning</a>, <a href = "https://github.com/QuantumBFS/Yao.jl">Quantum computing</a> and much more.</p>
<p>Julia also offers a number of domain-specific ecosystems, such as in biology <a href = "https://github.com/BioJulia"> (BioJulia)</a>, operations research <a href = "https://jump.dev"> (JuMP Dev)</a>, image processing <a href = "https://juliaimages.github.io/latest/">(JuliaImages)</a>, quantum physics <a href = "https://github.com/QuantumBFS">(QuantumBFS)</a>, nonlinear dynamics <a href = "https://github.com/JuliaDynamics"> (JuliaDynamics)</a>, quantitative economics <a href = "https://github.com/QuantEcon">(QuantEcon)</a>, astronomy <a href = "https://juliaastro.github.io"> (JuliaAstro)</a> and ecology <a href = "https://github.com/EcoJulia">(EcoJulia)</a>. With a set of highly enthusiastic developers and maintainers, the scientific ecosystem in Julia continues to grow rapidly. </p>
</div>
</div>
</div>
</div>
<!-- TAB DETAILS -- DATA SCIENCE -->
<div class="tab-pane" id="tab-ds" role="tabpanel" aria-labelledby="ds-tab">
<h3 class="tab-title">Data Science</h3>
<div class="row">
<div class="col-lg-12">
<img src="/assets/infra/onlinestats.gif" class="pb-2 ecosystem-image" style="width: 400px;" alt="Visualization of weighted data changing as more data is plotted" />
<div class="container">
<h4>Interact with your Data</h4>
<p>The Julia data ecosystem provides <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> to work with datasets, and perform common data manipulations. <a href="https://github.com/JuliaData/CSV.jl">CSV.jl</a> is a fast multi-threaded package to read CSV files and integration with the Arrow ecosystem is in the works with <a href="https://github.com/JuliaData/Arrow.jl">Arrow.jl</a>. Online computations on streaming data can be performed with <a href = "https://github.com/joshday/OnlineStats.jl">OnlineStats.jl</a>.
The <a href = "https://www.queryverse.org/">Queryverse</a> provides query, file IO and visualization functionality. In addition to working with tabular data, the <a href = "https://juliagraphs.github.io">JuliaGraphs</a> packages make it easy to work with combinatorial data.</p>
<p>Julia can work with almost all databases using <a href = "https://github.com/JuliaDatabases/JDBC.jl">JDBC.jl</a> and <a href = "https://github.com/JuliaDatabases/ODBC.jl">ODBC.jl</a> drivers. In addition, it also integrates with the Spark ecosystem through <a href = "https://github.com/dfdx/Spark.jl">Spark.jl</a>.</p>
</div>
</div>
</div>
</div>
<!-- TAB DETAILS -- VISUALIZATION -->
<div class="tab-pane active" id="tab-viz" role="tabpanel" aria-labelledby="viz-tab">
<h3 class="tab-title">Visualization</h3>
<div class="row">
<div class="col-lg-12">
<div class="container">
<img src="/assets/infra/waves.gif" class="pb-2 ecosystem-image" style="width: 400px;" alt="Visualization of waves in 3D, as a heatmap, and on the x y axis" />
<h4>Data Visualization and Plotting</h4>
<p>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.</p>
<p><a href="https://github.com/JuliaPlots/Plots.jl">Plots.jl</a> is a visualization interface and toolset. It provides a common API across various <a href="https://docs.juliaplots.org/latest/backends/">backends</a>, like <a href="https://github.com/jheinen/GR.jl">GR.jl</a>, <a href="https://github.com/JuliaPy/PyPlot.jl">PyPlot.jl</a>, and <a href="https://github.com/JuliaPlots/PlotlyJS.jl">PlotlyJS.jl</a>. <a href="https://github.com/JuliaPlots/Makie.jl">Makie.jl</a> is a sophisticated package for complex graphics and animations. Users who are used to "grammar of graphics" plotting APIs should take a look at <a href="https://github.com/GiovineItalia/Gadfly.jl">Gadfly.jl</a>. <a href="https://github.com/queryverse/VegaLite.jl">VegaLite.jl</a> provides the <a href="https://vega.github.io/vega-lite/">Vega-Lite</a> grammar of interactive graphics interface as a Julia package. For those who do not wish to leave the comfort of the terminal, there is also <a href="https://github.com/Evizero/UnicodePlots.jl">UnicodePlots.jl</a>.</p>
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