Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM.
Julia is dynamically typed, feels like a scripting language, and has good support for interactive use.
Reproducible environments make it possible to recreate the same Julia environment every time, across platforms, with pre-built binaries.
Julia 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.
Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. One can build entire Applications and Microservices in Julia.
Julia 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.
Julia 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 has foreign function interfaces for C, Fortran, C++, Python, R, Java, Mathematica, Matlab, and many other languages. Julia can also be embedded in other programs through its embedding API. Julia's PackageCompiler makes it possible to build binaries from Julia programs that can be integrated into larger projects. Python programs can call Julia 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.
Julia 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 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.
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 peformance implementation of the reinforcement learning algorithms from AlphaZero. Turing.jl is a best in class package for probabilistic programming.
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 (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 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 continues to grow rapidly.
The Julia 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 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 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 package. For those who do not wish to leave the comfort of the terminal, there is also UnicodePlots.jl.
Julia has been downloaded over 40 million times and the Julia community has registered over 9,100 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!
Highlights of the Julia 1.8 release.
JuliaCon 2022 Highlights
Doing small network scientific machine learning in Julia 5x faster than PyTorch ...