Meet the 2018 community prize winners.
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.
Julia has a rich language of descriptive datatypes, and type declarations can be used to clarify and solidify programs.
Julia uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. The standard library provides asynchronous I/O, process control, logging, profiling, a package manager, and more.
Julia excels at numerical computing. Its syntax is great for math, many numeric datatypes are supported, and parallelism is available out of the box. Julia's multiple dispatch is a natural fit for defining number and array-like datatypes.
Julia packages naturally work well together. Matrices of unit quantities, or data table columns of currencies and colors, just work — and with good performance.
Julia lets you write UIs, statically compile your code, or even deploy it on a webserver. It also has powerful shell-like capabilities for managing other processes. It provides Lisp-like macros and other metaprogramming facilities.
Julia has foreign function interfaces for C/Fortran, C++, Python, R, Java, and many other languages. Julia can also be embedded in other programs through its embedding API. Specifically, 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.
Julia is designed for parallelism, and provides built-in primitives for parallel computing at every level: instruction level parallelism, multi-threading 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 various hardware accelerators, such as GPUs and Xeon Phis. Packages such as DistributedArrays.jl and Dagger.jl provide higher levels of abstraction for parallelism.
Julia provides powerful tools for deep learning (Flux.jl and Knet.jl), machine learning and AI. Julia’s mathematical syntax makes it an ideal way to express algorithms just as they are written in papers, build trainable models with automatic differentiation, GPU acceleration and support for terabytes of data with JuliaDB.
Julia's rich machine learning and statistics ecosystem includes capabilities for generalized linear models, decision trees, and clustering. You can also find packages for Bayesian Networks and Markov Chain Monte Carlo.
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 (DifferntialEquations.jl), optimization tools (JuMP.jl and Optim.jl), iterative linear solvers (IterativeSolvers.jl) and many more, that can drive all your simulations.Julia also offers a number of domain-specific ecosystems, such as in biology (BioJulia), operations research (JuliaOpt), quantum physics (QuantumOptics), nonlinear dynamics (JuliaDynamics), quantitative economics (QuantEcon), astronomy (JuliaAstro) and ecology (EcoJulia). With a set of highly enthusiastic developers and maintainers from various parts of the scientific community, this ecosystem will only continue to get bigger and bigger.
The Julia data ecosystem lets you load multidimensional datasets quickly, perform aggregations, joins and preprocessing operations in parallel, and save them to disk in efficient formats. You can also perform online computations on streaming data with OnlineStats.jl. Whether you're looking for the convenient and familiar DataFrames, or a new approach with JuliaDB, Julia provides you a rich variety of tools. The Queryverse package acts a meta package through which you can access these tools with Julian APIs. In addition to working with tabular data, the JuliaGraphs packages make it easy to work with combinatorial data.
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. Users who prefer a more grammar of graphics style API might like the pure Julia Gadfly.jl plotting package. For those who do not wish to leave the comfort of the terminal, there is also UnicodePlots.jl.
Julia has been downloaded over 2 million times and the Julia community has developed over 1,900 Julia packages. 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!
Congratulations to Tim Besard, Katharine Hyatt, Chris Rackauckas and Elliot Saba