Julia Language

Julia Language is dynamically-typed, feels like a scripting language, and has good support for interactive use. Julia has a rich language of descriptive data types, and type declarations can be used to clarify and solidify programs. The programs compile to efficient native code for multiple platforms via LLVM.

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. It has high-level syntax, making it an accessible language for programmers from any background or experience level. Julia Language is free for everyone to use, and all source code is publicly viewable on GitHub.

It has been downloaded over 10 million times and the community has registered over 2,000 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.

Features and Highlights

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.

Build, Deploy or Embed Your Code
The app 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.

Interact with your Data
The 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, It provides you a rich variety of tools. 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.

Scalable Machine Learning
It 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.

Rich Ecosystem for Scientific Computing
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 the app, such as the state-of-the-art differential equations ecosystem (DifferentialEquations.jl), optimization tools (JuMP.jl and Optim.jl), iterative linear solvers (IterativeSolvers.jl), a robust framework for Fourier transforms (AbstractFFTs.jl), a general-purpose quantum simulation framework (Yao.jl), and many more, that can drive all your simulations.

Parallel and Heterogeneous Computing
The language 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 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.