Julia Language (64-bit) Download (Last 2021) for Windows 10, 8, 7

Julia uses multiple mission as an example, making it easy to express multiple object-oriented and functional programming patterns. The standard library provides asynchronous I / O, process control, logging, profiling, package management and more. It has a high level of syntax, making the language accessible to developers from any background or level of experience. Julia’s tongue is free for everyone and all source codes are publicly visible GitHub.

It has been downloaded over 10 million times and the community has registered over 2,000 packages for community use. These include various math libraries, data handling tools, and general purpose computer kits. Apart from these, you can easily use libraries from Python, R, C / Fortran, C ++ and Java.

Features and best moments

Data visualization and design
Data visualization has a complex history. Software design exchanges features and simplicity, speed and beauty, as well as static and dynamic interface. Some packages create a screen and never change it, while others make real-time updates.

Create, develop or embed your password
The application allows you to write user interfaces, statically compose your password, or even deploy it to a web server. It also has powerful shell-like capabilities for managing other processes. Provides Lisp macros and other post-programming installations.

Interact with your data
The data ecosystem allows you to quickly load multidimensional data sets, run aggregations, connections, and preprocessing processes in parallel, and save them to disk in efficient formats. You can also perform web streams with OnlineStats.jl. Whether you are looking for convenient and familiar DataFrames or a new approach with JuliaDBIt provides you with a rich variety of tools. Queryverse provides query, IO, and visualization functions. In addition to working with table data, the Julia Packages make it easy to work with combined data.

Evolutionary mechanical learning
Provides powerful tools for in-depth learning (Flux.jl and Knet.jl), machine learning and AI. Julia’s mathematical syntax makes it an ideal way to express algorithms as written in documents, create educational models with auto-differentiation, GPU acceleration and support for terabyte data with JuliaDB.

Rich ecosystem of scientific informatics
Julia has been designed from the start as very good at arithmetic and computer science. This can be seen in the abundance of scientific tools written in the application, such as the advanced technological ecosystem of equations (DifferentialEquations.jl), optimization tools (JuMP.jl and Optim.jl), repetitive linear solvents (IterativeSolvers.jl), a powerful Fourier Transform Framework (AbstractFFTs.jl), a general purpose quantum simulation framework (Yao.jl), and more that can drive all your simulations.

Parallel and heterogeneous calculation
The language is designed for parallelism and provides built-in primitives for parallel computers at every level: teaching-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 GPU and Xeon Phis. Packages such as DistributedArrays.jl and Dagger.jl provide higher levels of removal for parallelism.