Julia is a high-performance programming language, which is also meant to be easy (was considered a contradictory goal before), originally made with technical computing or with science in mind (like MATLAB), but it's a fully general-purpose language similar to e.g. Python, can be used for e.g. web development to building artificial intelligence.
Julia is a high-level language, which means a programmer can focus on what to do, but does not require knowledge of computer hardware. Writing programs in Julia takes less time than in some other languages.
Some state-of-the-art software has already been written in Julia, because it's considered easier to do then in the other popular languages. Some of it can also be used from other languages like Python or R. Julia was designed to be unusually easy to work with other languages, i.e. to benefit from code already written in other languages, to "reuse" their code.
Julia drew inspiration from other programming languages like Python, MATLAB, R, Ruby, Perl, Mathematica, Lisp, and C.
Julia is supported by notebooks like Pluto.jl, Jupyter, and since 2025 Google Colab supports Julia natively.
It is recommended to install Julia with juliaup (which installs and takes care of Julia version updates), it is recommended NOT to install Julia through Linux distro's package managers. Juliaup is available in the Microsoft Store under the name Julia, which installs both.
Julia supports calling C (and also e.g. Fortran or Rust) without glue code, using the keyword ccall. But it's also possible to call e.g. C++, Python, Java, MATLAB and R with help of Julia libraries for that purpose, and it's also possible to use those languages to call Julia, to reuse Julia libraries (that some of are state-of-the-art, such as SciML, made for scientific machine learning, which includes e.g. that DifferentialEquations.jl package).
References
↑"Smoothing data with Julia's @generated functions". 5 November 2015. Retrieved 9 December 2015. Julia's generated functions are closely related to the multistaged programming (MSP) paradigm popularized by Taha and Sheard, which generalizes the compile time/run time stages of program execution by allowing for multiple stages of delayed code execution.
↑"Julia". Julia. NumFocus project. Retrieved 9 December 2016. Julia's Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for ...
↑"NVIDIA CUDA ⋅ JuliaGPU". juliagpu.org. Archived from the original on 29 January 2022. Retrieved 17 January 2022. we have shown the performance to approach and even sometimes exceed that of CUDA C on a selection of applications from the Rodinia benchmark suite