Julia (programming language)

Work on Julia began in 2009, when Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman set out to create a free language that was both high-level and fast. On 14 February 2012, the team launched a website with a blog post explaining the language's mission. In an interview with InfoWorld in April 2012, Karpinski said of the name "Julia": "There's no good reason, really. It just seemed like a pretty name." Bezanson said he chose the name on the recommendation of a friend,{{Cite web |last1=Torre |first1=Charles |title=Stefan Karpinski and Jeff Bezanson on Julia |url=https://channel9.msdn.com/Blogs/Charles/Stefan-Karpinski-and-Jeff-Bezanson-Julia-Programming-Language |website=Channel 9 |publisher=MSDN |access-date=4 December 2018 |archive-date=4 December 2018 |archive-url=https://web.archive.org/web/20181204102053/https://channel9.msdn.com/Blogs/Charles/Stefan-Karpinski-and-Jeff-Bezanson-Julia-Programming-Language |url-status=live }} then years later wrote:

{{blockquote|Maybe julia stands for "Jeff's uncommon lisp is automated"?{{Cite web |last1=Bezanson |first1=Jeff |title=CAS Benchmarks |url=https://discourse.julialang.org/t/cas-benchmarks-symbolics-jl-and-maxima/58359/17 |website=discourse.julialang.org |date=2 April 2021 |access-date=2 April 2021 |archive-date=2 April 2021 |archive-url=https://web.archive.org/web/20210402224346/https://discourse.julialang.org/t/cas-benchmarks-symbolics-jl-and-maxima/58359/17 |url-status=live }}}}

Julia's syntax is now considered stable, since version 1.0 in 2018, and Julia has a backward compatibility guarantee for 1.x and also a stability promise for the documented (stable) API, while in the years before in the early development prior to 0.7 the syntax (and semantics) was changed in new versions. All of the (registered package) ecosystem uses the new and improved syntax, and in most cases relies on new APIs that have been added regularly, and in some cases minor additional syntax added in a forward compatible way e.g. in Julia 1.7.

In the 10 years since the 2012 launch of pre-1.0 Julia, the community has grown. The Julia package ecosystem has over 11.8 million lines of code (including docs and tests).{{Cite web |title=Newsletter August 2021 - Julia Computing Completes $24 Million Series A Fundraise and Former Snowflake CEO Bob Muglia Joins Julia Computing Board of Directors - JuliaHub |url=https://juliahub.com/blog/2021/08/newsletter-august/ |access-date=2022-11-16 |website=juliahub.com |archive-date=16 November 2022 |archive-url=https://web.archive.org/web/20221116115927/https://juliahub.com/blog/2021/08/newsletter-august/ |url-status=live }} The JuliaCon academic conference for Julia users and developers has been held annually since 2014 with JuliaCon2020{{Cite web |title=JuliaCon 2020 |url=https://juliacon.org/2020/ |access-date=2023-10-06 |website=JuliaCon 2020 |language=en |archive-date=12 October 2023 |archive-url=https://web.archive.org/web/20231012155924/https://juliacon.org/2020/ |url-status=live }} welcoming over 28,900 unique viewers,{{Cite web|date=2020-08-11|title=JuliaCon 2020 Wrap-up|url=https://julialang.org/blog/2020/08/juliacon-2020-wrapup/#outcomes|access-date=2020-12-20|website=julialang.org|archive-date=30 November 2020|archive-url=https://web.archive.org/web/20201130113640/https://julialang.org/blog/2020/08/juliacon-2020-wrapup/#outcomes|url-status=live}} and then JuliaCon2021 breaking all previous records (with more than 300 JuliaCon2021 presentations available for free on YouTube, up from 162 the year before), and 43,000 unique viewers during the conference.{{Cite web|title=JuliaCon 2021 Highlights|url=https://julialang.org/blog/2021/08/juliacon-highlights/|access-date=2021-09-06|website=julialang.org|language=en|archive-date=6 September 2021|archive-url=https://web.archive.org/web/20210906222719/https://julialang.org/blog/2021/08/juliacon-highlights/|url-status=live}}

Three of the Julia co-creators are the recipients of the 2019 James H. Wilkinson Prize for Numerical Software (awarded every four years) "for the creation of Julia, an innovative environment for the creation of high-performance tools that enable the analysis and solution of computational science problems."{{Cite web |title=Julia language co-creators win James H. Wilkinson Prize for Numerical Software |url=https://news.mit.edu/2018/julia-language-co-creators-win-james-wilkinson-prize-numerical-software-1226 |access-date=2019-01-22 |website=MIT News |date=26 December 2018 |archive-date=28 January 2019 |archive-url=https://web.archive.org/web/20190128230208/http://news.mit.edu/2018/julia-language-co-creators-win-james-wilkinson-prize-numerical-software-1226 |url-status=live }} Also, Alan Edelman, professor of applied mathematics at MIT, has been selected to receive the 2019 IEEE Computer Society Sidney Fernbach Award "for outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language."{{Cite press release |url=https://www.computer.org/press-room/2019-news/2019-ieee-fernbach-award-edelman |title=Alan Edelman of MIT Recognized with Prestigious 2019 IEEE Computer Society Sidney Fernbach Award {{!}} IEEE Computer Society |language=en-US |date=1 October 2019 |access-date=2019-10-09 |archive-date=9 October 2019 |archive-url=https://web.archive.org/web/20191009130947/https://www.computer.org/press-room/2019-news/2019-ieee-fernbach-award-edelman |url-status=live }}

Both Julia 0.7{{Cite web|url=https://discourse.julialang.org/t/what-is-julia-0-7-how-does-it-relate-to-1-0/9994|title=What is Julia 0.7? How does it relate to 1.0?|website=JuliaLang|date=26 March 2018|language=en|access-date=2018-10-17|archive-date=27 July 2018|archive-url=https://web.archive.org/web/20180727054635/https://discourse.julialang.org/t/what-is-julia-0-7-how-does-it-relate-to-1-0/9994|url-status=live}} and version 1.0 were released on 8 August 2018. Work on Julia 0.7 was a "huge undertaking" (e.g., because of an "entirely new optimizer"), and some changes were made to semantics, e.g. the iteration interface was simplified.{{Cite web |url=https://julialang.org/blog/2018/07/iterators-in-julia-0.7 |title=Writing Iterators in Julia 0.7 |first=Eric |last=Davies |website=julialang.org |access-date=2018-08-05 |archive-date=6 August 2018 |archive-url=https://web.archive.org/web/20180806024646/https://julialang.org/blog/2018/07/iterators-in-julia-0.7 |url-status=live }}

Julia 1.6 was the largest release since 1.0, and it was the long-term support (LTS) version for the longest time, faster on many fronts, e.g. introduced parallel precompilation and faster loading of packages, in some cases "50x speedup in load times for large trees of binary artifacts".{{Cite web|author1=Jeff Bezanson|author2=Stefan Karpinski|author3=Viral Shah|author4=Alan Edelman|display-authors=etal|title=Julia 1.6 Highlights|url=https://julialang.org/blog/2021/03/julia-1.6-highlights/|access-date=2021-03-26|website=julialang.org|language=en|archive-date=26 March 2021|archive-url=https://web.archive.org/web/20210326155220/https://julialang.org/blog/2021/03/julia-1.6-highlights/|url-status=live}} Since 1.7 Julia development is back to time-based releases.{{Cite web|title=Upgrade to OpenBLAS 0.3.13 · Pull Request #39216 · JuliaLang/julia|url=https://github.com/JuliaLang/julia/pull/39216#issuecomment-816285199|access-date=2021-04-26|website=GitHub|language=en|quote=Given that 1.7 is not too far away (timed releases going forward)|archive-date=23 March 2022|archive-url=https://web.archive.org/web/20220323054712/https://github.com/JuliaLang/julia/pull/39216#issuecomment-816285199|url-status=live}} Julia 1.7 was released in November 2021 with many changes, e.g. a new faster random-number generator and Julia 1.7.3 fixed e.g. at least one security issue.{{Cite web |title=[Zlib_jll] Update to v1.2.12+3 by giordano · Pull Request #44810 · JuliaLang/julia |url=https://github.com/JuliaLang/julia/pull/44810 |access-date=2022-05-25 |website=GitHub |language=en |archive-date=25 May 2022 |archive-url=https://web.archive.org/web/20220525225959/https://github.com/JuliaLang/julia/pull/44810 |url-status=live }} Julia 1.8 was released in 2022 and 1.8.5 in January 2023,{{Cite web |title=Backports for Julia 1.8.5 by KristofferC · Pull Request #48011 · JuliaLang/julia |url=https://github.com/JuliaLang/julia/pull/48011 |access-date=2023-01-08 |website=GitHub |language=en |archive-date=4 January 2023 |archive-url=https://web.archive.org/web/20230104192611/https://github.com/JuliaLang/julia/pull/48011 |url-status=live }} with 1.8.x improvements for distributing Julia programs without source code, and compiler speedup, in some cases by 25%,{{Cite web |title=compiler: speed up bootstrapping time by 25% by aviatesk · Pull Request #41794 · JuliaLang/julia |url=https://github.com/JuliaLang/julia/pull/41794 |access-date=2022-03-03 |website=GitHub |language=en |quote=the bootstrapping took about 80 seconds previously, but on this PR the time is reduced to about 60 seconds. |archive-date=3 March 2022 |archive-url=https://web.archive.org/web/20220303155817/https://github.com/JuliaLang/julia/pull/41794 |url-status=live }} and more controllable inlining (i.e. now also allowing applying @inline at the call site, not just on the function itself). Julia 1.9 was released on 7 May 2023. It has many improvements, such as the ability to precompile packages to native machine code (older Julia versions also have precompilation for packages, but only partial, never fully to native code, so those earlier versions had a "first use" penalty, slowing down while waiting to fully compile). Precompiled packages, since version 1.9, can be up to hundreds of times faster on first use (e.g. for CSV.jl and DataFrames.jl), and to improve precompilation of packages a new package PrecompileTools.jl has been introduced. Julia 1.10 was released on 25 December 2023 with many new features, e.g. parallel garbage collection, and improved package load times and a new parser, i.e. it rewritten in Julia, with better error messages and improved stacktrace rendering.{{Cite web |title=julia/HISTORY.md at master · JuliaLang/julia |url=https://github.com/JuliaLang/julia/blob/master/HISTORY.md#julia-v110-release-notes |access-date=2024-12-01 |website=GitHub |language=en}}

Julia 1.11 was released on 7 October 2024 (and 1.11.5 on 14 April 2025), and with it 1.10.5 became the next long-term support (LTS) version (i.e. those are the only two supported versions), since replaced by 1.10.8 released on 10 March, and 1.6 is no longer an LTS version. Julia 1.11 adds e.g. parallel garbage collection and the new public keyword to signal safe public API (Julia users are advised to use such API, not internals, of Julia or packages, and package authors advised to use the keyword, generally indirectly, e.g. prefixed with the @compat macro, from Compat.jl, to also support older Julia versions, at least the LTS version). Julia 1.11.1 has much improved startup (over 1.11.0 that had a regression), and over 1.10, and this can be important for some benchmarks.

It's possible to compile very small binary executables using juliac which is available in the upcoming Julia 1.12 (now in beta3), and other compilers exist usable for older Julia versions, that also allows compiling though not to small binaries with the main one.

Since 2014,{{Cite web|url=https://juliacon.org/2014/|title=JuliaCon 2014|website=juliacon.org|access-date=2021-06-20}} the Julia Community has hosted an annual Julia Conference focused on developers and users. The first JuliaCon took place in Chicago and kickstarted the annual occurrence of the conference. Since 2014, the conference has taken place across a number of locations including MIT{{Cite web|url=https://news.mit.edu/2016/juliacon-draws-global-users-of-dynamic-programming-language-0718|title=JuliaCon 2016 at MIT|website=mit.edu|date=18 July 2016|access-date=2021-06-20|archive-date=24 June 2021|archive-url=https://web.archive.org/web/20210624201658/https://news.mit.edu/2016/juliacon-draws-global-users-of-dynamic-programming-language-0718|url-status=live}} and the University of Maryland, Baltimore.{{Cite web|url=https://technical.ly/baltimore/2019/07/23/juliacon-provides-the-stage-for-a-week-of-programming-talks-and-a-new-baltimore-company/|title=JuliaCon 2019 at UMB|website=technical.ly|date=23 July 2019|access-date=2021-06-20|archive-date=24 June 2021|archive-url=https://web.archive.org/web/20210624201651/https://technical.ly/baltimore/2019/07/23/juliacon-provides-the-stage-for-a-week-of-programming-talks-and-a-new-baltimore-company/|url-status=live}} The event audience has grown from a few dozen people to over 28,900 unique attendees{{Cite web|url=https://julialang.org/blog/2020/08/juliacon-2020-wrapup/#outcomes|title=JuliaCon 2020 wrap up|website=julialang.org|access-date=2021-06-20|archive-date=30 November 2020|archive-url=https://web.archive.org/web/20201130113640/https://julialang.org/blog/2020/08/juliacon-2020-wrapup/#outcomes|url-status=live}} during JuliaCon 2020, which took place virtually. JuliaCon 2021 also took place virtually{{Cite web|url=https://juliacon.org/2021/|title=JuliaCon 2021|website=Juliacon.org|access-date=2021-06-20|archive-date=20 June 2021|archive-url=https://web.archive.org/web/20210620034020/https://juliacon.org/2021/|url-status=live}} with keynote addresses from professors William Kahan, the primary architect of the IEEE 754 floating-point standard (which virtually all CPUs and languages, including Julia, use),{{Cite web |title=JuliaCon 2021 Highlights |url=https://julialang.org/blog/2021/08/juliacon-highlights/ |access-date=2022-03-03 |website=julialang.org |language=en |quote=This year's JuliaCon was the biggest and best ever, with more than 300 presentations available for free on YouTube, more than 20,000 registrations, and more than 43,000 unique YouTube viewers during the conference, up from 162 presentations, 10,000 registrations, and 28,900 unique YouTube viewers during last year's conference. |archive-date=6 September 2021 |archive-url=https://web.archive.org/web/20210906222719/https://julialang.org/blog/2021/08/juliacon-highlights/ |url-status=live }} Jan Vitek,{{Cite web|url=https://janvitek.org/|title=Jan Vitek Homepage|website=janvitek.org|access-date=2021-06-20|archive-date=22 January 2024|archive-url=https://web.archive.org/web/20240122214511/https://janvitek.org/|url-status=live}} Xiaoye Sherry Li, and Soumith Chintala, a co-creator of PyTorch.{{Cite web|url=https://soumith.ch/|title=Soumith Chintala Homepage|website=soumith.ch|access-date=2021-06-20|archive-date=24 June 2021|archive-url=https://web.archive.org/web/20210624202250/https://soumith.ch/|url-status=live}} JuliaCon grew to 43,000 unique attendees and more than 300 presentations (still freely accessible, plus for older years). JuliaCon 2022 will also be virtual held between July 27 and July 29, 2022, for the first time in several languages, not just in English.

The Julia language became a NumFOCUS fiscally sponsored project in 2014 in an effort to ensure the project's long-term sustainability.{{Cite web|url=https://numfocus.org/project/julia|title=Julia: NumFOCUS Sponsored Project since 2014|website=numfocus.org|access-date=2020-09-29|archive-date=28 September 2020|archive-url=https://web.archive.org/web/20200928023230/https://numfocus.org/project/julia|url-status=live}} Jeremy Kepner at MIT Lincoln Laboratory was the founding sponsor of the Julia project in its early days. In addition, funds from the Gordon and Betty Moore Foundation, the Alfred P. Sloan Foundation, Intel, and agencies such as NSF, DARPA, NIH, NASA, and FAA have been essential to the development of Julia.{{Cite web|url=https://julialang.org/research/|title=The Julia Language|website=julialang.org|access-date=2019-09-22|archive-date=26 July 2019|archive-url=https://web.archive.org/web/20190726215130/https://julialang.org/research/|url-status=live}} Mozilla, the maker of Firefox web browser, with its research grants for H1 2019, sponsored "a member of the official Julia team" for the project "Bringing Julia to the Browser",{{Cite web|url=https://www.zdnet.com/article/mozilla-is-funding-a-way-to-support-julia-in-firefox/|title=Mozilla is funding a way to support Julia in Firefox|last=Cimpanu|first=Catalin|website=ZDNet|language=en|access-date=2019-09-22|archive-date=10 July 2019|archive-url=https://web.archive.org/web/20190710012226/https://www.zdnet.com/article/mozilla-is-funding-a-way-to-support-julia-in-firefox/|url-status=live}} meaning to Firefox and other web browsers.{{Cite web|url=https://alpha.iodide.io/notebooks/225/|archive-url=https://web.archive.org/web/20190922161033/https://alpha.iodide.io/notebooks/225/|url-status=dead|archive-date=22 September 2019|title=Julia in Iodide|website=alpha.iodide.io|access-date=2019-09-22}}{{Cite web|url=https://iodide-project.github.io/docs/language_plugins/|title=Language plugins - Iodide Documentation|website=iodide-project.github.io|access-date=2019-09-22|archive-date=22 September 2019|archive-url=https://web.archive.org/web/20190922161121/https://iodide-project.github.io/docs/language_plugins/|url-status=live}}{{Cite web|url=https://mozilla-research.forms.fm/mozilla-research-grants-2019h1/forms/6510|title=Mozilla Research Grants 2019H1|website=Mozilla|language=en|quote=running language interpreters in WebAssembly. To further increase access to leading data science tools, we're looking for someone to port R or Julia to WebAssembly and to attempt to provide a level 3 language plugin for Iodide: automatic conversion of data basic types between R/Julia and Javascript, and the ability to share class instances between R/Julia and Javascript.|access-date=2019-09-22|archive-date=9 October 2019|archive-url=https://web.archive.org/web/20191009130024/https://mozilla-research.forms.fm/mozilla-research-grants-2019h1/forms/6510}}{{Cite web|title=Literate scientific computing and communication for the web: iodide-project/iodide|date=2019-09-20|url=https://github.com/iodide-project/iodide|publisher=iodide|quote=We envision a future workflow that allows you to do your data munging in Python, fit a quick model in R or JAGS, solve some differential equations in Julia, and then display your results with a live interactive d3+JavaScript visualization ... and all that within a single, portable, sharable, and hackable file.|access-date=2019-09-22|archive-date=24 August 2018|archive-url=https://web.archive.org/web/20180824140212/https://github.com/iodide-project/iodide|url-status=live}} The Julia language is also supported by individual donors on GitHub.{{Cite web|url=https://github.com/sponsors/JuliaLang|title=Sponsor the Julia Language|website=github.com|access-date=2021-06-05|archive-date=5 July 2021|archive-url=https://web.archive.org/web/20210705013745/https://github.com/sponsors/JuliaLang|url-status=live}}

JuliaHub, Inc. was founded in 2015 as Julia Computing, Inc. by Viral B. Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski and Keno Fischer.{{Cite web|url=https://juliacomputing.com/about-us|title=About Us – Julia Computing|website=juliacomputing.com|access-date=2017-09-12|archive-date=1 September 2019|archive-url=https://web.archive.org/web/20190901171449/https://juliacomputing.com/about-us|url-status=live}}{{Cite web |title=About Us - JuliaHub |url=https://juliahub.com/company/about-us/ |access-date=2022-11-16 |website=juliahub.com |archive-date=16 November 2022 |archive-url=https://web.archive.org/web/20221116112437/https://juliahub.com/company/about-us/ |url-status=live }}

In June 2017, Julia Computing raised US$4.6{{nbsp}}million in seed funding from General Catalyst and Founder Collective,{{Cite press release|url=https://juliacomputing.com/communication/2017/06/19/seed-funding.html|title=Julia Computing Raises $4.6M in Seed Funding|archive-url=https://web.archive.org/web/20190510040656/https://juliacomputing.com/communication/2017/06/19/seed-funding.html|archive-date=2019-05-10}} the same month was "granted $910,000 by the Alfred P. Sloan Foundation to support open-source Julia development, including $160,000 to promote diversity in the Julia community",{{Cite news|date=2017-06-26|title=Julia Computing Awarded $910,000 Grant by Alfred P. Sloan Foundation, Including $160,000 for STEM Diversity|url=https://juliacomputing.com/media/2017/06/26/sloan-grant.html|access-date=2020-07-28|website=juliacomputing.com|archive-date=3 August 2020|archive-url=https://web.archive.org/web/20200803093745/https://juliacomputing.com/media/2017/06/26/sloan-grant.html}} and in December 2019 the company got $1.1{{nbsp}}million funding from the US government to "develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation, and air conditioning (HVAC) systems in buildings".{{Cite web|title=DIFFERENTIATE—Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements|url=https://arpa-e.energy.gov/sites/default/files/documents/files/DIFFERENTIATE_Project_Descriptions_FINAL.pdf}} In July 2021, Julia Computing announced they raised a $24 million Series A round led by Dorilton Ventures,{{Cite news|title=Julia Computing raises $24 mln in funding round led by Dorilton Ventures|newspaper=Reuters|date=19 July 2021|url=https://www.reuters.com/technology/julia-computing-raises-24-mln-funding-round-led-by-dorilton-ventures-2021-07-19/|access-date=18 August 2021|archive-date=18 August 2021|archive-url=https://web.archive.org/web/20210818161926/https://www.reuters.com/technology/julia-computing-raises-24-mln-funding-round-led-by-dorilton-ventures-2021-07-19/|url-status=live}} which also owns {{nowrap|Formula 1}} team Williams Racing, that partnered with Julia Computing. Williams' Commercial Director said: "Investing in companies building best-in-class cloud technology is a strategic focus for Dorilton and Julia's versatile platform, with revolutionary capabilities in simulation and modelling, is hugely relevant to our business. We look forward to embedding Julia Computing in the world's most technologically advanced sport".{{Cite press release|title=Williams welcomes Julia Computing as Dorilton Ventures partner|url=https://www.williamsf1.com/news/2021/partners/williams-welcomes-julia-computing|access-date=2021-09-02|website=www.williamsf1.com|language=en|archive-date=2 September 2021|archive-url=https://web.archive.org/web/20210902092335/https://www.williamsf1.com/news/2021/partners/williams-welcomes-julia-computing|url-status=live}} In June 2023, JuliaHub received (again, now under its new name) a $13 million strategic new investment led by AE Industrial Partners HorizonX ("AEI HorizonX"). AEI HorizonX is a venture capital investment platform formed in partnership with The Boeing Company, which uses Julia.{{Cite press release |title=JuliaHub Receives $13 Million Strategic Investment from AE Industrial Partners HorizonX |url=https://info.juliahub.com/juliahub-receives-13-million-strategic-investment-from-ae-industrial-partners-horizonx |date=June 27, 2023 |access-date=2023-06-30 |website=info.juliahub.com |language=en}} Tim Holy's work (at Washington University in St. Louis's Holy Lab) on Julia 1.9 (improving responsiveness) was funded by the Chan Zuckerberg Initiative.

{{Wikibooks|Introducing Julia}}

Julia is a general-purpose programming language,{{Cite web

| title = The Julia Language

| type = official website

| url = https://julialang.org/

| quote = General Purpose [..] Julia lets you write UIs, statically compile your code, or even deploy it on a webserver.

| access-date = 9 December 2016

| archive-date = 21 February 2017

| archive-url = https://web.archive.org/web/20170221073014/http://julialang.org/

| url-status = live

}} while also originally designed for numerical/technical computing. It is also useful for low-level systems programming,{{Cite web |url=https://juliacon.org/2018/talks_workshops/42/

|archive-url=https://web.archive.org/web/20181105083419/http://juliacon.org/2018/talks_workshops/42/

|archive-date=5 November 2018

|first=Todd |last=Green

|title=Low-Level Systems Programming in High-Level Julia

|date=10 August 2018

|access-date=5 November 2018

}} as a specification language,{{Cite web|last=Moss|first=Robert|date=26 June 2015|title=Using Julia as a Specification Language for the Next-Generation Airborne Collision Avoidance System|url=https://juliacon.org/2015/images/juliacon2015_moss_v3.pdf|url-status=live|archive-url=https://web.archive.org/web/20150701182804/http://juliacon.org/talks.html|archive-date=1 July 2015|access-date=29 June 2015|quote=Airborne collision avoidance system}} high-level synthesis (HLS) tool (for hardware, e.g. FPGAs),{{Cite arXiv|title=High-level Synthesis using the Julia Language |eprint=2201.11522 |quote=We present a prototype Julia HLS tool, written in Julia, that transforms Julia code to VHDL.|last1=Biggs |first1=Benjamin |last2=McInerney |first2=Ian |last3=Kerrigan |first3=Eric C. |last4=Constantinides |first4=George A. |year=2022 |class=cs.SE }} and for web programming{{Cite press release|work=plotly|date=2020-10-26|title=Announcing Dash for Julia|url=https://medium.com/plotly/announcing-dash-for-julia-f017c90c6ef1|access-date=2021-09-02|language=en|archive-date=2 September 2021|archive-url=https://web.archive.org/web/20210902094440/https://medium.com/plotly/announcing-dash-for-julia-f017c90c6ef1|url-status=live}} at both server{{Cite web|url=https://medium.com/@richardanaya/how-to-create-a-multi-threaded-http-server-in-julia-ca12dca09c35|title=How to create a multi-threaded HTTP server in Julia|last=Anaya|first=Richard|date=2019-04-28|website=Medium|language=en|access-date=2019-07-25|quote=In summary, even though Julia lacks a multi-threaded server solution currently out of box, we can easily take advantage of its process distribution features and a highly popular load balancing tech to get full CPU utilization for HTTP handling.|archive-date=25 July 2019|archive-url=https://web.archive.org/web/20190725161314/https://medium.com/@richardanaya/how-to-create-a-multi-threaded-http-server-in-julia-ca12dca09c35|url-status=live}}{{Cite web|last=Anthoff|first=David|title=Node.js installation for julia|website=GitHub|date=2019-06-01|url=https://github.com/davidanthoff/NodeJS.jl|access-date=2019-07-25|archive-date=4 September 2024|archive-url=https://web.archive.org/web/20240904035046/https://github.com/davidanthoff/NodeJS.jl|url-status=live}} and client{{Cite web |date=2019-07-07 |title=Translate Julia to JavaScript |url=https://github.com/JuliaGizmos/JSExpr.jl |access-date=2019-07-25 |publisher=JuliaGizmos |archive-date=28 March 2019 |archive-url=https://web.archive.org/web/20190328210322/https://github.com/JuliaGizmos/JSExpr.jl |url-status=live }}{{Cite web |last=Fischer |first=Keno |date=2019-07-22 |title=Running julia on wasm |url=https://github.com/Keno/julia-wasm |access-date=2019-07-25 |website=GitHub |archive-date=21 November 2020 |archive-url=https://web.archive.org/web/20201121081548/https://github.com/keno/julia-wasm |url-status=live }} side.

The main features of the language are:

• Multiple dispatch: providing ability to define function behavior across combinations of argument types
• Dynamic type system: types for documentation, optimization, and dispatch
• Performance approaching that of statically-typed languages like C
• A built-in package manager
• Lisp-like macros and other metaprogramming facilities
• Designed for parallel and distributed computing
• Coroutines: lightweight green threading
• Automatic generation of code for different argument types
• Extensible conversions and promotions for numeric and other types

Multiple dispatch (also termed multimethods in Lisp) is a generalization of single dispatch{{snd}} the polymorphic mechanism used in common object-oriented programming (OOP) languages, such as Python, C++, Java, JavaScript, and Smalltalk{{snd}} that uses inheritance. In Julia, all concrete types are subtypes of abstract types, directly or indirectly subtypes of the Any type, which is the top of the type hierarchy. Concrete types can not themselves be subtyped the way they can in other languages; composition is used instead (see also inheritance vs subtyping).

By default, the Julia runtime must be pre-installed as user-provided source code is run. Alternatively, Julia (GUI) apps can be quickly bundled up into a single file with AppBundler.jl{{Cite web |title=AppBundler.jl |date=2023-12-13 |url=https://github.com/PeaceFounder/AppBundler.jl |access-date=2023-12-18 |publisher=PeaceFounder |archive-date=18 December 2023 |archive-url=https://web.archive.org/web/20231218172216/https://github.com/PeaceFounder/AppBundler.jl |url-status=live }} for "building Julia GUI applications in modern desktop application installer formats. It uses Snap for Linux, MSIX for Windows, and DMG for MacOS as targets. It bundles full Julia within the app".{{Cite web |date=2023-11-30 |title=[ANN] AppBundler.jl - Bundle Your Julia GUI Application |url=https://discourse.julialang.org/t/ann-appbundler-jl-bundle-your-julia-gui-application/106971 |access-date=2023-12-18 |website=Julia Programming Language |language=en |archive-date=4 September 2024 |archive-url=https://web.archive.org/web/20240904035046/https://discourse.julialang.org/t/ann-appbundler-jl-bundle-your-julia-gui-application/106971 |url-status=live }} PackageCompiler.jl can build standalone executables that need no Julia source code to run.{{Cite web|title=GitHub - JuliaLang/PackageCompiler.jl: Compile your Julia Package.|date=2019-02-14|url=https://github.com/JuliaLang/PackageCompiler.jl|publisher=The Julia Language|access-date=2019-02-15|archive-date=23 March 2019|archive-url=https://web.archive.org/web/20190323182857/https://github.com/JuliaLang/PackageCompiler.jl|url-status=live}}

In Julia, everything is an object, much like object-oriented languages; however, unlike most object-oriented languages, all functions use multiple dispatch to select methods, rather than single dispatch.

Most programming paradigms can be implemented using Julia's homoiconic macros and packages. Julia's syntactic macros (used for metaprogramming), like Lisp macros, are more powerful than text-substitution macros used in the preprocessor of some other languages such as C, because they work at the level of abstract syntax trees (ASTs). Julia's macro system is hygienic, but also supports deliberate capture when desired (like for anaphoric macros) using the esc construct.

Julia draws inspiration from various dialects of Lisp, including Scheme and Common Lisp, and it shares many features with Dylan, also a multiple-dispatch-oriented dynamic language (which features an infix syntax rather than a Lisp-like prefix syntax, while in Julia "everything"{{Cite web |url=https://learnxinyminutes.com/docs/julia/ |title=Learn Julia in Y Minutes |website=Learnxinyminutes.com |access-date=2017-05-31 |archive-date=15 August 2018 |archive-url=https://web.archive.org/web/20180815232855/https://learnxinyminutes.com/docs/julia/ |url-status=live }} is an expression), and with Fortress, another numerical programming language (which features multiple dispatch and a sophisticated parametric type system). While Common Lisp Object System (CLOS) adds multiple dispatch to Common Lisp, not all functions are generic functions.

In Julia, Dylan, and Fortress, extensibility is the default, and the system's built-in functions are all generic and extensible. In Dylan, multiple dispatch is as fundamental as it is in Julia: all user-defined functions and even basic built-in operations like + are generic. Dylan's type system, however, does not fully support parametric types, which are more typical of the ML lineage of languages. By default, CLOS does not allow for dispatch on Common Lisp's parametric types; such extended dispatch semantics can only be added as an extension through the CLOS Metaobject Protocol. By convergent design, Fortress also features multiple dispatch on parametric types; unlike Julia, however, Fortress is statically rather than dynamically typed, with separate compiling and executing phases. The language features are summarized in the following table:

An example of the extensibility of Julia, the Unitful.jl package adds support for physical units of measurement to the language.

Julia has built-in support for calling C or Fortran language libraries using the @ccall macro. Additional libraries allow users to call to or from other languages such as Python,{{Cite web |title=PythonCall & JuliaCall |date=2023-10-29 |url=https://github.com/JuliaPy/PythonCall.jl |access-date=2023-10-30 |publisher=JuliaPy |archive-date=31 October 2023 |archive-url=https://web.archive.org/web/20231031010517/https://github.com/JuliaPy/PythonCall.jl |url-status=live }} C++,{{Cite web |last=Cords |first=Clem |title=Clemapfel/jluna |website=GitHub |date=2024-11-12 |url=https://github.com/Clemapfel/jluna |access-date=2024-11-26}}{{Cite web |title=CxxWrap |date=2023-10-28 |url=https://github.com/JuliaInterop/CxxWrap.jl |access-date=2023-10-30 |publisher=JuliaInterop}} Rust, R,{{Cite web |title=RCall.jl |date=2023-10-16 |url=https://github.com/JuliaInterop/RCall.jl |access-date=2023-10-30 |publisher=JuliaInterop |archive-date=30 April 2019 |archive-url=https://web.archive.org/web/20190430053549/https://github.com/JuliaInterop/RCall.jl |url-status=live }} Java{{Cite news |date=2020-06-02 |title=Julia and Spark, Better Together |url=https://juliacomputing.com/blog/2020/06/02/julia-spark.html |archive-url=https://web.archive.org/web/20200714205019/https://juliacomputing.com/blog/2020/06/02/julia-spark.html |archive-date=14 July 2020 |website=juliacomputing.com}} and to use with SQL.{{Cite web |last=Foster |first=Claire |title=SQLREPL.jl |website=GitHub |date=2023-10-23 |url=https://github.com/c42f/SQLREPL.jl |access-date=2023-10-31 |archive-date=27 September 2022 |archive-url=https://web.archive.org/web/20220927085821/https://github.com/c42f/SQLREPL.jl |url-status=live }}{{Cite web |last=Noh |first=WooKyoung |title=Octo.jl |website=GitHub |date=2023-10-18 |url=https://github.com/wookay/Octo.jl |access-date=2023-10-31}}{{Cite web |title=Usage Guide · FunSQL.jl |url=https://mechanicalrabbit.github.io/FunSQL.jl/stable/guide/#Why-FunSQL? |access-date=2023-10-31 |website=mechanicalrabbit.github.io |archive-date=31 October 2023 |archive-url=https://web.archive.org/web/20231031175900/https://mechanicalrabbit.github.io/FunSQL.jl/stable/guide/#Why-FunSQL? |url-status=live }}{{Cite web |date=21 October 2022 |title=Using Julia with Oracle Databases |url=https://blogs.oracle.com/timesten/post/using-julia-with-oracle-database |access-date=8 November 2022 |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108125241/https://blogs.oracle.com/timesten/post/using-julia-with-oracle-database |url-status=live }}

Julia can be compiled to binary executables with PackageCompiler.jl. Smaller executables can also be written using a static subset of the language provided by StaticCompiler.jl that does not support runtime dispatch (nor garbage collection, since excludes the runtime that provides it).{{Cite web |last=Short |first=Tom |title=StaticCompiler |website=GitHub |date=2023-10-30 |url=https://github.com/tshort/StaticCompiler.jl |access-date=2023-10-30 |archive-date=31 October 2023 |archive-url=https://web.archive.org/web/20231031010517/https://github.com/tshort/StaticCompiler.jl |url-status=live }}

The Julia official distribution includes an interactive command-line read–eval–print loop (REPL),{{Cite web|url=https://docs.julialang.org/en/v1/stdlib/REPL/|title=The Julia REPL · The Julia Language|website=docs.julialang.org|access-date=2019-09-22|archive-date=22 September 2019|archive-url=https://web.archive.org/web/20190922175929/https://docs.julialang.org/en/v1/stdlib/REPL/|url-status=live}} with a searchable history, tab completion, and dedicated help and shell modes,{{Cite web|url=https://en.wikibooks.org/wiki/Introducing_Julia/The_REPL|title=Introducing Julia/The REPL - Wikibooks, open books for an open world|website=en.wikibooks.org|quote=you can install the Julia package OhMyREPL.jl [..] which lets you customize the REPL's appearance and behaviour|access-date=2019-09-22|archive-date=23 June 2019|archive-url=https://web.archive.org/web/20190623142710/https://en.wikibooks.org/wiki/Introducing_Julia/The_REPL|url-status=live}} which can be used to experiment and test code quickly.{{Cite web|url=https://docs.julialang.org/en/v1/manual/getting-started/|title=Getting Started · The Julia Language|website=docs.julialang.org|language=en|access-date=2018-08-15|archive-date=10 August 2019|archive-url=https://web.archive.org/web/20190810173026/https://docs.julialang.org/en/v1/manual/getting-started/|url-status=live}} The following fragment represents a sample session example where strings are concatenated automatically by println:See also: {{URL|https://docs.julialang.org/en/v1/manual/strings/}} for string interpolation and the string(greet, ", ", whom, ".\n") example for preferred ways to concatenate strings. Julia has the println and print functions, but also a @printf macro (i.e., not in function form) to eliminate run-time overhead of formatting (unlike the same function in C).

julia> p(x) = 2x^2 + 1; f(x, y) = 1 + 2p(x)y

julia> println("Hello world!", " I'm on cloud ", f(0, 4), " as Julia supports recognizable syntax!")

Hello world! I'm on cloud 9 as Julia supports recognizable syntax!

The REPL gives user access to the system shell and to help mode, by pressing ; or ? after the prompt (preceding each command), respectively. It also keeps the history of commands, including between sessions.{{Cite web|title=Julia Documentation|url=https://docs.julialang.org/|website=JuliaLang.org|access-date=18 November 2014|archive-date=17 December 2016|archive-url=https://web.archive.org/web/20161217182748/http://docs.julialang.org/|url-status=live}} Code can be tested inside Julia's interactive session or saved into a file with a .jl extension and run from the command line by typing:

$ julia

Julia uses UTF-8 and LaTeX codes, allowing it to support common math symbols for many operators, such as ∈ for the in operator, typable with \in then pressing {{keypress|TAB}} (i.e. uses LaTeX codes, or also possible by simply copy-pasting, e.g. {{not a typo|√ and ∛}} possible for sqrt and cbrt functions). Julia has support for Unicode 15.1 (Julia 1.12-DEV supports latest 16.0 release{{Cite web |title=support Unicode 16 via utf8proc 2.10.0 by stevengj · Pull Request #56925 · JuliaLang/julia |url=https://github.com/JuliaLang/julia/pull/56925 |access-date=2025-01-08 |website=GitHub |language=en}}) for the languages of the world, even for source code, e.g. variable names (while it's recommended to use English for public code, and e.g. package names).

Julia is supported by Jupyter, an online interactive "notebooks" environment,{{Cite web |url=https://jupyter.org/ |title=Project Jupyter |access-date=19 August 2015 |archive-date=29 June 2017 |archive-url=https://web.archive.org/web/20170629054445/https://jupyter.org/ |url-status=live }} and [https://github.com/fonsp/Pluto.jl Pluto.jl], a "reactive notebook" (where notebooks are saved as pure Julia files), a possible replacement for the former kind.{{Cite web|last=Boudreau|first=Emmett|date=2020-10-16|title=Could Pluto Be A Real Jupyter Replacement?|url=https://towardsdatascience.com/could-pluto-be-a-real-jupyter-replacement-6574bfb40cc6|access-date=2020-12-08|website=Medium|language=en|archive-date=12 April 2023|archive-url=https://web.archive.org/web/20230412112240/https://towardsdatascience.com/could-pluto-be-a-real-jupyter-replacement-6574bfb40cc6|url-status=live}} In addition Posit's (formerly RStudio Inc's) Quarto publishing system supports Julia, Python, R and Observable JavaScript (those languages have official support by the company, and can even be weaved together in the same notebook document, more languages are unofficially supported).{{Cite web |last=Machlis |first=Sharon |date=2022-07-27 |title=RStudio changes name to Posit, expands focus to include Python and VS Code |url=https://www.infoworld.com/article/3668252/rstudio-changes-name-to-posit-expands-focus-to-include-python-and-vs-code.html |access-date=2023-01-18 |website=InfoWorld |language=en}}{{Cite web |date=2022-07-20 |title=Heads up! Quarto is here to stay. Immediately combine R & Python in your next document: An extension on a recent post. |url=https://www.ds-econ.com/quarto/ |access-date=2023-01-18 |website=ds-econ |language=en |archive-date=31 January 2023 |archive-url=https://web.archive.org/web/20230131161337/https://www.ds-econ.com/quarto/ |url-status=usurped }}

The REPL can be extended with additional modes, and has been with packages, e.g. with an SQL mode,{{Cite web |first=Chris |last=Foster |title=SQLREPL.jl |website=GitHub |date=2022-04-04 |url=https://github.com/c42f/SQLREPL.jl |access-date=2022-09-27 |archive-date=27 September 2022 |archive-url=https://web.archive.org/web/20220927085821/https://github.com/c42f/SQLREPL.jl |url-status=live }} for database access, and RCall.jl adds an {{nowrap|R mode}}, to work with the R language.{{Cite web |title=Getting Started · RCall.jl |url=https://juliainterop.github.io/RCall.jl/latest/gettingstarted.html#Several-Ways-to-use-RCall-1 |access-date=2022-09-27 |website=juliainterop.github.io |archive-date=4 September 2024 |archive-url=https://web.archive.org/web/20240904035201/https://juliainterop.github.io/RCall.jl/latest/gettingstarted.html#Several-Ways-to-use-RCall-1 |url-status=live }}

Julia's Visual Studio Code extension provides a fully-featured integrated development environment with "built-in dynamic autocompletion, inline results, plot pane, integrated REPL, variable view, code navigation, and many other advanced language features"{{Cite news |title=Julia in Visual Studio Code |url=https://code.visualstudio.com/docs/languages/julia}} e.g. debugging is possible, linting, and profiling.{{Cite web|last=Holy|first=Tim|title=GitHub - timholy/ProfileView.jl: Visualization of Julia profiling data.|website=GitHub|date=2019-09-13|url=https://github.com/timholy/ProfileView.jl|access-date=2019-09-22|archive-date=31 January 2020|archive-url=https://web.archive.org/web/20200131231452/https://github.com/timholy/ProfileView.jl|url-status=live}}{{Cite web|last=Gregg|first=Brendan|title=GitHub - brendangregg/FlameGraph: Stack trace visualizer.|website=GitHub|date=2019-09-20|url=https://github.com/brendangregg/FlameGraph|access-date=2019-09-22|archive-date=26 September 2019|archive-url=https://web.archive.org/web/20190926230048/https://github.com/brendangregg/FlameGraph|url-status=live}}{{Cite web|url=https://julialang.org/blog/2019/03/debuggers|title=A Julia interpreter and debugger|website=julialang.org|access-date=2019-04-10}}{{Cite web|url=https://timholy.github.io/Rebugger.jl/dev/|title=Home · Rebugger.jl|website=timholy.github.io|access-date=2019-04-10|archive-date=31 March 2019|archive-url=https://web.archive.org/web/20190331171846/https://timholy.github.io/Rebugger.jl/dev/|url-status=live}}

Julia is in practice interoperable with other languages, in fact the majority of the top 20 languages in popular use. Julia can be used to call shared library functions individually, such as those written in C or Fortran, and packages are available to allow calling other languages (which do not provide C-exported functions directly), e.g. Python (with PythonCall.jl), R,{{Cite web |date=2024-10-21 |title=Julia crashes on installation of the RCall module |url=https://discourse.julialang.org/t/julia-crashes-on-installation-of-the-rcall-module/121560/6 |access-date=2024-10-22 |website=Julia Programming Language |language=en |quote=For me RCall loads without issue on Julia 1.11 on MacOS}} MATLAB, C# (and other .NET languages with DotNET.jl, from them with JdotNET), JavaScript, Java (and other JVM languages, such as Scala with JavaCall.jl). And packages for other languages allow to call to Julia, e.g. from Python, R (to Julia 1.10.x currently possible{{Cite web |title=juliacall fails in julia 1.11 with 'undefined symbol: jl_stdout_obj' · Issue #234 · Non-Contradiction/JuliaCall |url=https://github.com/Non-Contradiction/JuliaCall/issues/234 |access-date=2024-10-22 |website=GitHub |language=en}}), Rust, Ruby, or C#. Such as with juliacall (part of PythonCall.jl) to call from Python and a different JuliaCall package for calling, Julia up to 1.10.x, from R. Julia has also been used for hardware, i.e. to compile to VHDL, as a high-level synthesis tool, for example FPGAs.

Julia has packages supporting markup languages such as HTML (and also for HTTP), XML, JSON and BSON, and for databases (such as PostgreSQL,{{Cite web |title=Home · LibPQ.jl |url=https://invenia.github.io/LibPQ.jl/stable/ |access-date=2022-11-08 |website=invenia.github.io |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108125236/https://invenia.github.io/LibPQ.jl/stable/ |url-status=dead }} Mongo, Oracle, including for TimesTen,{{Cite web |last=Hood |first=Doug |date=21 October 2022 |title=Using Julia with Oracle Databases |url=https://blogs.oracle.com/timesten/post/using-julia-with-oracle-database |access-date=8 November 2022 |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108125241/https://blogs.oracle.com/timesten/post/using-julia-with-oracle-database |url-status=live }} MySQL, SQLite, Microsoft SQL Server,{{Cite web |title=Home · FunSQL.jl |url=https://docs.juliahub.com/FunSQL/HGzDG/0.9.2/ |access-date=2022-11-08 |website=docs.juliahub.com |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108125240/https://docs.juliahub.com/FunSQL/HGzDG/0.9.2/ |url-status=live }} Amazon Redshift, Vertica, ODBC) and web use in general.{{Cite web |title=Genie Builder - Visual Studio Marketplace |url=https://marketplace.visualstudio.com/items?itemName=GenieBuilder.geniebuilder |access-date=2022-11-08 |website=marketplace.visualstudio.com |language=en-us |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108123305/https://marketplace.visualstudio.com/items?itemName=GenieBuilder.geniebuilder |url-status=live }}{{Cite web |date=2022-02-01 |title=How to Build Your First Web App in Julia with Genie.jl |url=https://www.freecodecamp.org/news/how-to-build-web-apps-in-julia/ |access-date=2022-11-08 |website=freeCodeCamp.org |language=en |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108123305/https://www.freecodecamp.org/news/how-to-build-web-apps-in-julia/ |url-status=live }}

Julia has a built-in package manager and includes a default registry system.{{Cite web|url=https://github.com/JuliaRegistries/General|title=JuliaRegistries / General|website=GitHub|access-date=2020-04-30|archive-date=3 August 2020|archive-url=https://web.archive.org/web/20200803230347/https://github.com/JuliaRegistries/General|url-status=live}} Packages are most often distributed as source code hosted on GitHub, though alternatives can also be used just as well. Packages can also be installed as binaries, using artifacts.{{Cite web|url=https://julialang.github.io/Pkg.jl/dev/artifacts/|title=Pkg.jl - Artifacts|access-date=2020-06-04|archive-date=2 August 2020|archive-url=https://web.archive.org/web/20200802205207/https://julialang.github.io/Pkg.jl/dev/artifacts/|url-status=live}} Julia's package manager is used to query and compile packages, as well as managing environments. Federated package registries are supported, allowing registries other than the official to be added locally.{{Cite web|url=https://julialang.github.io/Pkg.jl/v1/registries/|title=Pkg.jl - Registries|access-date=2020-04-30|archive-date=13 June 2020|archive-url=https://web.archive.org/web/20200613144113/https://julialang.github.io/Pkg.jl/v1/registries/|url-status=live}}

Julia's core is implemented in Julia and C, together with C++ for the LLVM dependency. The code parsing, code-lowering, and bootstrapping were implemented in FemtoLisp, a Scheme dialect, up to version 1.10.{{Cite web | first=Jeff | last=Bezanson | title=JeffBezanson/femtolisp | website=GitHub | date=6 June 2019 | url=https://github.com/JeffBezanson/femtolisp | access-date=16 June 2019 | archive-date=22 December 2022 | archive-url=https://web.archive.org/web/20221222170835/https://github.com/JeffBezanson/femtolisp | url-status=live }} Since that version the new pure-Julia stdlib package JuliaSyntax.jl is used for the parsing (while the old one can still be chosen){{Cite web |title=JuliaSyntax |date=2022-08-28 |url=https://github.com/JuliaLang/JuliaSyntax.jl |publisher=The Julia Programming Language |access-date=2022-08-28 |archive-date=28 August 2022 |archive-url=https://web.archive.org/web/20220828185806/https://github.com/JuliaLang/JuliaSyntax.jl |url-status=live }} which improves speed and "greatly improves parser error messages in various cases".{{Cite web |title=Enable JuliaSyntax.jl as an alternative Julia parser by c42f · Pull Request #46372 · JuliaLang/julia |url=https://github.com/JuliaLang/julia/pull/46372 |access-date=2022-08-28 |website=GitHub |language=en |archive-date=28 August 2022 |archive-url=https://web.archive.org/web/20220828185805/https://github.com/JuliaLang/julia/pull/46372 |url-status=live }} The LLVM compiler infrastructure project is used as the back end for generating optimized machine code for all commonly-used platforms. With some exceptions, the standard library is implemented in Julia.

Julia has tier 1 macOS support, for 64-bit Apple Silicon Macs, natively (previously such Apple M1-based Macs were only supported by running in Rosetta 2 emulation{{Cite web |date=2022-05-25 |title=Julia v1.7.3 has been released |url=https://discourse.julialang.org/t/julia-v1-7-3-has-been-released/81683 |access-date=2022-05-26 |website=JuliaLang |language=en |archive-date=26 May 2022 |archive-url=https://web.archive.org/web/20220526015606/https://discourse.julialang.org/t/julia-v1-7-3-has-been-released/81683 |url-status=live }}{{Cite web|title=Darwin/ARM64 tracking issue · Issue #36617 · JuliaLang/julia|url=https://github.com/JuliaLang/julia/issues/36617|access-date=2020-12-08|website=GitHub|language=en|archive-date=11 November 2020|archive-url=https://web.archive.org/web/20201111014801/https://github.com/JuliaLang/julia/issues/36617|url-status=live}}), and also fully supports Intel-based Macs. Windows on ARM has no official support yet. Julia has "initial support of OpenBSD in julia." but more is coming to make it actually work: https://github.com/JuliaLang/julia/issues/53632 -->

Julia has four support tiers.{{Cite web|url=https://julialang.org/downloads/#support-tiers|title=Julia Downloads|website=julialang.org|access-date=2019-05-17|archive-date=26 January 2021|archive-url=https://web.archive.org/web/20210126095723/https://julialang.org/downloads/#support-tiers|url-status=live}} All IA-32 processors completely implementing the i686 subarchitecture are supported and all 64-bit x86-64 (aka amd64), i.e. all less than about a decade old are supported. 64-bit Armv8 (and later; i.e. AArch64) processors are supported on first tier (for macOS); otherwise second tier on Linux, and ARMv7 (AArch32) on third tier.{{Cite web |title=julia/arm.md |date=2021-10-07 |url=https://github.com/JuliaLang/julia/blob/master/doc/src/devdocs/build/arm.md |publisher=The Julia Language |quote=A list of known issues for ARM is available. |access-date=2022-05-15 |archive-date=15 May 2022 |archive-url=https://web.archive.org/web/20220515202910/https://github.com/JuliaLang/julia/blob/master/doc/src/devdocs/build/arm.md |url-status=live }} Hundreds of packages are GPU-accelerated:{{Cite web |title=JuliaGPU |url=https://juliagpu.org/ |access-date=2022-11-16 |website=juliagpu.org |quote=Almost 300 packages rely directly or indirectly on Julia's GPU capabilities. |archive-date=23 May 2020 |archive-url=https://web.archive.org/web/20200523103259/https://juliagpu.org/ |url-status=live }} Nvidia GPUs have support with CUDA.jl (tier 1 on 64-bit Linux and tier 2 on 64-bit Windows, the package implementing PTX, for compute capability 3.5 (Kepler) or higher; both require CUDA 11+, older package versions work down to CUDA 9). There are also additionally packages supporting other accelerators, such as Google's TPUs,{{Cite web|title=Julia on TPUs|date=2019-11-26|url=https://github.com/JuliaTPU/XLA.jl|publisher=JuliaTPU|access-date=2019-11-29|archive-date=30 April 2019|archive-url=https://web.archive.org/web/20190430044159/https://github.com/JuliaTPU/XLA.jl|url-status=live}} and some Intel (integrated) GPUs, through oneAPI.jl,{{Cite web|title=Introducing: oneAPI.jl ⋅ JuliaGPU|url=https://juliagpu.org/post/2020-11-05-oneapi_0.1/|access-date=2021-09-06|website=juliagpu.org}} and AMD's GPUs have support with e.g. OpenCL; and experimental support for the AMD ROCm stack.{{Cite web|url=https://juliagpu.org/rocm/|title=AMD ROCm · JuliaGPU|website=juliagpu.org|access-date=2020-04-20|archive-date=13 June 2020|archive-url=https://web.archive.org/web/20200613154944/https://juliagpu.org/rocm/|url-status=live}}

for several ARM platforms, from small Raspberry Pis to the world's fastest (at one point, until recently) supercomputer Fugaku's ARM-based A64FX.{{Cite web |last=Giordano |first=Mosè |title=Julia on Fugaku (2022-07-23) |website=GitHub |date=2022-09-29 |url=https://github.com/giordano/julia-on-fugaku |access-date=2022-11-08 |archive-date=8 November 2022 |archive-url=https://web.archive.org/web/20221108120723/https://github.com/giordano/julia-on-fugaku |url-status=live }} PowerPC]] LE (64-bit) has tier 3 support, meaning it "may or may not build", and its tier will lower to 4 for 1.12, i.e. then no longer builds/works.{{Cite web |date=2025-02-18 |title=PowerPC will be demoted to Tier 4 in Julia 1.12 and later |url=https://discourse.julialang.org/t/powerpc-will-be-demoted-to-tier-4-in-julia-1-12-and-later/122923/4 |access-date=2025-02-23 |website=Julia Programming Language |language=en}}

Julia has official (tier 2) support for 64-bit ARMv8 meaning e.g. newer 64-bit (ARMv8-A) Raspberry Pi computers work with Julia (e.g. the Pi Compute Module 4 has been used in space running Julia code). For many Pis, especially older 32-bit ones, it helps to cross-compile the user's Julia code for them. The older 32-bit ARMv7 Pis worked in older Julia versions (still do, but for latest Julia version(s), note downgraded from tier 3 to its current tier 4: "Julia built at some point in the past, but is known not to build currently."). The original Raspberry Pi 1 has no official support (since it uses ARMv6 which has newer had a support tier; though some cut-down Julia has been known to run on that Pi).{{Cite web |url=https://github.com/JuliaLang/julia/issues/10488

|title=Cross-compiling for ARMv6

|quote=I believe #10917 should fix this. The CPU used there arm1176jzf-s. Please reopen if it does not.

|access-date=16 May 2015}}

{{Cite web |url=https://github.com/JuliaLang/julia/issues/10235

|title=ARM build failing during bootstrap on Raspberry Pi 2

|quote=I can confirm (FINALLY) that it works on the Raspberry Pi 2 [..] I guess we can announce alpha support for arm in 0.4 as well. |access-date=16 May 2015}}

Pico versions of the Pi are known to no work (since using the M-profile Arm, not running under Linux; not yet supported). Julia is now supported in Raspbian{{Cite web |url=https://julialang.org/blog/2017/05/raspberry-pi-julia |title=Julia available in Raspbian on the Raspberry Pi |quote=Julia works on all the Pi variants, we recommend using the Pi 3. |access-date=6 June 2017 |archive-date=4 May 2017 |archive-url=https://web.archive.org/web/20170504162102/https://julialang.org/blog/2017/05/raspberry-pi-julia |url-status=live }} while support is better for newer Pis, e.g., those with Armv7 or newer; the Julia support is promoted by the Raspberry Pi Foundation.{{Cite web |url=https://www.raspberrypi.org/blog/julia-language-raspberry-pi/ |title=Julia language for Raspberry Pi |work=Raspberry Pi Foundation |date=12 May 2017 |access-date=6 June 2017 |archive-date=2 June 2017 |archive-url=https://web.archive.org/web/20170602144753/https://www.raspberrypi.org/blog/julia-language-raspberry-pi/ |url-status=live }}

On some platforms, Julia may need to be compiled from source code (e.g., the original Raspberry Pi), with specific build options, which has been done and unofficial pre-built binaries (and build instructions) are available.{{Cite web|title=Build Julia for RaspberryPi Zero|url=https://gist.github.com/terasakisatoshi/3f8a55391b1fc22a5db4a43da8d92c98|access-date=2020-08-14|website=Gist|language=en|archive-date=1 December 2020|archive-url=https://web.archive.org/web/20201201075252/https://gist.github.com/terasakisatoshi/3f8a55391b1fc22a5db4a43da8d92c98|url-status=live}}{{Cite web|title=JuliaBerry: Julia on the Raspberry Pi|url=https://juliaberry.github.io/|access-date=2020-08-14|website=juliaberry.github.io|archive-date=8 July 2020|archive-url=https://web.archive.org/web/20200708065730/https://juliaberry.github.io/|url-status=live}}

Julia has also been built for 64-bit RISC-V (has tier 3 support),{{Cite web |title=Release v1.12-0a92fecc12 · maleadt/julia |url=https://github.com/maleadt/julia/releases/tag/v1.12-0a92fecc12 |access-date=2024-10-12 |website=GitHub |language=en}}{{Cite web |title=julia/doc/src/devdocs/build/riscv.md at master · alexfanqi/julia |url=https://github.com/alexfanqi/julia/blob/master/doc/src/devdocs/build/riscv.md |access-date=2024-10-09 |website=GitHub |language=en}} i.e. has some supporting code in core Julia.

While Julia requires an operating system by default, and has no official support to run without, or on embedded system platforms such as Arduino, Julia code has still been run on it, with some limitations, i.e. on a baremetal 16 MHz 8-bit (ATmega328P) AVR-microcontroller Arduino with 2 KB RAM (plus 32 KB of flash memory).{{Cite web |title=Running Julia baremetal on an Arduino |url=https://seelengrab.github.io/articles/Running%20Julia%20baremetal%20on%20an%20Arduino/ |access-date=2022-05-24 |website=seelengrab.github.io |archive-date=24 May 2022 |archive-url=https://web.archive.org/web/20220524075548/https://seelengrab.github.io/articles/Running%20Julia%20baremetal%20on%20an%20Arduino/ |url-status=live }}{{Cite web |last=Sukera |title=AVRDevices.jl |website=GitHub |date=2023-07-31 |url=https://github.com/Seelengrab/AVRDevices.jl |access-date=2023-08-05 |archive-date=5 August 2023 |archive-url=https://web.archive.org/web/20230805203930/https://github.com/Seelengrab/AVRDevices.jl |url-status=live }}

Julia has been adopted at many universities including MIT, Stanford, UC Berkeley, Ferdowsi University of Mashhad and the University of Cape Town. Large private firms across many sectors have adopted the language including Amazon, IBM, JP Morgan AI Research,{{Cite web |last=Chen |first=Jiahao |title=Jiahao Chen |url=https://jiahao.github.io/ |access-date=2023-02-23 |website=Jiahao Chen |language=en-us |archive-date=23 February 2023 |archive-url=https://web.archive.org/web/20230223164307/https://jiahao.github.io/ |url-status=dead }} and ASML. Julia has also been used by government agencies including NASA and the FAA, as well as every US national energy laboratory.{{Cite web |title='Why We Created Julia' Turns Ten Years Old |url=https://juliahub.com/company/media/2022/02/julia-turns-ten-years-old/ |access-date=2022-11-16 |website=juliahub.com |archive-date=16 November 2022 |archive-url=https://web.archive.org/web/20221116115921/https://juliahub.com/company/media/2022/02/julia-turns-ten-years-old/ |url-status=live }}{{Cite web |title=Newsletter January 2022 - Julia Growth Statistics - Julia Computing |url=https://juliacomputing.com/blog/2022/01/newsletter-january/ |access-date=2022-01-26 |website=juliacomputing.com |language=en |archive-date=26 January 2022 |archive-url=https://web.archive.org/web/20220126104801/https://juliacomputing.com/blog/2022/01/newsletter-january/ |url-status=live }}

• Amazon, for quantum computing{{Cite web |title=Introducing Braket.jl - Quantum Computing with Julia |url=https://forem.julialang.org/kshyatt/introducing-braketjl-10f2 |access-date=2023-02-23 |website=Julia Community 🟣 |date=15 November 2022 |language=en |quote=Almost all of the Python SDK's features are reimplemented in Julia — for those few that aren't, we are also providing a subsidiary package, PyBraket.jl, which allows you to translate Julia objects into their Python equivalents and call the Python SDK. |archive-date=19 June 2024 |archive-url=https://web.archive.org/web/20240619193302/https://forem.julialang.org/kshyatt/introducing-braketjl-10f2 |url-status=live }} and machine learning through Amazon SageMaker{{Cite web |date=May 2020 |title=Getting started with Julia on Amazon SageMaker: Step-by-step Guide |url=https://d1.awsstatic.com/whitepapers/julia-on-sagemaker.pdf |access-date=23 February 2023 |archive-date=9 March 2024 |archive-url=https://web.archive.org/web/20240309071341/https://d1.awsstatic.com/whitepapers/julia-on-sagemaker.pdf |url-status=live }}
• ASML, for hard real-time programming with their machines{{Cite web |title=Towards Using Julia for Real-Time applications in ASML JuliaCon 2022 |url=https://pretalx.com/juliacon-2022/talk/GUQBSE/ |access-date=2023-02-23 |website=pretalx.com |date=27 July 2022 |language=en |archive-date=23 February 2023 |archive-url=https://web.archive.org/web/20230223164311/https://pretalx.com/juliacon-2022/talk/GUQBSE/ |url-status=live }}
• The Climate Modeling Alliance{{Cite web |title=Home - CliMA |url=https://clima.caltech.edu/ |website=CliMA – Climate Modeling Alliance |access-date=18 June 2023 |archive-date=18 June 2023 |archive-url=https://web.archive.org/web/20230618064622/https://clima.caltech.edu/ |url-status=live }} for climate change modeling{{Cite press release |title=Julia Computing Brings Support for NVIDIA GPU Computing on Arm Powered Servers - JuliaHub |url=https://juliahub.com/blog/2019/12/nvidia-ngc-arm/ |access-date=2022-11-16 |website=juliahub.com |archive-date=16 November 2022 |archive-url=https://web.archive.org/web/20221116115917/https://juliahub.com/blog/2019/12/nvidia-ngc-arm/ |url-status=live }}
• CERN, to analyze data from the Large Hadron Collider (LHCb experiment){{Cite web |date=27 September 2021 |title=Julia for HEP Mini-workshop |url=https://indico.cern.ch/event/1074269/ |access-date=2022-08-23 |website=indico.cern.c h |quote=Julia and the first observation of Ω⁻_b → Ξ⁺_c K⁻ π⁻ |archive-date=11 August 2022 |archive-url=https://web.archive.org/web/20220811180733/https://indico.cern.ch/event/1074269/ |url-status=live }}{{Cite web |last=Mikhasenko |first=Misha |date=2022-07-29 |title=ThreeBodyDecay |url=https://github.com/mmikhasenko/ThreeBodyDecay.jl |access-date=2022-08-23 |website=GitHub |archive-date=23 August 2022 |archive-url=https://web.archive.org/web/20220823105223/https://github.com/mmikhasenko/ThreeBodyDecay.jl |url-status=live }}{{Cite web |last=Mikhasenko |first=Misha |date=July 2021 |title=Julia for QCD spectroscopy |url=https://indico.cern.ch/event/1074269/contributions/4539610/attachments/2317472/3945345/spectroscopy_mmikhasenko.pdf |access-date=2022-08-23 |website=indico.cern.ch |quote=Summary: Julia is ready to be used in physics HEP analysis |archive-date=23 August 2022 |archive-url=https://web.archive.org/web/20220823100909/https://indico.cern.ch/event/1074269/contributions/4539610/attachments/2317472/3945345/spectroscopy_mmikhasenko.pdf |url-status=live }}.{{Cite web |date=2022-08-19 |title=JuliaHEP/UnROOT.jl |url=https://github.com/JuliaHEP/UnROOT.jl |access-date=2022-08-23 |publisher=JuliaHEP |archive-date=19 June 2024 |archive-url=https://web.archive.org/web/20240619193142/https://github.com/JuliaHEP/UnROOT.jl |url-status=live }}{{Cite web |title=Julia · Search · GitLab |url=https://gitlab.cern.ch/search?search=Julia&nav_source=navbar&project_id=741&group_id=635&scope=commits&repository_ref=master |access-date=2022-08-23 |website=GitLab |language=en |archive-date=23 August 2022 |archive-url=https://web.archive.org/web/20220823095642/https://gitlab.cern.ch/search?search=Julia&nav_source=navbar&project_id=741&group_id=635&scope=commits&repository_ref=master |url-status=live }}{{Cite web |title=Commits · master · sft / lcgcmake · GitLab |url=https://gitlab.cern.ch/sft/lcgcmake/-/commits/master/cmake/toolchain/heptools-dev-base.cmake |access-date=2022-08-23 |website=GitLab |language=en |quote=bump julia version to 1.7.3 |archive-date=12 April 2023 |archive-url=https://web.archive.org/web/20230412113743/https://gitlab.cern.ch/sft/lcgcmake/-/commits/master/cmake/toolchain/heptools-dev-base.cmake |url-status=live }}
• NASA and the Jet Propulsion Laboratory use Julia to model spacecraft separation dynamics,{{Cite web |title=Modeling Spacecraft Separation Dynamics in Julia - Jonathan Diegelman | website=YouTube | date=9 March 2021 |url=https://www.youtube.com/watch?v=tQpqsmwlfY0 |language=en |access-date=2021-09-06 |archive-date=6 September 2021 |archive-url=https://web.archive.org/web/20210906221540/https://www.youtube.com/watch?v=tQpqsmwlfY0 |url-status=live }}{{Cite web |title=Circuitscape/Circuitscape.jl |date=2020-02-25 |url=https://github.com/Circuitscape/Circuitscape.jl |publisher=Circuitscape |access-date=2020-05-26 |archive-date=30 July 2020 |archive-url=https://web.archive.org/web/20200730074511/https://github.com/Circuitscape/Circuitscape.jl |url-status=live }}{{Cite web |title=Conservation through Coding: 5 Questions with Viral Shah {{!}} Science Mission Directorate |url=https://science.nasa.gov/earth-science/applied-sciences/making-space-for-earth/5-questions-with-viral-shah |access-date=2020-05-26 |website=science.nasa.gov |archive-date=25 May 2020 |archive-url=https://web.archive.org/web/20200525212814/https://science.nasa.gov/earth-science/applied-sciences/making-space-for-earth/5-questions-with-viral-shah |url-status=dead }} analyze TRAPPIST exoplanet datasets,{{Cite web |title=Julia in the Wild - Julia Data Science |url=https://juliadatascience.io/julia_wild |access-date=2022-09-12 |website=juliadatascience.io |archive-date=12 September 2022 |archive-url=https://web.archive.org/web/20220912202632/https://juliadatascience.io/julia_wild |url-status=live }}{{Cite web |title=Seven Rocky TRAPPIST-1 Planets May Be Made of Similar Stuff |url=https://exoplanets.nasa.gov/news/1669/seven-rocky-trappist-1-planets-may-be-made-of-similar-stuff/ |access-date=2022-10-06 |website=Exoplanet Exploration: Planets Beyond our Solar System |date=21 January 2021 |archive-date=6 October 2022 |archive-url=https://web.archive.org/web/20221006193612/https://exoplanets.nasa.gov/news/1669/seven-rocky-trappist-1-planets-may-be-made-of-similar-stuff/ |url-status=live }} and analyze cosmic microwave background data from the Big Bang{{Cite web |title=Julia in Astronomy & Astrophysics Research {{!}} Eric B. Ford {{!}} JuliaCon 2022 | website=YouTube | date=25 July 2022 |url=https://www.youtube.com/watch?v=vj1uzilanQI |language=en |access-date=2022-10-06 |archive-date=6 October 2022 |archive-url=https://web.archive.org/web/20221006193235/https://www.youtube.com/watch?v=vj1uzilanQI |url-status=live }}
• The Brazilian INPE, for space missions and satellite simulations{{Cite web |title=JuliaSpace/SatelliteToolbox.jl |date=2020-05-20 |url=https://github.com/JuliaSpace/SatelliteToolbox.jl |publisher=JuliaSpace |access-date=2020-05-26 |archive-date=16 June 2021 |archive-url=https://web.archive.org/web/20210616105212/https://github.com/JuliaSpace/SatelliteToolbox.jl |url-status=live }}
• Julia has also flown in space, on a small satellite,{{Cite web |date=2024-12-13 |title=Julia and the GPS payload onboard Waratah Seed-1 satellite |url=https://discourse.julialang.org/t/julia-and-the-gps-payload-onboard-waratah-seed-1-satellite/123795 |access-date=2025-02-04 |website=Julia Programming Language |quote=We flew our GPS receiver payload, Harry v3 on Waratah Seed-1 6U cubesat [..] Julia can also run on Raspberry Pi CM4, the processor I used on our GPS payload computer. |language=en}} used for a GPS module. And Julia has also been used to design satellites constallations.{{Cite AV media |url=https://www.youtube.com/watch?v=2AfCljss-Lk |title=Designing satellites constellations with Julia {{!}} Clement de Givry {{!}} JuliaCon 2024 |date=2024-10-01 |last=The Julia Programming Language |access-date=2025-02-04 |via=YouTube}}
• Embedded hardware to plan and execute flight of autonomous U.S. Air Force Research Laboratory VTOL drones{{Cite web |last=Hobbs |first=Kerianne |date=December 2022 |title=Year of Autonomy in Alaskan Glaciers, Flight, Earth Orbit, Cislunar Space and Mars |url=https://digitaleditions.walsworth.com/publication/?m=7270&i=769555&p=48 |publisher=Aerospace America Year in Review |page=48 |quote=The flight test team was able to demonstrate … a vertical takeoff and landing vehicle with both electric and conventional fuel propulsion systems onboard. The [uncrewed aerial system] was able to plan and execute these missions autonomously using onboard hardware. It was the first time the Julia programming language was flown on the embedded hardware - algorithms were precompiled ahead of time. |access-date=26 January 2023 |archive-date=19 June 2024 |archive-url=https://web.archive.org/web/20240619193142/https://digitaleditions.walsworth.com/publication/?m=7270&i=769555&p=48 |url-status=live }}

Julia is widely used for drug development in the pharmaceutical industry, having been adopted by Moderna, Pfizer, AstraZeneca, Procter & Gamble, and United Therapeutics.{{Cite web |title=Case Study - JuliaHub |url=https://juliahub.com/case-studies/ |access-date=2023-02-10 |website=juliahub.com |archive-date=10 February 2023 |archive-url=https://web.archive.org/web/20230210211700/https://juliahub.com/case-studies/ |url-status=live }}{{Cite web |title=Pumas-AI |url=https://pumas.ai/ |access-date=2023-02-10 |website=Pumas-AI |language=en |archive-date=10 February 2023 |archive-url=https://web.archive.org/web/20230210211700/https://pumas.ai/ |url-status=live }}

• The Federal Reserve Bank of New York, for macroeconomic modeling in Julia since 2015, including estimates of COVID-19 shocks in 2021{{Cite web |title=Release v1.3.0 · FRBNY-DSGE/DSGE.jl |url=https://github.com/FRBNY-DSGE/DSGE.jl/releases/tag/v1.3.0 |access-date=2022-01-03 |website=GitHub |language=en |quote=New subspecs of Model1002 for estimating the DSGE with COVID-19 shocks |archive-date=3 January 2022 |archive-url=https://web.archive.org/web/20220103170726/https://github.com/FRBNY-DSGE/DSGE.jl/releases/tag/v1.3.0 |url-status=live }}
• Also the Bank of Canada, central bank, for macroeconomic modeling{{Cite web |date=2023-05-02 |title=Finance and Economics Use Cases |url=https://discourse.julialang.org/t/finance-and-economics-use-cases/9452/104 |access-date=2023-05-04 |website=Julia Programming Language |language=en}}
• BlackRock, the world's largest asset manager, for financial time-series analysis{{Cite web |last=D'Cunha |first=Suparna Dutt |date=2017-09-20 |title=How A New Programming Language Created By Four Scientists Now Used By The World's Biggest Companies |url=https://www.forbes.com/sites/suparnadutt/2017/09/20/this-startup-created-a-new-programming-language-now-used-by-the-worlds-biggest-companies/ |url-status=live |archive-url=https://web.archive.org/web/20221001082708/https://www.forbes.com/sites/suparnadutt/2017/09/20/this-startup-created-a-new-programming-language-now-used-by-the-worlds-biggest-companies/?sh=2c98522a7de2 |archive-date=2022-10-01 |access-date=2022-10-01 |website=Forbes |language=en}}
• Aviva, the UK's largest general insurer, for actuarial calculations
• Mitre Corporation, for verification of published election results{{Cite web |title=Julia for Election Security |date=23 September 2022 |url=https://forem.julialang.org/ramsdell/julia-for-election-security-4gh |access-date=2022-09-27 |publisher=Julia Forem |language=en |archive-date=4 September 2024 |archive-url=https://web.archive.org/web/20240904035047/https://forem.julialang.org/ramsdell/julia-for-election-security-4gh |url-status=live }}
• Nobel laureate Thomas J. Sargent, for macroeconometric modeling{{Cite web |title=Nobel Laureate Thomas J. Sargent - JuliaHub |url=https://juliahub.com/case-studies/thomas-sargent/ |access-date=2023-02-10 |website=juliahub.com |archive-date=10 February 2023 |archive-url=https://web.archive.org/web/20230210211659/https://juliahub.com/case-studies/thomas-sargent/ |url-status=live }}

{{Portal|Computer programming|Free and open-source software}}

• Comparison of numerical-analysis software
• Comparison of statistical packages
• Differentiable programming
• JuMP {{ndash}} an algebraic modeling language for mathematical optimization embedded in Julia
• Python
• Nim
• Ring
• Mojo

{{Reflist|25em}}

• {{cite book|last=Nagar|first=Sandeep|year=2017|title=Beginning Julia Programming: For Engineers and Scientists|publisher=Springer |isbn=978-1-4842-3171-5 |url=https://books.google.com/books?id=KmRADwAAQBAJ&pg=PR1 }}
• {{cite journal|last1=Bezanson|first1=J|last2=Edelman|first2=A|last3=Karpinski|first3=S|last4=Shah|first4=V. B|year=2017|title=Julia: A fresh approach to numerical computing|journal=SIAM Review |volume=59 |issue=1 |pages=65–98 |doi=10.1137/141000671 |arxiv=1411.1607 |citeseerx=10.1.1.760.8894|s2cid=13026838}}
• {{cite book|last=Joshi|first=Anshul|year=2016|title=Julia for Data Science － Explore the world of data science from scratch with Julia by your side|publisher=Packt |isbn=978-1-78355-386-0 |url=https://books.google.com/books?id=Bn9cDgAAQBAJ&pg=PP2}}
• Tobin A Driscoll and Richard J. Braun (Aug. 2022). "Fundamentals of Numerical Computation: Julia Edition". SIAM. {{ISBN|978-1-611977-00-4}}.
• C. T. Kelley (2022). "Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia", SIAM. {{ISBN|978-1-611977-26-4}}.
• {{cite book|last=Kalicharan|first=Noel|year=2021|title=Julia - Bit by Bit|series=Undergraduate Topics in Computer Science |publisher=Springer |doi=10.1007/978-3-030-73936-2 |isbn=978-3-030-73936-2 |s2cid=235917112 |url=https://link.springer.com/book/10.1007/978-3-030-73936-2 }}
• Clemens Heitzinger (2022): "Algorithms with Julia", Springer, ISBN 978-3-031-16559-7.

{{Wikibooks|Introducing Julia}}

• {{Official website|https://julialang.org}}
• {{GitHub|JuliaLang/Julia}}

{{Programming languages}}

{{FOSS}}

{{Numerical analysis software}}

{{Mathematical optimization software}}

{{Statistical software}}

{{Differentiable computing}}

{{Authority control}}

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