Julia
ParadigmMulti-paradigm: multiple dispatch (primary paradigm), procedural, functional, meta, multistaged[1]
Designed byJeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah
DeveloperJeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors[2][3]
First appeared2012; 12 years ago (2012)[4]
Stable release
1.10.3[5] Edit this on Wikidata / 30 April 2024; 15 days ago (30 April 2024) and
1.6.7 LTS[8][9] / 19 July 2022; 21 months ago (2022-07-19)
Preview release
1.8.3[6][7] / and 1.8.4 being worked on[10] and 1.9.0-DEV with daily updates[11] and 1.10.0-DEV[12]
Typing disciplineDynamic,[13] strong,[13] nominative, parametric, optional
Implementation languageJulia, C, C++, Scheme, LLVM[14]
PlatformTier 1: x86-64, IA-32, CUDA 10.1+[15]/Nvidia GPUs (for Linux and Windows)
Tier 2: 64-bit Arm (e.g. Apple M1 Macs, while they also have tier 1 support using Rosetta[16]), 32-bit Windows (64-bit is tier 1)
Tier 3: 32-bit Arm, PowerPC, AMD (ROCm) GPUs.
Also supports oneAPI/Intel's GPUs and Google's TPUs,[17] and has web browser support (for JavaScript and WebAssembly),[18] and can work in Android. For more details see "supported platforms".
OSLinux, macOS, Windows and FreeBSD
LicenseMIT (core),[2] GPL v2;[19][20] a makefile option omits GPL libraries[21]
Filename extensions.jl
WebsiteJuliaLang.org
Influenced by

Julia is a high-level, dynamic programming language. Its features are well suited for numerical analysis and computational science.[27][28][29][30]

Distinctive aspects of Julia's design include a type system with parametric polymorphism in a dynamic programming language; with multiple dispatch as its core programming paradigm. Julia supports concurrent, (composable) parallel and distributed computing (with or without using MPI[31] or the built-in corresponding[clarification needed][32] to "OpenMP-style" threads[33]), and direct calling of C and Fortran libraries without glue code. Julia uses a just-in-time (JIT) compiler that is referred to as "just-ahead-of-time" (JAOT) in the Julia community, as Julia compiles all code (by default) to machine code before running it.[34][35]

Julia is garbage-collected,[36] uses eager evaluation, and includes efficient libraries for floating-point calculations, linear algebra, random number generation, and regular expression matching. Many libraries are available, including some (e.g., for fast Fourier transforms) that were previously bundled with Julia and are now separate.[37]

Several development tools support coding in Julia, such as integrated development environments (e.g. for Microsoft's Visual Studio Code, an extension is available[38] providing debugging and linting support); with integrated tools, e.g. a profiler (and flame graph support available[39][40] for the built-in one), debugger,[41] and the Rebugger.jl package "supports repeated-execution debugging"[a] and more.[43]

Julia works with other languages, calling C has special support, and with use of extra packages, e.g. for working with Python, R, Rust, C++, SQL and to work with or even to compile to JavaScript.

Julia can be compiled to binary executables supporting all features. Small binary executables can also be made, e.g. down to 9 KB (then without a garbage collector or Julia's runtime, i.e. with similar limited capabilities to the C language), for computers or even microcontrollers with 2 KB of RAM. By default, Julia code needs the Julia runtime, but some code can be compiled to small executables (with limited Julia capabilities) as mentioned, with packages helping for that, or to large executables, with a different package for that, keeping all capabilities of Julia. In both cases no source code needs to be distributed.

History

Work on Julia was started in 2009, by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman, who 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.[44] 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."[29] Bezanson said he chose the name on the recommendation of a friend,[45] then years later wrote:

Maybe julia stands for “Jeff’s uncommon lisp is automated”?[46]

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).[47] The JuliaCon academic conference for Julia users and developers has been held annually since 2014 with JuliaCon2020 welcoming over 28,900 unique viewers,[48] 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.[49]

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."[50] 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."[51]

Both Julia 0.7[52] 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;[53] and the syntax changed a little (with the syntax now stable, and same for 1.x and 0.7).

Julia 1.1 was released in January 2019 with a new "exception stack" feature.

Julia 1.2 was released in August 2019 with some built-in support for web browsers.[54]

Julia 1.3 added composable multi-threaded parallelism and a binary artifacts system for Julia packages.[55]

Julia 1.4 added syntax for generic array indexing to handle e.g. 0-based arrays.[56] The memory model was also changed.[57]

Julia 1.5 released in August 2020 added record and replay debugging support,[58] for Mozilla's rr tool. The release changed the behavior in the REPL (soft scope) to the one used in Jupyter, but fully compatible with non-REPL code. Most of the thread API was marked as stable, and with this release "arbitrary immutable objects—regardless of whether they have fields that reference mutable objects or not—can now be stack allocated",[59] reducing heap allocations, e.g. views are no longer allocating. Julia 1.5 targeted so-called "time-to-first-plot" performance, in general, the speed of compilation itself (as opposed to performance of the generated code), and adds tools for developers to improve package loading.[60]

Julia 1.6 was the largest release since 1.0, 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".[61]

As of version 1.7 Julia development is back to time-based releases.[62]

Julia 1.7.0 was released in November 2021, with many changes, e.g. a new faster random-number generator.

Julia 1.7.3 was released on 25 May 2022, fixing some issues, including at least one security update.[63]

Julia 1.8 has been released (and 1.8.1 and 1.8.2 as followup on 29 September 2022, both fixing bugs and invalidations, thus compiling faster), with improvements for distributing Julia programs without source code, and compiler speedup, in some cases by 25%,[64] and more controllable inlining (i.e. now also allowing applying @inline at the call site, not just on the function itself) and Julia 1.9 is the next milestone, it and the milestones for 1.10, 1.11, 1.12 and 2.0 currently have no set due dates.[65][66]

JuliaCon

Since 2014,[67] 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[68] and the University of Maryland, Baltimore.[69] The event audience has grown from a few dozen people to over 28,900 unique attendees[70] during JuliaCon 2020, which took place virtually. JuliaCon 2021 also took place virtually[71] with keynote addresses from professors William Kahan (the primary architect of the IEEE 754 floating-point standard, which his keynote is about, that virtually all CPUs use and languages, including Julia),[72] and Jan Vitek,[73] Xiaoye Sherry Li, and Soumith Chintala (co-creator of PyTorch).[74] 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.

Sponsors

The Julia language became a NumFOCUS fiscally sponsored project in 2014 in an effort to ensure the project's long-term sustainability.[75] 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.[76] 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",[77] meaning to Firefox and other web browsers.[78][79][80][81] The Julia language is also supported by individual donors on GitHub.[82]

Julia Computing company

Julia Computing, Inc. was founded in 2015 by Viral B. Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski and Keno Fischer.[83]

In June 2017, Julia Computing raised US$4.6 million in seed funding from General Catalyst and Founder Collective,[84] 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",[85] and in December 2019 the company got $1.1 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".[86] In July 2021, Julia Computing announced they raised a $24 million Series A round led by Dorilton Ventures,[87] which also owns 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".[88]

Language features

Julia is a general-purpose programming language,[89] while also originally designed for numerical/technical computing. It is also useful for low-level systems programming,[90] as a specification language,[91] High-level Synthesis (HLS) tool (for hardware, e.g. FPGAs),[92] and for web programming[93] at both server[94][95] and client[96][18] side.

The main features of the language are:

Multiple dispatch (also termed multimethods in Lisp) is a generalization of single dispatch – the polymorphic mechanism used in common object-oriented programming (OOP) languages, such as Python, C++, Java, JavaScript, and Smalltalk – 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, a standalone executable that needs no Julia source code can be built with e.g. PackageCompiler.jl.[100]

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 ALGOL-like free-form infix syntax rather than a Lisp-like prefix syntax, while in Julia "everything"[101] 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:

Language Type system Generic functions Parametric types
Julia Dynamic Default Yes
Common Lisp Dynamic Opt-in Yes (but no dispatch)
Dylan Dynamic Default Partial (no dispatch)
Fortress Static Default Yes

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

Interaction

The Julia official distribution includes an interactive command-line read–eval–print loop (REPL),[102] with a searchable history, tab completion, and dedicated help and shell modes,[103] which can be used to experiment and test code quickly.[104] The following fragment represents a sample session example where strings are concatenated automatically by println:[105] 

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.[106] Code can be tested inside the Julia's interactive section or saved into a file with a .jl extension and run from the command line by typing:[101] 

 $ julia <filename>

Julia uses UTF-8, e.g. for source code, meaning also allowing as an option common math symbols for many operators, such as ∈ for the in operator, typable with \in then pressing Tab ↹ (i.e. uses LaTeX codes, or also possible by simply copy-pasting, e.g. √ and ∛ possible for sqrt and cbrt functions). Julia has support for Unicode 14.0 (from 2021, and 1.9-DEV supports the latest Unicode 15.0[107]), for the languages of the world, even for source code, e.g. variable names (while not using English isn't recommended for code for others to read e.g. package names).[108]

Julia is supported by Jupyter, an online interactive "notebooks" environment,[109] and Pluto.jl, a "reactive notebook" (where notebooks are saved as pure Julia files), a possible replacement for the former kind.[110]

The REPL can be extended with additional modes, and has been with packages, e.g. with an SQL mode,[111] for database access, and RCall.jl adds an R mode, to work with the R language.[112]

Use with other languages

Julia is in practice interoperable with other languages (e.g. majority of top 10–20 languages in popular use). Julia's ccall keyword is used to call C-exported or Fortran shared library functions individually, and packages are available to allow calling other languages e.g. Python, R, MATLAB, C# (and other .NET languages with DotNET.jl), JavaScript, Java (and other JVM languages, such as Scala with JavaCall.jl).[113] And packages for other languages allow to call to Julia, e.g. from Python, R, Rust, or Ruby, such as with pyjulia and PythonCall.jl/juliacall for Python and a different JuliaCall package for R. Julia has also been used for hardware, i.e. to compile to VHDL, as a High-level Synthesis (HLS) tool (for e.g. FPGAs).[92]

Julia has packages supporting markup languages such as HTML (and also for HTTP), XML, JSON and BSON, and for databases and web use in general.[citation needed]

Package system

Julia has a built-in package manager and includes a default registry system.[114] 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.[115] 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.[116]

Implementation

Julia's core is implemented in Julia and C, together with C++ for the LLVM dependency. The code parsing and code-lowering are implemented in FemtoLisp, a Scheme dialect[117] (that parser can however be switched out at runtime, with the pure-Julia package JuliaSyntax.jl,[118] a drop-in replacement, meant to be faster and it "greatly improves parser error messages in various cases",[119] thus it's being considered as an addition to Julia, first as a non-default option in 1.9.0, but can already be used in older versions). A non-default parser package Parser.jl (for e.g. much faster floating-point number parsing on Windows[120]) is available, already used indirectly by some packages, but is also being considered for inclusion into Julia.[121] The LLVM compiler infrastructure project is used as the back end for generation of 64-bit or 32-bit optimized machine code depending on the platform Julia runs on. With some exceptions (e.g., PCRE, which is being considered for removal in Julia 2.0;[122] Julia already has alternative regex libraries, e.g. RE2.jl for Google's re2, StrRegex.jl and ReadableRegex.jl), the standard library is implemented in Julia. A very notable aspect of Julia's implementation is its speed, which is often within a factor of two relative to fully optimized C code (and thus often an order of magnitude faster than Python or R).[123][124][125][126] Development of Julia began in 2009 and an open-source version was publicized in February 2012.[4][127]

Current and future platforms

Julia has tier 1 macOS support, meaning for Intel-based Macs, but also for the new Apple M1-based Macs, by either running in Rosetta 2 emulation,[16][128] or, while then, with tier 2 native (non-Rosetta) support with Julia 1.8 (unlike the older LTS version of Julia which only has tier 3 (experimental) support; Windows on ARM has no official support yet). The work on that support (i.e. without emulation) is mostly done, and many programs work with Julia 1.8.0. Julia was prior to 1.8.0 claimed to work[129] "ok" on M1 Macs (at reduced performance) through the (automatic) use of Rosetta 2 (that needs to emulate Julia).

Since Julia uses JIT, Julia generates native machine code directly, before a function is first run (i.e. a different approach than compiling to bytecode, that you distribute by default, to be run on a virtual machine (VM), as with e.g. Java/JVM; then translated from the bytecode while running, as done by Dalvik on older versions of Android).

Julia has four support tiers.[130] 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. ARMv8 (AArch64) processors are supported on second tier, and ARMv7 and ARMv6 (AArch32) are supported with some caveats (lower tier) for Julia 1.0.x and also had official executables for later versions, while 32-bit ARM support was later downgraded to tier 3 (however, unofficial binaries are available for Julia 1.5.1[131]).[132] CUDA (i.e. Nvidia GPUs; implementing PTX) has tier 1 support, with the help of an external package. There are also additionally packages supporting other accelerators, such as Google's TPUs,[133] and some Intel (integrated) GPUs, through oneAPI.jl,[134] and AMD's GPUs have support with e.g. OpenCL; and experimental support for the AMD ROCm stack.[135] Julia's downloads page provides executables (and source) for all the officially supported platforms.

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.[136][137] Julia has been built on several ARM platforms.[citation needed] PowerPC (64-bit) has tier 3 support, meaning it "may or may not build". Julia is now supported in Raspbian[138] while support is better for newer Pis, e.g., those with ARMv7 or newer; the Julia support is promoted by the Raspberry Pi Foundation.[139]

There is also support for web browsers/JavaScript through JSExpr.jl;[96] and the alternative language of web browsers, WebAssembly, has minimal support[18] for several upcoming external Julia projects. Julia can compile to ARM; thus, in theory, Android apps can be made with the NDK, but for now Julia has been made to run under Android only indirectly, i.e. with a Ubuntu chroot on Android.[140]

While Julia requires an operating system by default, and has no official support to run without or on embedded system platforms like Arduino, Julia code has still been run, with some limitations, on a baremetal 16 MHz 8-bit AVR-microcontroller Arduino with 2 KB RAM (plus 32 KB of flash memory).[141]

Adoption

Julia has been adopted by many universities including MIT, Stanford, and UC Berkeley. Large private firms across many sectors have adopted the language including Amazon, Intel, Capital One, Uber, and Pfizer. Julia has also been used in government agencies like NASA and the FAA as well as every US national energy laboratory.[142][143]

Scientific computing

Other notable uses

See also

Notes

  1. ^ [With Rebugger.jl] it is possible to:
    • test different modifications to the code or arguments without exiting "debug mode" or saving the file
    • run the same chosen block of code repeatedly without needing to repeat “setup” work placing nested method in the original call stack.[42]
  2. ^ For calling Python 3 (the older default to call Python 2, is also still supported)[97][98] and calling in the other direction, from Python to Julia, is also supported with pyjulia.[99]

References

  1. ^ "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.
  2. ^ a b "LICENSE.md". GitHub. September 2017.
  3. ^ "Contributors to JuliaLang/julia". GitHub.
  4. ^ a b c d e f g "Why We Created Julia". Julia website. February 2012. Retrieved 7 February 2013.
  5. ^ "Download Julia".
  6. ^ "Backports for Julia 1.8.3 by KristofferC · Pull Request #46984 · JuliaLang/julia". GitHub. Retrieved 2 October 2022.
  7. ^ "GitHub - JuliaLang/julia at release-1.8". GitHub. Retrieved 8 November 2022.
  8. ^ "GitHub - JuliaLang/julia at release-1.6". GitHub. Retrieved 19 July 2022.
  9. ^ "release-1.6: Backports for 1.6.7 by KristofferC · Pull Request #45319 · JuliaLang/julia". GitHub. Retrieved 16 May 2022.
  10. ^ "Backports for Julia 1.8.4 by KristofferC · Pull Request #47488 · JuliaLang/julia". GitHub. Retrieved 8 November 2022.
  11. ^ "Set VERSION to 1.9.0-DEV, move 1.8 NEWS to HISTORY, update SPDX by KristofferC · Pull Request #44203 · JuliaLang/julia". GitHub. Retrieved 25 February 2022.
  12. ^ "Set VERSION to 1.10.0-DEV by KristofferC · Pull Request #47222 · JuliaLang/julia". GitHub. Retrieved 19 October 2022.
  13. ^ a b Engheim, Erik (17 November 2017). "Dynamically Typed Languages Are Not What You Think". Medium. Retrieved 27 January 2021.
  14. ^ "Building Julia (Detailed)". GitHub. September 2017. Retrieved 16 May 2022.
  15. ^ "NVIDIA CUDA ⋅ JuliaGPU". juliagpu.org. 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
  16. ^ a b "Julia v1.7.3 has been released". JuliaLang. 25 May 2022. Retrieved 26 May 2022.
  17. ^ "External Method Tables by Keno · Pull Request #39697 · JuliaLang/julia". GitHub. Retrieved 9 June 2021.
  18. ^ a b c Fischer, Keno (22 July 2019). "Running julia on wasm". GitHub. Retrieved 25 July 2019.
  19. ^ "julia/julia.spdx.json". GitHub. September 2017. Retrieved 16 May 2022.
  20. ^ "Non-GPL Julia?". Groups.google.com. Retrieved 31 May 2017.
  21. ^ "Introduce USE_GPL_LIBS Makefile flag to build Julia without GPL libraries". GitHub. Note that this commit does not remove GPL utilities such as git and busybox that are included in the Julia binary installers on Mac and Windows. It allows building from source with no GPL library dependencies.
  22. ^ Stokel-Walker, Chris. "Julia: The Goldilocks language". Increment. Stripe. Retrieved 23 August 2020.
  23. ^ a b c d "Home · The Julia Language". docs.julialang.org. Retrieved 15 August 2018.
  24. ^ "Programming Language Network". GitHub. Retrieved 6 December 2016.
  25. ^ "What Should We Call the Language of Mathematica?—Stephen Wolfram Writings". writings.stephenwolfram.com. Retrieved 24 June 2021.
  26. ^ "JuliaCon 2016". JuliaCon. Retrieved 6 December 2016. He has co-designed the programming language Scheme, which has greatly influenced the design of Julia
  27. ^ Bryant, Avi (15 October 2012). "Matlab, R, and Julia: Languages for data analysis". O'Reilly Strata. Archived from the original on 26 April 2014.
  28. ^ Singh, Vicky (23 August 2015). "Julia Programming Language – A True Python Alternative". Technotification.
  29. ^ a b Krill, Paul (18 April 2012). "New Julia language seeks to be the C for scientists". InfoWorld.
  30. ^ Finley, Klint (3 February 2014). "Out in the Open: Man Creates One Programming Language to Rule Them All". Wired.
  31. ^ "GitHub - JuliaParallel/MPI.jl: MPI wrappers for Julia". Parallel Julia. Retrieved 22 September 2019.
  32. ^ "Questions about getting started with parallel computing". JuliaLang. 16 June 2019. Retrieved 8 October 2019.
  33. ^ "Julia and Concurrency". JuliaLang. 24 June 2019. Retrieved 22 September 2019.
  34. ^ Fischer, Keno; Nash, Jameson. "Growing a Compiler - Getting to Machine Learning from a General Purpose Compiler". Julia Computing Blog. Retrieved 11 April 2019.
  35. ^ "Creating a sysimage". PackageCompiler Documentation.
  36. ^ "Suspending Garbage Collection for Performance...good idea or bad idea?". Groups.google.com. Retrieved 31 May 2017.
  37. ^ now available with using FFTW in current versions (That dependency, is one of many which, was moved out of the standard library to a package because it is GPL licensed, and thus is not included in Julia 1.0 by default.) "Remove the FFTW bindings from Base by ararslan · Pull Request #21956 · JuliaLang/julia". GitHub. Retrieved 1 March 2018.
  38. ^ "Julia for Visual Studio Code". www.julia-vscode.org. Retrieved 17 July 2022.
  39. ^ Holy, Tim (13 September 2019). "GitHub - timholy/ProfileView.jl: Visualization of Julia profiling data". GitHub. Retrieved 22 September 2019.
  40. ^ Gregg, Brendan (20 September 2019). "GitHub - brendangregg/FlameGraph: Stack trace visualizer". GitHub. Retrieved 22 September 2019.
  41. ^ "A Julia interpreter and debugger". julialang.org. Retrieved 10 April 2019.
  42. ^ "[ANN] Rebugger: interactive debugging for Julia 0.7/1.0". JuliaLang. 21 August 2018. Retrieved 10 April 2019.
  43. ^ "Home · Rebugger.jl". timholy.github.io. Retrieved 10 April 2019.
  44. ^ "Why We Created Julia". JuliaLang.org. Retrieved 5 June 2017. ((cite web)): Cite uses deprecated parameter |authors= (help)
  45. ^ Torre, Charles. "Stefan Karpinski and Jeff Bezanson on Julia". Channel 9. MSDN. Retrieved 4 December 2018.
  46. ^ Bezanson, Jeff (2 April 2021). "CAS Benchmarks". discourse.julialang.org. Julia. Retrieved 2 April 2021.
  47. ^ "Newsletter August 2021 - Julia Computing". juliacomputing.com. Retrieved 2 September 2021.
  48. ^ "JuliaCon 2020 Wrap-up". julialang.org. 11 August 2020. Retrieved 20 December 2020.
  49. ^ "JuliaCon 2021 Highlights". julialang.org. Retrieved 6 September 2021.
  50. ^ "Julia language co-creators win James H. Wilkinson Prize for Numerical Software". MIT News. Retrieved 22 January 2019.
  51. ^ "Alan Edelman of MIT Recognized with Prestigious 2019 IEEE Computer Society Sidney Fernbach Award | IEEE Computer Society" (Press release). 1 October 2019. Retrieved 9 October 2019.
  52. ^ "What is Julia 0.7? How does it relate to 1.0?". JuliaLang. 26 March 2018. Retrieved 17 October 2018.
  53. ^ Davies, Eric. "Writing Iterators in Julia 0.7". julialang.org. Retrieved 5 August 2018.
  54. ^ "Sys.isjsvm([os])". The Julia Language. 20 August 2019. Retrieved 20 August 2019. Predicate for testing if Julia is running in a JavaScript VM (JSVM), including e.g. a WebAssembly JavaScript embedding in a web browser.
  55. ^ Bezanson, Jeff; Karpinski, Stefan; Shah, Viral; Edelman, Alan. "The Julia Language". julialang.org. Retrieved 13 December 2019.
  56. ^ "support a[begin] for a[firstindex(a)] by stevengj · Pull Request #33946 · JuliaLang/julia". GitHub. Retrieved 7 April 2020.
  57. ^ quinnj. "For structs with all isbits or isbitsunion fields, allow them to be stored inline in arrays · Pull Request #32448 · JuliaLang/julia". GitHub. Retrieved 7 April 2020. I still keep running into problems that this causes internally because it was a breaking change that changes assumptions made by some users and inference/codegen.
  58. ^ Fischer, Keno (2 May 2020). "Coming in Julia 1.5: Time Traveling (Linux) Bug Reporting". julialang.org. Retrieved 5 May 2020. Overhead for recording of single threaded processes is generally below 2x, most often between 2% and 50% (lower for purely numerical calculations, higher for workloads that interact with the OS). Recording multiple threads or processes that share memory (as opposed to using kernel-based message passing) is harder. [..] As expected, the threads test is the worst offender with about 600% overhead.
  59. ^ "The Julia Language". julialang.org. Retrieved 14 August 2020. There are some size-based limits to which structs can be stack allocated, but they are unlikely to be exceeded in practice. ((cite web)): Cite uses deprecated parameter |authors= (help)
  60. ^ "The Julia Language". julialang.org. Retrieved 16 September 2020. ((cite web)): Cite uses deprecated parameter |authors= (help)
  61. ^ "Julia 1.6 Highlights". julialang.org. Retrieved 26 March 2021. ((cite web)): Cite uses deprecated parameter |authors= (help)
  62. ^ "Upgrade to OpenBLAS 0.3.13 · Pull Request #39216 · JuliaLang/julia". GitHub. Retrieved 26 April 2021. Given that 1.7 is not too far away (timed releases going forward)
  63. ^ "[Zlib_jll] Update to v1.2.12+3 by giordano · Pull Request #44810 · JuliaLang/julia". GitHub. Retrieved 25 May 2022.
  64. ^ "compiler: speed up bootstrapping time by 25% by aviatesk · Pull Request #41794 · JuliaLang/julia". GitHub. Retrieved 3 March 2022. the bootstrapping took about 80 seconds previously, but on this PR the time is reduced to about 60 seconds.
  65. ^ "Milestones - JuliaLang/julia". GitHub. Retrieved 27 August 2022.
  66. ^ "The future of Julia, 1.6, 1.7-rc1, 1.8, 1.9, 1.10 and 2.0 and LTS". JuliaLang. 14 September 2021. Retrieved 17 January 2022. I suspect at some point 1.x work will slow down a bit and we'll get some more capacity to stop and think about 2.0 kinds of changes, but that time just hasn't happened yet.
  67. ^ "JuliaCon 2014". juliacon.org. Retrieved 20 June 2021.
  68. ^ "JuliaCon 2016 at MIT". mit.edu. Retrieved 20 June 2021.
  69. ^ "JuliaCon 2019 at UMB". technical.ly. 23 July 2019. Retrieved 20 June 2021.
  70. ^ "JuliaCon 2020 wrap up". julialang.org. Retrieved 20 June 2021.
  71. ^ "JuliaCon 2021". Juliacon.org. Retrieved 20 June 2021.
  72. ^ "JuliaCon 2021 Highlights". julialang.org. Retrieved 3 March 2022. 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.
  73. ^ "Jan Vitek Homepage". janvitek.org. Retrieved 20 June 2021.
  74. ^ "Soumith Chintala Homepage". soumith.ch. Retrieved 20 June 2021.
  75. ^ "Julia: NumFOCUS Sponsored Project since 2014". numfocus.org. Retrieved 29 September 2020.
  76. ^ "The Julia Language". julialang.org. Retrieved 22 September 2019.
  77. ^ Cimpanu, Catalin. "Mozilla is funding a way to support Julia in Firefox". ZDNet. Retrieved 22 September 2019.
  78. ^ "Julia in Iodide". alpha.iodide.io. Retrieved 22 September 2019.
  79. ^ "Language plugins - Iodide Documentation". iodide-project.github.io. Retrieved 22 September 2019.
  80. ^ "Mozilla Research Grants 2019H1". Mozilla. Retrieved 22 September 2019. 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.
  81. ^ "Literate scientific computing and communication for the web: iodide-project/iodide". iodide. 20 September 2019. Retrieved 22 September 2019. 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 within a single, portable, sharable, and hackable file.
  82. ^ "Sponsor the Julia Language". github.com. Retrieved 5 June 2021.
  83. ^ "About Us – Julia Computing". juliacomputing.com. Retrieved 12 September 2017.
  84. ^ "Julia Computing Raises $4.6M in Seed Funding". Archived from the original on 10 May 2019.
  85. ^ "Julia Computing Awarded $910,000 Grant by Alfred P. Sloan Foundation, Including $160,000 for STEM Diversity". juliacomputing.com. 26 June 2017. Retrieved 28 July 2020.
  86. ^ "DIFFERENTIATE—Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements" (PDF).
  87. ^ "Julia Computing raises $24 mln in funding round led by Dorilton Ventures". Reuters. 19 July 2021.
  88. ^ "Williams welcomes Julia Computing as Dorilton Ventures partner". www.williamsf1.com (Press release). Retrieved 2 September 2021.
  89. ^ "The Julia Language" (official website). General Purpose [..] Julia lets you write UIs, statically compile your code, or even deploy it on a webserver.
  90. ^ Green, Todd (10 August 2018). "Low-Level Systems Programming in High-Level Julia". Archived from the original on 5 November 2018. Retrieved 5 November 2018.
  91. ^ Moss, Robert (26 June 2015). "Using Julia as a Specification Language for the Next-Generation Airborne Collision Avoidance System" (PDF). Archived from the original on 1 July 2015. Retrieved 29 June 2015. Airborne collision avoidance system
  92. ^ a b Biggs, Benjamin; McInerney, Ian; Kerrigan, Eric C.; Constantinides, George A. (2022). "High-level Synthesis using the Julia Language". arXiv:2201.11522 [cs.SE]. We present a prototype Julia HLS tool, written in Julia, that transforms Julia code to VHDL.
  93. ^ "Announcing Dash for Julia". plotly (Press release). 26 October 2020. Retrieved 2 September 2021.
  94. ^ Anaya, Richard (28 April 2019). "How to create a multi-threaded HTTP server in Julia". Medium. Retrieved 25 July 2019. 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.
  95. ^ Anthoff, David (1 June 2019). "Node.js installation for julia". GitHub. Retrieved 25 July 2019.
  96. ^ a b "Translate Julia to JavaScript". JuliaGizmos. 7 July 2019. Retrieved 25 July 2019.
  97. ^ "PyCall.jl". stevengj. github.com. 7 November 2021.
  98. ^ "Using PyCall in julia on Ubuntu with python3". julia-users at Google Groups. to import modules (e.g., python3-numpy)
  99. ^ "python interface to julia". GitHub. 6 November 2021.
  100. ^ "GitHub - JuliaLang/PackageCompiler.jl: Compile your Julia Package". The Julia Language. 14 February 2019. Retrieved 15 February 2019.
  101. ^ a b "Learn Julia in Y Minutes". Learnxinyminutes.com. Retrieved 31 May 2017.
  102. ^ "The Julia REPL · The Julia Language". docs.julialang.org. Retrieved 22 September 2019.
  103. ^ "Introducing Julia/The REPL - Wikibooks, open books for an open world". en.wikibooks.org. Retrieved 22 September 2019. you can install the Julia package OhMyREPL.jl [..] which lets you customize the REPL's appearance and behaviour
  104. ^ "Getting Started · The Julia Language". docs.julialang.org. Retrieved 15 August 2018.
  105. ^ See also: 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).
  106. ^ "Julia Documentation". JuliaLang.org. Retrieved 18 November 2014.
  107. ^ "support Unicode 15 via utf8proc 2.8 by stevengj · Pull Request #47392 · JuliaLang/julia". GitHub. Retrieved 3 November 2022.
  108. ^ "support Unicode 14.0.0 (via utf8proc 2.7.0)". Github. 21 October 2022. Retrieved 3 November 2022.((cite web)): CS1 maint: url-status (link)
  109. ^ "Project Jupyter".
  110. ^ Boudreau, Emmett (16 October 2020). "Could Pluto Be A Real Jupyter Replacement?". Medium. Retrieved 8 December 2020.
  111. ^ c42f (4 April 2022). "SQLREPL.jl". Retrieved 27 September 2022.((cite web)): CS1 maint: numeric names: authors list (link)
  112. ^ "Getting Started · RCall.jl". juliainterop.github.io. Retrieved 27 September 2022.
  113. ^ "Julia and Spark, Better Together". juliacomputing.com. 2 June 2020. Retrieved 14 July 2020.
  114. ^ "JuliaRegistries / General". GitHub. Retrieved 30 April 2020.
  115. ^ "Pkg.jl - Artifacts". Retrieved 4 June 2020.
  116. ^ "Pkg.jl - Registries". Retrieved 30 April 2020.
  117. ^ Bezanson, Jeff (6 June 2019). "JeffBezanson/femtolisp". GitHub. Retrieved 16 June 2019.
  118. ^ "JuliaSyntax". The Julia Programming Language. 28 August 2022. Retrieved 28 August 2022.
  119. ^ "Enable JuliaSyntax.jl as an alternative Julia parser by c42f · Pull Request #46372 · JuliaLang/julia". GitHub. Retrieved 28 August 2022.
  120. ^ "issue with parsing float in MS Windows · Issue #45612 · JuliaLang/julia". GitHub. Retrieved 28 August 2022. The parse function performs poorly in MS Windows, and using simple threading escalates the problem significantly.
  121. ^ "Move Parsers.jl to base · Issue #45301 · JuliaLang/julia". GitHub. Retrieved 28 August 2022.
  122. ^ "move Regex into a stdlib · Issue #38300 · JuliaLang/julia". GitHub. Retrieved 28 August 2022.
  123. ^ Julia: A Fast Dynamic Language for Technical Computing – slides
  124. ^ Bezanson, Jeff; Karpinski, Stefan; Shah, Viral B.; Edelman, Alan (23 September 2012). "Julia: A Fast Dynamic Language for Technical Computing". arXiv:1209.5145 [cs].
  125. ^ "How To Make Python Run As Fast As Julia". IBM. 2015.
  126. ^ "Basic Comparison of Python, Julia, R, Matlab and IDL". 2015. Archived from the original on 27 November 2017.
  127. ^ Gibbs, Mark (9 January 2013). "Pure and Julia are cool languages worth checking out". Network World (column). Retrieved 7 February 2013.
  128. ^ "Darwin/ARM64 tracking issue · Issue #36617 · JuliaLang/julia". GitHub. Retrieved 8 December 2020.
  129. ^ Carlton, Sam (8 December 2020). "ThatGuySam/doesitarm". GitHub. Retrieved 8 December 2020.
  130. ^ "Julia Downloads". julialang.org. Retrieved 17 May 2019.
  131. ^ "Bring Julia code to embedded hardware (ARM)". JuliaLang. 23 January 2019. Retrieved 30 March 2021.
  132. ^ "julia/arm.md". The Julia Language. 7 October 2021. Retrieved 15 May 2022. A list of known issues for ARM is available.
  133. ^ "Julia on TPUs". JuliaTPU. 26 November 2019. Retrieved 29 November 2019.
  134. ^ "Introducing: oneAPI.jl ⋅ JuliaGPU". juliagpu.org. Retrieved 6 September 2021.
  135. ^ "AMD ROCm · JuliaGPU". juliagpu.org. Retrieved 20 April 2020.
  136. ^ "Build Julia for RaspberryPi Zero". Gist. Retrieved 14 August 2020.
  137. ^ "JuliaBerry: Julia on the Raspberry Pi". juliaberry.github.io. Retrieved 14 August 2020.
  138. ^ "Julia available in Raspbian on the Raspberry Pi". Julia works on all the Pi variants, we recommend using the Pi 3.
  139. ^ "Julia language for Raspberry Pi". Raspberry Pi Foundation. 12 May 2017.
  140. ^ "Using Julia on Android?". JuliaLang. 27 September 2019. Retrieved 2 October 2019.
  141. ^ "Running Julia baremetal on an Arduino". seelengrab.github.io. Retrieved 24 May 2022.
  142. ^ "'Why We Created Julia' Turns Ten Years Old - Julia Computing". juliacomputing.com. Retrieved 18 February 2022.
  143. ^ "Newsletter January 2022 - Julia Growth Statistics - Julia Computing". juliacomputing.com. Retrieved 26 January 2022.
  144. ^ "Julia Computing Brings Support for NVIDIA GPU Computing on Arm Powered Servers - Julia Computing". juliacomputing.com (Press release). Retrieved 2 September 2021.
  145. ^ Modeling Spacecraft Separation Dynamics in Julia - Jonathan Diegelman, retrieved 6 September 2021
  146. ^ Circuitscape/Circuitscape.jl, Circuitscape, 25 February 2020, retrieved 26 May 2020
  147. ^ "Conservation through Coding: 5 Questions with Viral Shah | Science Mission Directorate". science.nasa.gov. Retrieved 26 May 2020.
  148. ^ "Julia in the Wild - Julia Data Science". juliadatascience.io. Retrieved 12 September 2022.
  149. ^ "Seven Rocky TRAPPIST-1 Planets May Be Made of Similar Stuff". Exoplanet Exploration: Planets Beyond our Solar System. Retrieved 6 October 2022.
  150. ^ Julia in Astronomy & Astrophysics Research | Eric B. Ford | JuliaCon 2022, retrieved 6 October 2022
  151. ^ JuliaSpace/SatelliteToolbox.jl, JuliaSpace, 20 May 2020, retrieved 26 May 2020
  152. ^ "Julia for HEP Mini-workshop". indico.cern.c h. 27 September 2021. Retrieved 23 August 2022. Julia and the first observation of Ω⁻_b → Ξ⁺_c K⁻ π⁻
  153. ^ Mikhasenko, Misha (29 July 2022). "ThreeBodyDecay". GitHub. Retrieved 23 August 2022.
  154. ^ Mikhasenko, Misha (July 2021). "Julia for QCD spectroscopy" (PDF). indico.cern.ch. Retrieved 23 August 2022. Summary: Julia is ready to be used in physics HEP analysis.
  155. ^ "JuliaHEP/UnROOT.jl". JuliaHEP. 19 August 2022. Retrieved 23 August 2022.
  156. ^ "Julia · Search · GitLab". GitLab. Retrieved 23 August 2022.
  157. ^ "Commits · master · sft / lcgcmake · GitLab". GitLab. Retrieved 23 August 2022. bump julia version to 1.7.3
  158. ^ "Release v1.3.0 · FRBNY-DSGE/DSGE.jl". GitHub. Retrieved 3 January 2022. New subspecs of Model1002 for estimating the DSGE with COVID-19 shocks
  159. ^ a b D'Cunha, Suparna Dutt (20 September 2017). "How A New Programming Language Created By Four Scientists Now Used By The World's Biggest Companies". Forbes. Archived from the original on 1 October 2022. Retrieved 1 October 2022.
  160. ^ "Julia for Election Security". Julia Community 🟣. Retrieved 27 September 2022.

Further reading


External links

Wikibooks has a book on the topic of: Introducing Julia
Programming languages
Free and open-source software
Numerical-analysis software
Mathematical optimization software
Statistical software
Differentiable computing
Authority control databases
Categories