Julia (programming language)

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Julia prog language.svg
Official Julia logo
Paradigm Multi-paradigm: multiple dispatch ("object-oriented"), procedural, functional, meta, multistaged[1]
Designed by Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah
Developer Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors[2][3]
First appeared 2012; 6 years ago (2012)[4]
Stable release
1.0.2[5] / 8 November 2018; 10 days ago (2018-11-08)
Preview release
1.1.0-DEV / daily updates
Typing discipline Dynamic, nominative, parametric,Optional
Implementation language Julia, C, Scheme (the parser; using the FemtoLisp implementation), assembly and dependencies (i.e. LLVM) in C++; standard library: Julia (mostly), C (a few dependencies), Fortran (for BLAS)[6]
Platform IA-32, x86-64
OS Linux, macOS, Windows and community support for FreeBSD
License MIT (core),[2] GPL v2;[6][7] a makefile option omits GPL libraries[8]
Filename extensions .jl
Website JuliaLang.org
Influenced by

Julia is a high-level general-purpose[12] dynamic programming language that was originally designed to address the needs of high-performance numerical analysis and computational science, without the typical need of separate compilation to be fast,[13][14][15][16] also usable for client and server web use,[17][18] low-level systems programming[19] or as a specification language.[20]

Distinctive aspects of Julia's design include a type system with parametric polymorphism and types in a fully dynamic programming language and multiple dispatch as its core programming paradigm. It allows concurrent, parallel and distributed computing, and direct calling of C and Fortran libraries without glue code.

Julia is garbage-collected,[21] uses eager evaluation and includes efficient libraries for floating-point calculations, linear algebra, random number generation, and regular expression matching. Many libraries are available, and some of them (e.g. for fast Fourier transforms) were previously bundled with Julia.[22]


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.[23] There is no official reason for the name "Julia".[24]

Since the 2012 launch, the Julia community has grown, with over 2,000,000 downloads as of August 2018.[25] The JuliaCon[26] academic conference for Julia users and developers has been held annually since 2014.

Version 0.3 was released in August 2014, version 0.4 in October 2015, and version 0.5 in October 2016.[27] Versions 0.5 and earlier are no longer maintained.[28] Julia 0.6 was released in June 2017,[29] and was the stable release version until 8 August 2018.

Both Julia 0.7 (that's a useful release for testing packages, and knowing how to upgrade them for 1.0[30]) and the related version 1.0 were released on 8 August 2018. Work on Julia 0.7 was a "huge undertaking" (e.g. because of "entirely new optimizer"), and some changes were made to the syntax (with the syntax now stable, and same for 1.x and 0.7) and semantics; the iteration interface was simplified.[31]

The release candidate for Julia 1.0 (Julia 1.0.0-rc1) was released on 7 August 2018 and the final version a day later. The team has written that code which runs without warnings on Julia 0.7, will run identically on Julia 1.0.[32]

Buxfix releases are expected roughly monthly, and Julia 1.0.1 and 1.0.2 have followed that schedule (no such bugfix releases in the pipeline for 0.7-release). In addition for new (compatible) language features, Julia 1.1 is expected on November 15, 2018 and Julia 1.2 is due February 15, 2019.

Notable uses

Julia has attracted some high-profile clients, from investment manager BlackRock, which uses it for time-series analytics, to the British insurer Aviva, which uses it for risk calculations. In 2015, the Federal Reserve Bank of New York used Julia to make models of the US economy, noting that the language made model estimation "about 10 times faster" than before (previously used MATLAB). Julia's co-founders established Julia Computing in 2015 to provide paid support, training, and consulting services to clients, though Julia itself remains free to use. At the 2017 JuliaCon[33] conference, Jeffrey Regier, Keno Fischer and others announced[34] that the Celeste project[35] used Julia to achieve "peak performance of 1.54 petaFLOPS using 1.3 million threads"[36] on 9300 Knights Landing (KNL) nodes of the Cori (Cray XC40) supercomputer (the 5th fastest in the world at the time; 8th fastest as of November 2017). Julia thus joins C, C++, and Fortran as high-level languages in which petaFLOPS computations have been written.

Language features

According to the official website, the main features of the language are:

  • Multiple dispatch: providing ability to define function behavior across many combinations of argument types
  • Dynamic type system: types for documentation, optimization, and dispatch
  • Good performance, approaching that of statically-typed languages like C
  • A built-in package manager
  • Lisp-like macros and other metaprogramming facilities
  • Call Python functions: use the PyCall package[a]
  • Call C functions directly: no wrappers or special APIs
  • Powerful shell-like abilities to manage other processes
  • Designed for parallel and distributed computing
  • Coroutines: lightweight green threading
  • User-defined types are as fast and compact as built-ins
  • Automatic generation of efficient, specialized code for different argument types
  • Elegant and extensible conversions and promotions for numeric and other types
  • Efficient support for Unicode, including but not limited to UTF-8

Multiple dispatch (also termed multimethods in Lisp) is a generalization of single dispatch – the polymorphic mechanism used in common object-oriented programming (OOP) languages – 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 be subtyped, but composition is used over inheritance, that is used by traditional object-oriented languages (see also inheritance vs subtyping).

Julia draws significant 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"[41] 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

By default, the Julia runtime must be pre-installed as user-provided source code is run, while another way is possible, where a standalone executable can be made that needs no Julia source code built with BuildExecutable.jl.[42][43]

Julia's syntactic macros (used for metaprogramming), like Lisp macros, are more powerful and different from 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.


The Julia official distribution includes an interactive session shell, called Julia's read–eval–print loop (REPL), which can be used to experiment and test code quickly.[44] The following fragment represents a sample session example where strings are concatenated automatically by println:[45]

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.[46] Code that 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:[41]

 $ julia <filename>

Julia is supported by Jupyter, an online interactive "notebooks" environment.[47]

Use with other languages

Julia's ccall keyword is used to call C-exported or Fortran shared library functions individually.

Julia has Unicode 11.0 support, with UTF-8 used for strings (by default) and for Julia source code, meaning allowing as an option common math symbols for many operators, such as ∈ for the in operator.

Julia has packages supporting markup languages such as HTML, (and also for HTTP), XML, JSON and BSON; and for database and web use in general.


Julia's core is implemented in Julia, C (and the LLVM dependency is in C++), assembly and its parser in Scheme ("FemtoLisp"). 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), the standard library is implemented in Julia itself. The most 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),[48] although these benchmark claims are often disputed.[49][50] Development of Julia began in 2009 and an open-source version was publicized in February 2012.[4][51]

Current and future platforms

While Julia uses JIT[52] (MCJIT[53] from LLVM) – Julia generates native machine code directly, before a function is first run (not bytecodes that are run on a virtual machine (VM) or translated as the bytecode is running, as with, e.g., Java; the JVM or Dalvik in Android).

Current support is for 64-bit and 32- x86 processors (all except for ancient pre-Pentium 4-era, to optimized for newer), while Julia also supports more, e.g. "fully supports ARMv8 (AArch64) processors, and supports ARMv7 and ARMv6 (AArch32) with some caveats."[54] Other platforms (other than those mainstream CPUs; or non-mainstream operating systems), have "Community" support, or "External" support (meaning in a package), e.g. for GPUs.

At least some platforms may need to be compiled from source code (e.g. the original Raspberry Pi), with options changed, while the download page has otherwise executables (and the source) available. Julia has been "successfully built" on several ARM platforms, up to e.g. "ARMv8 Data Center & Cloud Processors", such as Cavium ThunderX (first ARM with 48 cores). ARM v7 (32-bit) and ARM v8 (64-bit) has "Official" support and binaries (first to get after x86), while PowerPC (64-bit) has "Community" support and PTX (64-bit) (meaning Nvidia's CUDA on GPUs) has "External" support.

Julia is now supported in Raspbian[55] while support is better for newer (e.g.) ARMv7 Pis; the Julia support is promoted by the Raspberry Pi Foundation.[56] Support for GNU Hurd is being worked on (in JuliaLang's openlibm dependency project).[57]

Julia2C source-to-source compiler

A Julia2C source-to-source compiler from Intel Labs is available.[58] This source-to-source compiler is a fork of Julia, that emits C code (and makes the full Julia implementation not needed, for that generated C code) instead of native machine code, for functions or whole programs; this makes Julia effectively much more portable, as C is very portable with compilers available for most CPUs. The compiler is also meant to allow analyzing code at a higher level than C.[59]

Intel's ParallelAccelerator.jl[60] can be thought of as a partial Julia to C++ compiler (and then to machine code transparently), but the objective is parallel speedup (can be "100x over plain Julia", as in when benchmarked and compared to older Julia 0.4.4-pre version;[61] newer major versions of Julia such as 1.0, and 0.6 that ParallelAccelerator now supports, have been getting faster), while it also did in some cases also speed up serial code many-fold. It's not compiling the full Julia language to C++ (and C++ is only an implementation detail) and later versions might not compile to C++ as that's not the objective; it doesn't need to compile all of Julia's syntax, as the rest is handled by Julia.

Julia Computing company

Julia Computing, Inc. was founded by Viral B. Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski and Keno Fischer.[62]

In June 2017, Julia Computing raised $4.6M in seed funding from General Catalyst and Founder Collective.[63]

See also


  1. ^ Calling newer Python 3 also works[37][38] (and PyPy[39]) and calling in the other direction, from Python to Julia, is also supported with pyjulia.[40]


  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.
  3. ^ "Contributors to JuliaLang/julia". GitHub.
  4. ^ a b c d e f g h "Why We Created Julia". Julia website. February 2012. Retrieved 7 February 2013.
  5. ^ "v1.0.2". Github.com. 2018-11-08. Retrieved 2018-11-09.
  6. ^ a b "Julia". Julia. NumFocus project. Retrieved 9 December 2016. Julia's Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for ...
  7. ^ "Non-GPL Julia?". Groups.google.com. Retrieved 2017-05-31.
  8. ^ "Introduce USE_GPL_LIBS Makefile flag to build Julia without GPL libraries". 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.
  9. ^ a b c d "Home · The Julia Language". docs.julialang.org. Retrieved 2018-08-15.
  10. ^ "Programming Language Network". GitHub. Retrieved 6 December 2016.
  11. ^ "JuliaCon 2016". JuliaCon. Retrieved 6 December 2016. He has co-designed the programming language Scheme, which has greatly influenced the design of Julia
  12. ^ "The Julia Language" (official website). General Purpose [..] Julia lets you write UIs, statically compile your code, or even deploy it on a webserver.
  13. ^ Bryant, Avi (15 October 2012). "Matlab, R, and Julia: Languages for data analysis". O'Reilly Strata. Archived from the original on 2014-04-26.
  14. ^ Singh, Vicky (23 August 2015). "Julia Programming Language – A True Python Alternative". Technotification.
  15. ^ Krill, Paul (18 April 2012). "New Julia language seeks to be the C for scientists". InfoWorld.
  16. ^ Finley, Klint (3 February 2014). "Out in the Open: Man Creates One Programming Language to Rule Them All". Wired.
  17. ^ "Escher : With Escher you can build beautiful Web Uls entirely in Julia". Shasi.github.io. Retrieved 2017-05-31.
  18. ^ "Getting Started with Node Julia · Node Julia". Node-julia.readme.io. Retrieved 2017-05-31.
  19. ^ 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.
  20. ^ Moss, Robert (26 June 2015). "Using Julia as a Specification Language for the Next-Generation Airborne Collision Avoidance System". Archived from the original on 1 July 2015. Retrieved 29 June 2015. Airborne collision avoidance system
  21. ^ "Suspending Garbage Collection for Performance...good idea or bad idea?". Groups.google.com. Retrieved 2017-05-31.
  22. ^ (now available with using FFTW in current versions; that dependency is one of many 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 2018-03-01.
  23. ^ Jeff Bezanson, Stefan Karpinski, Viral Shah, Alan Edelman. "Why We Created Julia". JuliaLang.org. Retrieved 5 June 2017.
  24. ^ "There's no good reason, really. It just seemed like a pretty name.", Stefan Karpinski, New Julia language seeks to be the C for scientists, Infoworld, 18 April 2012
  25. ^ "Julia Computing". juliacomputing.com. Retrieved 2018-08-15.
  26. ^ "JuliaCon website". juliacon.org. Retrieved 2018-05-10.
  27. ^ The Julia Blog
  28. ^ Julia Download (Old Releases), julialang.org
  29. ^ https://julialang.org/blog/2017/06/julia-0.6-release
  30. ^ "What is Julia 0.7? How does it relate to 1.0?". JuliaLang. Retrieved 2018-10-17.
  31. ^ Jeff Bezanson, Stefan Karpinski, Viral Shah, Alan Edelman et al. "Writing Iterators in Julia 0.7". julialang.org. Retrieved 2018-08-05.
  32. ^ What is Julia 0.7? How does it relate to 1.0?, Stefan Karpinski, 27 March 2018
  33. ^ "JuliaCon 2017". juliacon.org. Retrieved 2017-06-04.
  34. ^ Fisher, Keno. "The Celeste Project". juliacon.org. Retrieved 24 June 2017.
  35. ^ Regier, Jeffrey; Pamnany, Kiran; Giordano, Ryan; Thomas, Rollin; Schlegel, David; McAulife, Jon; Prabat (2016). "Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference". arXiv:1611.03404 [cs.DC].
  36. ^ Claster, Andrew (12 September 2017). "Julia Joins Petaflop Club". Julia Computing (Press release). Celeste is written entirely in Julia, and the Celeste team loaded an aggregate of 178 terabytes of image data to produce the most accurate catalog of 188 million astronomical objects in just 14.6 minutes [..] a performance improvement of 1,000x in single-threaded execution.
  37. ^ "PyCall.jl". stevengj. github.com.
  38. ^ "Using PyCall in julia on Ubuntu with python3". julia-users at Google Groups. to import modules (e.g. python3-numpy)
  39. ^ "Polyglot.jl". wavexx. github.com.
  40. ^ "python interface to julia".
  41. ^ a b "Learn Julia in Y Minutes". Learnxinyminutes.com. Retrieved 2017-05-31.
  42. ^ "Build a standalone executables from a Julia script".
  43. ^ ".jl to .exe". Groups.google.com. Retrieved 2017-05-31.
  44. ^ "Getting Started · The Julia Language". docs.julialang.org. Retrieved 2018-08-15.
  45. ^ See also: https://docs.julialang.org/en/stable/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).
  46. ^ "Julia Documentation". JuliaLang.org. Retrieved 18 November 2014.
  47. ^ "Project Jupyther".
  48. ^ "Julia: A Fast Dynamic Language for Technical Computing" (PDF). 2012.
  49. ^ "How To Make Python Run As Fast As Julia". 2015.
  50. ^ "Basic Comparison of Python, Julia, R, Matlab and IDL". 2015.
  51. ^ Gibbs, Mark (9 January 2013). "Pure and Julia are cool languages worth checking out". Network World (column). Retrieved 7 February 2013.
  52. ^ "Support MCJIT". Github.com. Retrieved 26 May 2015.
  53. ^ "Using MCJIT with the Kaleidoscope Tutorial". 22 July 2013. Retrieved 26 May 2015. The older implementation (llvm::JIT) is a sort of ad hoc implementation that brings together various pieces of the LLVM code generation and adds its own glue to get dynamically generated code into memory one function at a time. The newer implementation (llvm::MCJIT) is heavily based on the core MC library and emits complete object files into memory then prepares them for execution.
  54. ^ julia: The Julia Language: A fresh approach to technical computing, The Julia Language, 2018-02-01, retrieved 2018-02-01, A list of known issues for ARM is available.
  55. ^ "Julia available in Raspbian on the Raspberry Pi". Julia works on all the Pi variants, we recommend using the Pi 3.
  56. ^ "Julia language for Raspberry Pi". Raspberry Pi Foundation.
  57. ^ "Fix building tests on GNU/kFreeBSD and GNU/Hurd by ginggs · Pull Request #129 · JuliaLang/openlibm". Github.com. Retrieved 2017-05-31.
  58. ^ "julia/j2c at j2c · IntelLabs/julia". Github.com. Retrieved 2017-05-31.
  59. ^ "Google Groups". groups.google.com. Retrieved 2018-08-15. By translating Julia to C, we leverage the high-level abstractions (matrix, vector, ..), which are easier to analyze, and can potentially add the rich extensions of C (like openmp, tbb, ...).

    The tool may also extend Julia to new architectures where the only available tool chain is for C
    Translation from C to Julia might be harder.
  60. ^ "The ParallelAccelerator package, part of the High Performance Scripting project at Intel Labs". Intel Labs.
  61. ^ Lindsey Kuper (2016-03-01). "An introduction to ParallelAccelerator.jl". JuliaLang.org. Retrieved 2017-05-31.
  62. ^ "About Us – Julia Computing". juliacomputing.com. Retrieved 2017-09-12.
  63. ^ https://juliacomputing.com/press/2017/06/19/funding.html

External links

  • Official website
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