Julia (programming language)

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.[43] 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."[28] Bezanson said he chose the name on the recommendation of a friend,[44] then years later wrote:

Maybe julia stands for “Jeff’s uncommon lisp is automated”?[45]

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

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."[49] 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."[50]

Both Julia 0.7[51] 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;[52] 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.[53]

Julia 1.3 added composable multi-threaded parallelism and a binary artifacts system for Julia packages.[54]

Julia 1.4 added syntax for generic array indexing to handle e.g. 0-based arrays.[55] The memory model was also changed.[56]

Julia 1.5 released in August 2020 added record and replay debugging support,[57] 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",[58] 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.[59]

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".[60]

As of version 1.7 Julia development is back to time-based releases.[61]

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.[62]

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%,[63] 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.[64][65]

Julia is a general-purpose programming language,[88] while also originally designed for numerical/technical computing. It is also useful for low-level systems programming,[89] as a specification language,[90] High-level Synthesis (HLS) tool (for hardware, e.g. FPGAs),[91] and for web programming[92] at both server[93][94] and client[95][17] 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
• Call C functions directly without wrappers or special APIs
• Ability to interface with other languages, e.g. PythonCall.jl allows calling to or from Python (also possible with PyCall.jl[lower-alpha 2]), R with RCall.jl, and Java/Scala with JavaCall.jl
• shell-like abilities to manage other processes
• Designed for parallel and distributed computing
• Coroutines: lightweight green threading
• User-defined types are as compact as built-ins
• Automatic generation of code for different argument types
• Extensible conversions and promotions for numeric and other types
• 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, 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.[99]

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"[100] 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.

The Julia official distribution includes an interactive command-line read–eval–print loop (REPL),[101] with a searchable history, tab completion, and dedicated help and shell modes,[102] which can be used to experiment and test code quickly.[103] The following fragment represents a sample session example where strings are concatenated automatically by println:[104]

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.[105] 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:[100]

 $ 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[106]), 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).[107]

Julia is supported by Jupyter, an online interactive "notebooks" environment,[108] and Pluto.jl, a "reactive notebook" (where notebooks are saved as pure Julia files), a possible replacement for the former kind.[109]

The REPL can be extended with additional modes, and has been with packages, e.g. with an SQL mode,[110] for database access, and RCall.jl adds an R mode, to work with the R language.[111]

Julia has a built-in package manager and includes a default registry system.[113] 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.[114] 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.[115]

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[116] (that parser can however be switched out at runtime, with the pure-Julia package JuliaSyntax.jl,[117] a drop-in replacement, meant to be faster and it "greatly improves parser error messages in various cases",[118] 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[119]) is available, already used indirectly by some packages, but is also being considered for inclusion into Julia.[120] 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;[121] 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).[122][123][124][125] Development of Julia began in 2009 and an open-source version was publicized in February 2012.[4][126]

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.[141][142]

• Comparison of numerical-analysis software
• Comparison of statistical packages
• Differentiable programming
• JuMP – an algebraic modeling language for mathematical optimization embedded in Julia

• Nagar, Sandeep (2017). Beginning Julia Programming: For Engineers and Scientists. Springer. ISBN 9781484231715.
• Bezanson, J; Edelman, A; Karpinski, S; Shah, V. B (2017). "Julia: A fresh approach to numerical computing". SIAM Review. 59 (1): 65–98. arXiv:1411.1607. CiteSeerX 10.1.1.760.8894. doi:10.1137/141000671. S2CID 13026838.
• Joshi, Anshul (2016). Julia for Data Science － Explore the world of data science from scratch with Julia by your side. Packt. ISBN 9781783553860.
• 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.
• Kalicharan, Noel (2021). Julia - Bit by Bit. Springer. ISBN 978-3-030-73936-2.

Wikibooks has a book on the topic of: Introducing Julia

• Official website
• julia on GitHub