Julia (programming language)
|Paradigm||Multi-paradigm: multiple dispatch ("object-oriented"), procedural, functional, meta, multistaged|
|Designed by||Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman|
|Developer||Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors|
|Typing discipline||Dynamic, nominative, parametric|
|OS||Linux, OS X, FreeBSD, Windows|
|License||MIT / GPL v2 A make-file option drops the GPL libraries|
|MATLAB, Lisp, C, Fortran, Mathematica (strictly its Wolfram Language), Python, Perl, R, Ruby, Lua|
Julia is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general-purpose programming, web use or as a specification language. Distinctive aspects of Julia's design include a type system with parametric 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, uses eager evaluation and includes efficient libraries for floating-point calculations, linear algebra, random number generation, fast Fourier transforms and regular expression matching.
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
- 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 capabilities for managing other processes
- Designed for parallelism and distributed computation
- 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 known as multimethods in Lisp) is a generalization of single dispatch – the polymorphic mechanism used in common object oriented (OO) 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 (such as an ALGOL-like free-form infix syntax rather than a Lisp-like prefix syntax, while in Julia "everything" is an expression) – also a multiple-dispatch-oriented dynamic language – and Fortress, another numerical programming language with multiple dispatch and a sophisticated parametric type system. While 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 compilation and execution phases. The language features are summarized in the following table:
|Language||Type system||Generic functions||Parametric types|
|Common Lisp||dynamic||opt-in||yes (but no dispatch)|
|Dylan||dynamic||default||partial (no dispatch)|
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
The Julia official distribution includes an interactive session shell, called Julia's REPL, which can be used to experiment and test code quickly. The following fragment represents a sample session on the REPL:
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
? after the prompt (preceding each command), respectively. The REPL also keeps the history of commands, even between sessions. For other examples, see the Julia documentation, which gives 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 (for example):
$ julia <filename>
Julia is also supported by Jupyter, an online interactive "notebooks" environment (project Jupyter is a multi-language extension, that "evolved", from the IPython command shell; now includes IJulia). See for other ways in the next section.
To use Julia with other languages
ccall keyword is used to call C-exported or Fortran shared library functions individually.
Julia has Unicode support, with UTF-8 used for source code and e.g. optionally allowing common math symbols for many operators, such as ∈ for the
in operator. For strings UTF-8, UTF-16 and UTF-32 (and ASCII) are fully supported encodings.
Julia's core is implemented in C and C++ (the LLVM dependency is in C++), its parser in Scheme ("femtolisp"), and the LLVM compiler framework is used for just-in-time (JIT) generation of 64-bit or 32-bit optimized machine code (i.e. not for VM) depending on the platform Julia runs on. With some exceptions (e.g., libuv), 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). Development of Julia began in 2009 and an open-source version was publicized in February 2012.
Current and future platforms
Current support is for newer x86 or older i386 processors and in 0.4.x: 32-bit ARM architecture ("Experimental and early support" with "work in progress - several tests are known to fail, and backtraces are not available" with alpha support for Raspberry Pi 1/2 but "[on ARMv7] Samsung Chromebook [..] Julia starts up just fine"), 64-bit ARMv8 (AArch64) and PowerPC being worked on, with almost no open specific issues, with binaries available for POWER7 ("due to some small support from IBM") and POWER8, that are expected to have official beta support as of 0.5 (at least for non-parallel support).
Julia2C source-to-source compiler
A Julia2C source-to-source compiler from Intel Labs is available. This source-to-source compiler is a fork of Julia, that implements the same Julia language syntax, which emits C code (for compatibility with more CPUs) instead of native machine code, for functions or whole programs. The compiler is also meant to allow analyzing code at a higher level than C.
- Calling newer Python 3 also works (and PyPy) and calling in the other direction, from Python to Julia, is also supported with pyjulia. Even calling recursively (back and forth) between these languages is possible, without (or with) using Polyglot.jl, that supports additional languages to Python.
- "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.
- "LICENSE.md". GitHub.
- "Contributors to JuliaLang/julia". GitHub.
- "Why We Created Julia". Julia website. February 2012. Retrieved 7 February 2013.
- Non-GPL Julia?
- "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 lets you build from source without any GPL library dependencies.
- "The Julia Language" (official website).
- Bryant, Avi (15 October 2012). "Matlab, R, and Julia: Languages for data analysis". O'Reilly Strata.
- Krill, Paul (18 April 2012). "New Julia language seeks to be the C for scientists". InfoWorld.
- Finley, Klint (3 February 2014). "Out in the Open: Man Creates One Programming Language to Rule Them All". Wired.
- "Escher lets you build beautiful interactive Web UIs in Julia". Retrieved 27 July 2015.
- "Getting Started with Node Julia". node-julia.
- Moss, Robert (26 June 2015). "Using Julia as a Specification Language for the Next-Generation Airborne Collision Avoidance System". Archived from the original on 11 July 2015. Retrieved 29 June 2015.
- "Suspending Garbage Collection for Performance...good idea or bad idea?".
- "PyCall.jl". stevengj. github.com.
- "Using PyCall in julia on Ubuntu with python3". julia-users at Google Groups.
to import modules (e.g. python3-numpy)
- "Polyglot.jl". wavexx. github.com.
- Learn X in Y minutes: Where X=Julia at learnxinyminutes.com
- Julia REPL documentation
- See also: http://julia.readthedocs.org/en/latest/manual/strings/ for string interpolation and the
string(greet, ", ", whom, ".\n")example for preferred ways to concatenate strings. While the
+operator is not used for string concatenation, it could easily be defined to do so. Julia has the println and print functions, but also a @printf macro, while not in a function form, to eliminate run-time overhead of formatting (unlike the same function in C).
- "Julia Documentation". julialang.org. Retrieved 18 November 2014.
- Learn Julia in Y Minutes
- "Chris Lattner discusses the name LLVM". Retrieved 22 December 2011.
- "Julia: A Fast Dynamic Language for Technical Computing" (PDF). 2012.
- Gibbs, Mark (9 January 2013). "Pure and Julia are cool languages worth checking out". Network World (column). Retrieved 7 February 2013.
- "Support MCJIT". Retrieved 26 May 2015.
- "Using MCJIT with the Kaleidoscope Tutorial". 22 July 2013. Retrieved 26 May 2015.
- "Cross-compiling for ARMv6". Retrieved 16 May 2015.
I believe #10917 should fix this. The CPU used there arm1176jzf-s.
- "ARM build failing during bootstrap on Raspberry Pi 2". Retrieved 16 May 2015.
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.
- "Porting Julia to PowerPC". Retrieved 9 May 2015.
Wow, the latest git allows me to build to completion.
- "IBM Power port".
I am hoping we can have beta support from the 0.5 release onwards for sequential julia. We were able to do this work due to some small support from IBM.
- "Julia2C initial release".
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.
|Wikibooks has a book on the topic of: Introducing Julia|