By default, Julia is run similarly to scripting languages, using its runtime, and allows for interactions,[23] but Julia programs/source code can also optionally be sent to users in one ready-to-install/run file, which can be made quickly, not needing anything preinstalled.[27] Julia programs can also be (separately) compiled to binary executables, even allowing no-source-code distribution, and the executables can get much smaller with Julia 1.12. Such compilation is not needed for speed, though it can decrease constant-factor startup cost, since Julia is also compiled when running interactively, but it can help with hiding source code. Features of the language can be separately compiled, so Julia can be used, for example, with its runtime or without it (which allows for smaller executables and libraries but is limited in capabilities).
Julia programs can reuse libraries from other languages by calling them, e.g. calling C or Rust libraries, and Julia (libraries) can also be called from other languages, e.g. Python and R, and several Julia packages have been made easily available from those languages, in the form of Python and R libraries for corresponding Julia packages. Calling in either direction has been implemented for many languages, not just those and C++.
Work on Julia began in 2009, when Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman set out to create a free language that was both high-level and fast. On 14 February 2012, the team launched a website with a blog post explaining the language's mission.[4] 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."[20] Bezanson said he chose the name on the recommendation of a friend,[33] then years later wrote:
Julia's syntax is now considered stable, since version 1.0 in 2018, and Julia has a backward compatibility guarantee for 1.x and also a stability promise for the documented (stable) API, while in the years before in the early development prior to 0.7 the syntax (and semantics) was changed in new versions. All of the (registered package) ecosystem uses the new and improved syntax, and in most cases relies on new APIs that have been added regularly, and in some cases minor additional syntax added in a forward compatible way e.g. in Julia1.7.
In the 10 years since the 2012 launch of pre-1.0 Julia, the community has grown. The Julia package ecosystem has over 11.8 million lines of code (including docs and tests).[35] The JuliaCon academic conference for Julia users and developers has been held annually since 2014 with JuliaCon2020[36] welcoming over 28,900 unique viewers,[37] 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.[38]
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."[39] Also, Alan Edelman, professor of applied mathematics at MIT, has been selected to receive the 2019 IEEE Computer SocietySidney Fernbach Award "for outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language."[40]
Both Julia 0.7[41] 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.[42]Julia 1.6 was the largest release since 1.0, and it was the long-term support (LTS) version for the longest time, faster on many fronts, e.g. introduced parallel precompilation and faster loading of packages, in some cases "50x speedup in load times for large trees of binary artifacts".[43] Since 1.7 Julia development is back to time-based releases.[44] Julia1.7 was released in November 2021 with many changes, e.g. a new faster random-number generator and Julia1.7.3 fixed e.g. at least one security issue.[45] Julia1.8 was released in 2022 and 1.8.5 in January 2023,[46] with 1.8.x improvements for distributing Julia programs without source code, and compiler speedup, in some cases by 25%,[47] and more controllable inlining (i.e. now also allowing applying @inline at the call site, not just on the function itself). Julia 1.9 was released on 7 May 2023. It has many improvements, such as the ability to precompile packages to native machine code (older Julia versions also have precompilation for packages, but only partial, never fully to native code, so those earlier versions had a "first use" penalty, slowing down while waiting to fully compile). Precompiled packages, since version 1.9, can be up to hundreds of times faster on first use (e.g. for CSV.jl and DataFrames.jl), and to improve precompilation of packages a new package PrecompileTools.jl has been introduced. Julia 1.10 was released on 25 December 2023 with many new features, e.g. parallel garbage collection, and improved package load times and a new parser, i.e. it rewritten in Julia, with better error messages and improved stacktrace rendering.[48]
Julia 1.11 was released on 7 October 2024 (and 1.11.1 on 16 October), and with it 1.10.5 became the next long-term support (LTS) version (i.e. those are the only two supported versions), since replaced by 1.10.7 released on 26 November, and 1.6 is no longer an LTS version. Julia 1.11 adds e.g. a new public keyword to signal safe public API (Julia users are advised to use such API, not internals, of Julia or packages, and package authors advised to use the keyword, generally indirectly, e.g. prefixed with the @compat macro, from Compat.jl, to also support older Julia versions, at least the LTS version). Julia 1.11.1 has much improved startup (over 1.11.0 that had a regression), and over 1.10, and this can be important for some benchmarks.
Some users may want to postpone upgrading to 1.11 (e.g. those calling Julia from R), because of known temporary package incompatibility.
Much smaller binary executables are possible with juliac which is only available in the upcoming Julia 1.12 (the current "nightly" version).
JuliaCon
Since 2014,[49] 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[50] and the University of Maryland, Baltimore.[51] The event audience has grown from a few dozen people to over 28,900 unique attendees[52] during JuliaCon 2020, which took place virtually. JuliaCon 2021 also took place virtually[53] with keynote addresses from professors William Kahan, the primary architect of the IEEE 754 floating-point standard (which virtually all CPUs and languages, including Julia, use),[54] Jan Vitek,[55] Xiaoye Sherry Li, and Soumith Chintala, a co-creator of PyTorch.[56] 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.[57] 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.[58]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",[59] meaning to Firefox and other web browsers.[60][61][62][63] The Julia language is also supported by individual donors on GitHub.[64]
In June 2017, Julia Computing raised US$4.6million in seed funding from General Catalyst and Founder Collective,[67] 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",[68] and in December 2019 the company got $1.1million 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".[69] In July 2021, Julia Computing announced they raised a $24 million Series A round led by Dorilton Ventures,[70] 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".[71] In June 2023, JuliaHub received (again, now under its new name) a $13 million strategic new investment led by AE Industrial Partners HorizonX ("AEI HorizonX"). AEI HorizonX is a venture capital investment platform formed in partnership with The Boeing Company, which uses Julia.[72] Tim Holy's work (at Washington University in St. Louis's Holy Lab) on Julia1.9 (improving responsiveness) was funded by the Chan Zuckerberg Initiative.
By default, the Julia runtime must be pre-installed as user-provided source code is run. Alternatively, Julia (GUI) apps can be quickly bundled up into a single file with AppBundler.jl[27] for "building Julia GUI applications in modern desktop application installer formats. It uses Snap for Linux, MSIX for Windows, and DMG for MacOS as targets. It bundles full Julia within the app".[82]PackageCompiler.jl can build standalone executables that need no Julia source code to run.[23]
In Julia, everything is an object, much like object-oriented languages; however, unlike most object-oriented languages, all functions use multiple dispatch to select methods, rather than single dispatch.
Julia draws inspiration from various dialects of Lisp, including Scheme and Common Lisp, and it shares many features with Dylan, also a multiple-dispatch-oriented dynamic language (which features an infix syntax rather than a Lisp-like prefix syntax, while in Julia "everything"[83] 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:
Julia can be compiled to binary executables with PackageCompiler.jl.[23] Smaller executables can also be written using a static subset of the language provided by StaticCompiler.jl that does not support runtime dispatch (nor garbage collection, since excludes the runtime that provides it).[93]
Interaction
The Julia official distribution includes an interactive command-line read–eval–print loop (REPL),[94] with a searchable history, tab completion, and dedicated help and shell modes,[95] which can be used to experiment and test code quickly.[96] The following fragment represents a sample session example where strings are concatenated automatically by println:[97]
julia>p(x)=2x^2+1;f(x,y)=1+2p(x)yjulia>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.[98] Code can be tested inside Julia's interactive session or saved into a file with a .jl extension and run from the command line by typing:[83]
$ julia<filename>
Julia uses UTF-8 and LaTeX codes, allowing it to support common math symbols for many operators, such as ∈ for the in operator, typable with \in then pressing 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 the latest major release Unicode 15.0 (Julia1.11-DEV supports latest 15.1 point release[99])[100] for the languages of the world, even for source code, e.g. variable names (while it's recommended to use English for public code, and e.g. package names).
Julia is supported by Jupyter, an online interactive "notebooks" environment,[101] and Pluto.jl, a "reactive notebook" (where notebooks are saved as pure Julia files), a possible replacement for the former kind.[102] In addition Posit's (formerly RStudio Inc's) Quarto publishing system supports Julia, Python, R and Observable JavaScript (those languages have official support by the company, and can even be weaved together in the same notebook document, more languages are unofficially supported).[103][104]
The REPL can be extended with additional modes, and has been with packages, e.g. with an SQL mode,[105] for database access, and RCall.jl adds an R mode, to work with the R language.[106]
Use with other languages
Julia is in practice interoperable with other languages, in fact the majority of the top 20 languages in popular use. Julia can be used to call shared library functions individually, such as those written in C or Fortran, and packages are available to allow calling other languages (which do not provide C-exported functions directly), e.g. Python (with PythonCall.jl), R,[107] MATLAB, C# (and other .NET languages with DotNET.jl, from them with JdotNET), JavaScript, Java (and other JVM languages, such as Scala with JavaCall.jl). And packages for other languages allow to call to Julia, e.g. from Python, R (to Julia 1.10.x currently possible[108]), Rust, Ruby, or C#. Such as with juliacall (part of PythonCall.jl) to call from Python and a different JuliaCall package for calling, Julia up to 1.10.x, from R. Julia has also been used for hardware, i.e. to compile to VHDL, as a high-level synthesis tool, for example FPGAs.[76]
Julia has packages supporting markup languages such as HTML (and also for HTTP), XML, JSON and BSON, and for databases (such as PostgreSQL,[109] Mongo,[110] Oracle, including for TimesTen,[111] MySQL, SQLite, Microsoft SQL Server,[110] Amazon Redshift, Vertica, ODBC) and web use in general.[112][113]
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, code-lowering, and bootstrapping were implemented in FemtoLisp, a Scheme dialect, up to version 1.10.[117] Since that version the new pure-Julia stdlib package JuliaSyntax.jl is used for the parsing (while the old one can still be chosen)[118] which improves speed and "greatly improves parser error messages in various cases".[119] The LLVM compiler infrastructure project is used as the back end for generating optimized machine code for all commonly-used platforms. With some exceptions, the standard library is implemented in Julia.
Julia has four support tiers.[122] 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 first tier (for macOS); otherwise second tier on Linux, and ARMv7 (AArch32) on third tier.[123] Hundreds of packages are GPU-accelerated:[124] Nvidia GPUs have support with CUDA.jl (tier 1 on 64-bit Linux and tier 2 on 64-bit Windows, the package implementing PTX, for compute capability 3.5 (Kepler) or higher; both require CUDA 11+, older package versions work down to CUDA 9). There are also additionally packages supporting other accelerators, such as Google's TPUs,[125] and some Intel (integrated) GPUs, through oneAPI.jl,[126] and AMD's GPUs have support with e.g. OpenCL; and experimental support for the AMD ROCm stack.[127]
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.[128][129] Julia has been built for several ARM platforms, from small Raspberry Pis to the world's fastest (at one point, until recently) supercomputer Fugaku's ARM-based A64FX.[130]PowerPC (64-bit) has tier3 support, meaning it "may or may not build", and its tier will lower to 4 for 1.12, i.e. then no longer works. Julia is now supported in Raspbian[131] while support is better for newer Pis, e.g., those with Armv7 or newer; the Julia support is promoted by the Raspberry Pi Foundation.[132] Julia has also been built for 64-bit RISC-V,[133][134] that has some supporting code in core Julia.
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
Eclipse is an integrated development environment (IDE) used in computer programming. It contains a base workspace and an extensible plug-in system for customizing the environment. It is the second-most-popular IDE for Java development, and, until 2016, was the most popular. Eclipse is written mostly in Java and its primary use is for developing Java applications, but it may also be used to develop applications in other programming languages via plug-ins, including Ada, ABAP, C, C++, C#, Clojure, COBOL, D, Erlang, Fortran, Groovy, Haskell, HLASM, JavaScript, Julia, Lasso, Lua, NATURAL, Perl, PHP, PL/I, Prolog, Python, R, Rexx, Ruby, Rust, Scala, and Scheme. It can also be used to develop documents with LaTeX and packages for the software Mathematica. Development environments include the Eclipse Java development tools (JDT) for Java and Scala, Eclipse CDT for C/C++, and Eclipse PDT for PHP, among others.
Programming languages can be grouped by the number and types of paradigms supported.
Django is a free and open-source, Python-based web framework that runs on a web server. It follows the model–template–views (MTV) architectural pattern. It is maintained by the Django Software Foundation (DSF), an independent organization established in the US as a 501(c)(3) non-profit.
The following tables list notable software packages that are nominal IDEs; standalone tools such as source-code editors and GUI builders are not included. These IDEs are listed in alphabetic order of the supported language.
Geany is a free and open-source lightweight GUI text editor using Scintilla and GTK, including basic IDE features. It is designed to have short load times, with limited dependency on separate packages or external libraries on Linux. It has been ported to a wide range of operating systems, such as BSD, Linux, macOS, Solaris and Windows. The Windows port lacks an embedded terminal window; also missing from the Windows version are the external development tools present under Unix, unless installed separately by the user. Among the supported programming languages and markup languages are C, C++, C#, Java, JavaScript, PHP, HTML, LaTeX, CSS, Python, Perl, Ruby, Pascal, Haskell, Erlang, Vala and many others.
Protocol Buffers (Protobuf) is a free and open-source cross-platform data format used to serialize structured data. It is useful in developing programs that communicate with each other over a network or for storing data. The method involves an interface description language that describes the structure of some data and a program that generates source code from that description for generating or parsing a stream of bytes that represents the structured data.
Cython is a superset of the programming language Python, which allows developers to write Python code that yields performance comparable to that of C.
Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. It represents an attempt to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty.
ggplot2 is an open-source data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a number of defaults for web and print display of common scales. Since 2005, ggplot2 has grown in use to become one of the most popular R packages.
RStudio IDE is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser. The RStudio IDE is a product of Posit PBC.
Nim is a general-purpose, multi-paradigm, statically typed, compiled high-level system programming language, designed and developed by a team around Andreas Rumpf. Nim is designed to be "efficient, expressive, and elegant", supporting metaprogramming, functional, message passing, procedural, and object-oriented programming styles by providing several features such as compile time code generation, algebraic data types, a foreign function interface (FFI) with C, C++, Objective-C, and JavaScript, and supporting compiling to those same languages as intermediate representations.
WebAssembly (Wasm) defines a portable binary-code format and a corresponding text format for executable programs as well as software interfaces for facilitating communication between such programs and their host environment.
Tom's Obvious, Minimal Language is a file format for configuration files. It is intended to be easy to read and write due to obvious semantics which aim to be "minimal", and it is designed to map unambiguously to a dictionary. Originally created by Tom Preston-Werner, its specification is open source. TOML is used in a number of software projects and is implemented in many programming languages.
ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing. It offers several programming models: HIP, OpenMP, and OpenCL.
Project Jupyter is a project to develop open-source software, open standards, and services for interactive computing across multiple programming languages.
Microsoft, a tech company historically known for its opposition to the open source software paradigm, turned to embrace the approach in the 2010s. From the 1970s through 2000s under CEOs Bill Gates and Steve Ballmer, Microsoft viewed the community creation and sharing of communal code, later to be known as free and open source software, as a threat to its business, and both executives spoke negatively against it. In the 2010s, as the industry turned towards cloud, embedded, and mobile computing—technologies powered by open source advances—CEO Satya Nadella led Microsoft towards open source adoption although Microsoft's traditional Windows business continued to grow throughout this period generating revenues of 26.8 billion in the third quarter of 2018, while Microsoft's Azure cloud revenues nearly doubled.
Flux is an open-source machine-learning software library and ecosystem written in Julia. Its current stable release is v0.14.5 . It has a layer-stacking-based interface for simpler models, and has a strong support on interoperability with other Julia packages instead of a monolithic design. For example, GPU support is implemented transparently by CuArrays.jl. This is in contrast to some other machine learning frameworks which are implemented in other languages with Julia bindings, such as TensorFlow.jl, and thus are more limited by the functionality present in the underlying implementation, which is often in C or C++. Flux joined NumFOCUS as an affiliated project in December of 2021.
Jeff Bezanson is an American computer scientist best known for co-creating the Julia programming language with Stefan Karpinski, Alan Edelman and Viral B. Shah in 2012. The language spawned Julia Computing Inc. of which Bezanson is the CTO. As a founder of the company and co-creator of the language, Bezanson earned the 2019 J.H. Wilkinson Prize for his work on the Julia programming language alongside Shah and Karpinski. Bezanson is also listed as an author on academic papers regarding the Julia language.
References
↑ "Smoothing data with Julia's @generated functions". 5 November 2015. Archived from the original on 4 March 2016. 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. September 2017. Archived from the original on 23 January 2021. Retrieved 20 October 2014.
1 2 3 4 5 Jeff Bezanson; Stefan Karpinski; Viral Shah; Alan Edelman (February 2012). "Why We Created Julia". Julia website. Archived from the original on 2 May 2020. Retrieved 7 February 2013.
↑ "NVIDIA CUDA ⋅ JuliaGPU". juliagpu.org. Archived from the original on 29 January 2022. 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
↑ "JuliaCon 2016". JuliaCon. Archived from the original on 4 March 2017. Retrieved 6 December 2016. He has co-designed the programming language Scheme, which has greatly influenced the design of Julia
↑ Jeff Bezanson; Stefan Karpinski; Viral Shah; Alan Edelman; etal. "Julia 1.6 Highlights". julialang.org. Archived from the original on 26 March 2021. Retrieved 26 March 2021.
↑ "JuliaCon 2021". Juliacon.org. Archived from the original on 20 June 2021. Retrieved 20 June 2021.
↑ "JuliaCon 2021 Highlights". julialang.org. Archived from the original on 6 September 2021. 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.
↑ "Mozilla Research Grants 2019H1". Mozilla. Archived from the original on 9 October 2019. 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.
↑ "Literate scientific computing and communication for the web: iodide-project/iodide". iodide. 20 September 2019. Archived from the original on 24 August 2018. 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 a single, portable, sharable, and hackable file.
↑ "The Julia Language" (official website). Archived from the original on 21 February 2017. Retrieved 9 December 2016. General Purpose [..] Julia lets you write UIs, statically compile your code, or even deploy it on a webserver.
1 2 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.
↑ Anaya, Richard (28 April 2019). "How to create a multi-threaded HTTP server in Julia". Medium. Archived from the original on 25 July 2019. 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.
↑ 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).
↑ "julia/arm.md". The Julia Language. 7 October 2021. Archived from the original on 15 May 2022. Retrieved 15 May 2022. A list of known issues for ARM is available.
↑ "JuliaGPU". juliagpu.org. Archived from the original on 23 May 2020. Retrieved 16 November 2022. Almost 300 packages rely directly or indirectly on Julia's GPU capabilities.
↑ "Julia on TPUs". JuliaTPU. 26 November 2019. Archived from the original on 30 April 2019. Retrieved 29 November 2019.
↑ "Introducing Braket.jl - Quantum Computing with Julia". Julia Community 🟣. 15 November 2022. Archived from the original on 19 June 2024. Retrieved 23 February 2023. Almost all of the Python SDK's features are reimplemented in Julia — for those few that aren't, we are also providing a subsidiary package, PyBraket.jl, which allows you to translate Julia objects into their Python equivalents and call the Python SDK.
↑ "Julia for HEP Mini-workshop". indico.cern.c h. 27 September 2021. Archived from the original on 11 August 2022. Retrieved 23 August 2022. Julia and the first observation of Ω-_b → Ξ+_c K- π-
↑ Mikhasenko, Misha (29 July 2022). "ThreeBodyDecay". GitHub. Archived from the original on 23 August 2022. Retrieved 23 August 2022.
↑ Mikhasenko, Misha (July 2021). "Julia for QCD spectroscopy"(PDF). indico.cern.ch. Archived(PDF) from the original on 23 August 2022. Retrieved 23 August 2022. Summary: Julia is ready to be used in physics HEP analysis.
↑ Hobbs, Kerianne (December 2022). "Year of Autonomy in Alaskan Glaciers, Flight, Earth Orbit, Cislunar Space and Mars". Aerospace America Year in Review. p.48. Archived from the original on 19 June 2024. Retrieved 26 January 2023. The flight test team was able to demonstrate … a vertical takeoff and landing vehicle with both electric and conventional fuel propulsion systems onboard. The [uncrewed aerial system] was able to plan and execute these missions autonomously using onboard hardware. It was the first time the Julia programming language was flown on the embedded hardware - algorithms were precompiled ahead of time.
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