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Julia is a high-level programming language that can be used for many different things. It’s good for numerical analysis and computer science because of how it works.
Julia’s design is unique because it has a type system with parametric polymorphism in a dynamic programming language, and its main programming paradigm is multiple dispatches. Julia supports concurrent (composable), parallel, and distributed computing (with or without MPI or the built-in “OpenMP-style” threads) and direct calls to C and Fortran libraries without glue code. The Julia community calls Julia’s just-in-time (JIT) compiler “just-ahead-of-time” (JAOT) because Julia compiles all code to machine code before running it.
Julia automatically gets rid of garbage, uses eager evaluation, and has fast libraries for floating-point calculations, linear algebra, making random numbers, and matching regular expressions. There are many libraries to choose from, including some (like fast Fourier transforms) that used to be part of Julia but are now separate.
Integrated development environments, such as Microsoft’s Visual Studio Code, have an extension that helps with debugging and linting. Julia also has built-in tools such as a profiler (with support for flame graphs for the built-in one), a debugger, and the Debugger. jl package, which “supports repeated-execution debugging” [a] and more.
With a package that supports all of Julia’s features, it can be turned into a binary executable. Using a different package, you can also make small binary executables, but then the Julia runtime isn’t included in the executable, e.g., down to 9 KB (then without, for example, the garbage collector, which is part of Julia’s runtime, so with limited capabilities like the C language), for computers or even microcontrollers with 2 KB of RAM. By default, Julia code needs the Julia runtime to support all Julia features, such as threading. However, some Julia code that is not idiomatic can be compiled into small executables with limited Julia capabilities. In either case, there is no need to share the source code.
Since 2014, the Julia community has put on an annual conference for developers and users called Julia Conference. The first JuliaCon was in Chicago, and it was the start of the conference happening every year. Since 2014, the conference has been held at MIT, the University of Maryland, Baltimore, and other places. During JuliaCon 2020, which was held online, the number of people who came to the event grew from a few dozen to more than 28,900. JuliaCon 2021 also took place online, and professors William Kahan, Jan Vitek, Xiaoye Sherry Li, and Soumith Chintala gave keynote talks. Kahan is the main architect of the IEEE 754 floating-point standard, which is talked about in his keynote and is used by almost all CPUs and languages, including Julia (the co-creator of PyTorch). JuliaCon grew to have more than 300 presentations and 43,000 people attend (still freely accessible, plus older years). Between July 27 and July 29, 2022, Juliacon will also be held online for the first time in languages other than English. This will happen between July 27 and July 29.
In 2014, NumFOCUS took on the Julia language as a fiscally sponsored project to make sure the project would last for a long time. In its early days, Jeremy Kepner at MIT Lincoln Laboratory was the project’s first sponsor. A lot of money has also come from the Gordon and Betty Moore Foundation, the Alfred P. Sloan Foundation, Intel, and agencies like the NSF, DARPA, NIH, NASA, and FAA. Mozilla, the company that makes the Firefox web browser, gave a research grant for the first half of 2019 to pay for “a member of the official Julia team” to work on the “Bringing Julia to the Browser” project. This means that Julia will be added to Firefox and other web browsers. People on GitHub also donate money to help the Julia language.
In 2015, Viral B. Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski, and Keno Fischer started Julia Computing, Inc., which later became JuliaHub, Inc.
In June 2017, General Catalyst and Founder Collective gave Julia Computing US $4.6 million in seed funding. In the same month, the Alfred P. Sloan Foundation gave Julia Computing $910,000 to support open-source Julia development, including $160,000 to promote diversity in the Julia community. In December 2019, the US government gave Julia Computing $1.1 million to “develop a neural component machine learning tool to reduce the total energy consumption of heating.” In July 2021, Julia Computing said that they had raised $24 million in a Series A round. The round was led by Dorilton Ventures, which also owns the Formula 1 team Williams Racing. The Commercial Director of Williams said: “Dorilton’s strategy is to invest in companies that are building the best cloud technology, and Julia’s platform, with its revolutionary capabilities in simulation and modeling, is very important to our business. We’re excited to put Julia Computing into the sport with the most advanced technology in the world “.
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