Re-posted from: http://juliacomputing.com/press/2017/05/25/github-top-ten.html
Cambridge, MA – Julia ranks as one of the top 10 programming languages developed on GitHub as measured by the number of stars and forks.
GitHub users star a repository in order to show appreciation and create a bookmark for easy access. Programmers fork a repository in order to add features or fix bugs and contribute to the project.
Julia ranks #10 in GitHub stars and #8 in forks among programming languages developed on GitHub.
Top GitHub Programming Languages
|Rank||Language||GitHub Stars||Number of Repositories|
Ranked by Number of GitHub Stars
About Julia Computing and Julia
Julia Computing (JuliaComputing.com) was founded in 2015 by the co-creators of the open source Julia language to develop products and provide support for businesses and researchers who use Julia.
Julia is the fastest modern high performance open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence. Julia combines the functionality and ease of use of Python, R, Matlab, SAS and Stata with the speed of Java and C++. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity. With more than 1 million downloads and +161% annual growth, Julia adoption is growing rapidly in finance, energy, robotics, genomics and many other fields.
Julia is lightning fast. Julia provides speed improvements up to
1,000x for insurance model estimation, 225x for parallel
supercomputing image analysis and 11x for macroeconomic modeling.
Julia is easy to learn. Julia’s flexible syntax is familiar and
comfortable for users of Python, R and Matlab.
Julia integrates well with existing code and platforms. Users of
Python, R, Matlab and other languages can easily integrate their
existing code into Julia.
Elegant code. Julia was built from the ground up for
mathematical, scientific and statistical computing, and has advanced
libraries that make coding simple and fast, and dramatically reduce
the number of lines of code required – in some cases, by 90%
Julia solves the two language problem. Because Julia combines
the ease of use and familiar syntax of Python, R and Matlab with the
speed of C, C++ or Java, programmers no longer need to estimate
models in one language and reproduce them in a faster
production language. This saves time and reduces error and cost.
Julia users, partners and employers looking to hire Julia programmers in 2017 include: Google, Apple, Amazon, Facebook, IBM, Intel, Microsoft, BlackRock, Capital One, PwC, Ford, Oracle, Comcast, DARPA, Moore Foundation, Federal Reserve Bank of New York (FRBNY), UC Berkeley Autonomous Race Car (BARC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Nobel Laureate Thomas J. Sargent, Brazilian National Development Bank (BNDES), Conning, Berkery Noyes, BestX, Path BioAnalytics, Invenia, AOT Energy, AlgoCircle, Trinity Health, Gambit, Augmedics, Tangent Works, Voxel8, Massachusetts General Hospital, NaviHealth, Farmers Insurance, Pilot Flying J, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Oak Ridge National Laboratory, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, MIT, Caltech, Stanford, UC Berkeley, Harvard, Columbia, NYU, Oxford, NUS, UCL, Nantes, Alan Turing Institute, University of Chicago, Cornell, Max Planck Institute, Australian National University, University of Warwick, University of Colorado, Queen Mary University of London, London Institute of Cancer Research, UC Irvine, University of Kaiserslautern, University of Queensland.
Julia is being used to: analyze images of the universe and research dark matter, drive parallel supercomputing, diagnose medical conditions, provide surgeons with real-time imagery using augmented reality, analyze cancer genomes, manage 3D printers, pilot self-driving racecars, build drones, improve air safety, manage the electric grid, provide analytics for foreign exchange trading, energy trading, insurance, regulatory compliance, macroeconomic modeling, sports analytics, manufacturing, and much, much more.