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So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
Earlier quoted context omitted.
They sell an enterprise version of Julia for $1500 per year. The main things you get are support, MS Excel integration, and a non-GPL license. They also offer consulting, training, and a few other flavors of Julia (large scale server based gets its own edition). I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.
Isn't Julia licensed under the MIT license? They advertise being MIT licensed on their website, and their Github repository is also under the MIT license.
From the website:
The core of the Julia implementation is licensed under the MIT license. Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL).
What is the business model? How are they making money?
They sell an enterprise version of Julia for $1500 per year. The main things you get are support, MS Excel integration, and a non-GPL license. They also offer consulting, training, and a few other flavors of Julia (large scale server based gets its own edition). I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.
So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
Here's a performance testimonial with links to real code and benchmarks against Fortran (not Python), from Steven G. Johnson, one of the authors of FFTW:
https://discourse.julialang.org/t/julia-vs-fortran-complaint...
I implemented an erfinv function Julia (https://github.com/JuliaLang/julia/pull/29874), and it was about 3x faster than Matlab or SciPy's erfinv function, both of which are taken from standard Fortran libraries. [...] I have found similar gains (compared to Fortran code called in SciPy) for other special functions, e.g. polygamma functions (https://github.com/JuliaLang/julia/pull/71255) and exponential integrals (https://github.com/JuliaMath/SpecialFunctions.jl/issues/19).
The reason Julia can beat the Fortran code is that metaprogramming makes it easy to apply performance optimizations that are awkward in Fortran. We have metaprogramming macros (@evalpoly) that can easily inline polynomial evaluations, whereas the Fortran code makes function calls that loop over look-up tables of polynomial coefficients. Even greater speedups are possible for evaluating polynomials of complex arguments, where there is a fancy recurrence from Knuth that is almost impossible to use effectively without code generation. In principle, the Fortran authors could have done the same inlining and gotten similar performance, but the code would have been much more painful to write by hand. (They could even have written a program to generate Fortran code, but that is even more painful.)
> I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
The Julia core is MIT licensed. The language distribution is currently GPL licensed due to linked dependencies, but there has been a no-GPL build flag available for several releases. GPL (and other) dependencies are continually being removed, and there are only one or two (GPL) libraries left in the currently nightly build.
What is the business model? How are they making money?
https://juliacomputing.com/products/ - JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance. - JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private…
As someone that uses Python and R for data analysis, is it worth learning this language?
So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
But what makes this really asinine is that Julia isn't even GPL: https://github.com/JuliaLang/julia/blob/master/LICENSE.md
Moreover, since I've been seeing this kind of hostility towards the GPL more and more often: it should be somewhat obvious to everyone that the current ecosystem of widely available, highest-quality OSS stacks is a rather surprising development. There are certainly parallel universes where parallel computing requires you to buy the compiler from IBM, and URIs end not in .html, but in .doc.
The GPL and its advocates aren't the only ones who created this, but I'm pretty sure they were at least necessary. That includes not just the software they created, but also the force of the moral argument for software freedom they made.