cdsousa

Wrong link, no? Shouldn't it be https://discourse.julialang.org/t/one-does-not-simply-walk-i... ?

We are free to come up with myths about the name origin. My favorite one is that it comes from Julia character of George Orwell's 1984 novel :D

This presentation is only a few days old.

Julia multiple dispatch allows dynamic polymorphism.

Check https://stackoverflow.com/questions/1801216/what-is-the-diff... and https://www.youtube.com/watch?v=kc9HwsxE1OY

This is very good news for people like me who would like to play with GPU kernels and arrays but usually have no access to NVIDIA/AMD hardware. (Although specific Intel hardware and Linux is being required for now.) I'm waiting for KernelAbstractions.jl integration.

AFAIK it is the opposite of what you have said. And although Julia has a lot of inspiration of Matlab syntax, it is absolutely not derived from it. https://julialang.org/blog/2012/02/why-we-created-julia/

That's right, I found it: https://docs.cupy.dev/en/stable/tutorial/kernel.html#kernel-...

""" [...] it is possible to perform kernel-like operations without actually writing your own GPU kernels: a = CUDA.zeros(1024) b = CUDA.ones(1024) a.^2 .+ sin.(b) """ [https://juliagpu.gitlab.io/CUDA.jl/usage/overview/#The-CuArr...]

As far as I know, that example code creates an ad-hoc kernel that performs the computation in a single pass.

I honestly don't know if that is possible in other frameworks.

Or maybe it should be "When on an online discussion someone mentions Julia, the probability of that discussion turning into a 0-based versus 1-based indexing discussion is 1"

There seems to be a kind of Godwin's law for Julia that states that "When on an online discussion someone mentions Julia, the probability of a complaint about 1-based indexing is 1".

That is pretty amazing! I wonder how would it look like if it was written in C. Anyone has some links to "Blobs" implementations in C?

In part, the fact that there are major problems moving the "giant" to where one would like (speed), e.g., unladen swallow, pypy, pyston, ...

Ha, ah! After a more careful read, I noticed this: "with the speed of C or Ruby"... I'm pretty sure Ruby performance was never a positive characteristic anywhere (right?). I wonder if this is a writing error or the journalist ignorance. Anyway, the article uncovers very nice facts about the history of Julia.

“Those people were our early converts—people who came for performance". That was entirely my case. After being using Python for a while and dropping to Cython in the performance bottlenecks (and evaluating other options), I was really longing for a language where I could have it all. When I first heard of Julia, in a highlighting package for Latex in the summer of 2013, my first thought was "oh no!, yet another high-level language, we already have Python people!". However, as soon as I read the "Why we created Julia" I just knew it was it, I finnaly could have my cake and eat it too. And in a matter of months I substituted almost all codes used in my half-the-way PhD to Julia.

In Julia:

    while true; print(rand(Bool) ? '╲' : '╱'); end