Yesterday I gave a short lightning talk at the
Inria Devmeetup
about Julia.
My intention was to show a few of the design aspects
of this rather recent programming language
and motivated from my experiences
in my
current PostDoc project at the MATHERIALS team
hint at interesting ongoing developments.
For this I chose to show some (hopefully) representative
code examples in a Jupyter notebook,
interactively running the Julia code
using an IJulia kernel.
For demonstrating Julia's multiple dispatch paradigm,
I hinted how functionality (like the mysquare
function in my example) can be easily implemented in a way
such that code can be combined with multiple different
computational backends.
Such backends include distributed array storage for large
chunks of data or arrays of static size,
where the size information may be used at compile-time
to speed up the byte code for small problems.
GPU backends are possible as well, but I did not go into this
in my presentation.
With respect to speed I show some timings from a heat equation
example (courtesy Antoine Levitt)
comparing a Julia, a python and a C implementation.
Last but not least I hinted at the interoperability
with python packages and Fortran code
and showed a plotting example,
where I used Zygote
to automatically compute and show the gradient
of a function using adjoint-mode automatic differentiation.
The notebook I used for the presentation is both attached
below and can be found
on github
as well.
For a rendered version of the notebook you can
open it in nbviewer.
