mauro3

We're teaching a course at ETH Zurich [1] where --besides the actual payload of solving partial differential equations (PDEs) on GPUs-- we put a lot of emphasis on "tools". Thus students learn how to use git and submit their homework via pushing to a repo of theirs on github, we teach testing and continuous integration, writing documentation, running code on a cluster, etc. In their final project, again submitted as a GitHub repo, they need to make use of all of theses skills (and of course solve some PDEs).

Note that excellent work in this space is done by the Software Carpentry project which exists since 1998 [2].

[1] https://pde-on-gpu.vaw.ethz.ch/ [2] https://software-carpentry.org/

This YT-video explains the 8355 method really well https://youtu.be/zB8cKBYNTps . It's how I learned it. They call the sexy-move the fishing-move.

Here some results for VSCode: https://github.com/Microsoft/vscode/issues/27378

Update: on my system Emacs has an average of 6ms vs VSCode 17ms.

X-ref: https://github.com/Microsoft/vscode/issues/27378

Here an Github issue from VSCode related to this: https://github.com/Microsoft/vscode/issues/27378 which shows some results for VSCode vs emacs, neovim and sublime.

The Swiss topographic office map has a similar feature "Journey through time": https://map.geo.admin.ch

Here zoomed in on one of the big glaciers, which shows how it retreated: https://map.geo.admin.ch/?topic=ech&lang=en&bgLayer=ch.swiss...

Whilst Julia's foreign function interface is indeed good and it is really easy to call into C, the point is that Julia itself is as fast as C. So you don't need to write any C code to get performance, instead just tune the bottlenecks in Julia itself.

For instance, the standard library of Julia [1] is written in Julia itself (and is very performant) and only calls into external C or Fortran libraries where there are well established code-bases (e.g. BLAS, FFTW). Compare this to, e.g., Python or R where much of the standard library is written in C.

[1] https://github.com/JuliaLang/julia/tree/master/base

The outlook toward Julia 1.0 was given by Stefan Karpinski in his JuliaCon 2016 talk: https://youtu.be/5gXMpbY1kJY . But yes, the plan is to have a 0.6 and then 1.0.

Aside: femtolisp is used in the Julia parser, and was created by one of (or the) main Julia contributors. A femtolisp REPL is included in the Julia executable:

   >> julia --lisp
  ;  _
  ; |_ _ _ |_ _ |  . _ _
  ; | (-||||_(_)|__|_)|_)
  ;-------------------|----------------------------------------------------------
  
  > (+ 1 2)
  3
  
  >

There are efforts under way to improve the standing of scientific programmers, for instance the UK initiative described in this post: http://software-carpentry.org/blog/2015/06/what-is-a-researc... .

There are now fellowships in the UK specifically aimed at scientific programmers: https://www.epsrc.ac.uk/funding/calls/rsefellowships/ . Even so, currently there no real career path for scientific programmers. Hopefully this will change in the future!