Last Thursday the PhDs of my graduate school gathered again for our
self-organised mini conference, named
Having organised this event myself
as well a couple of years back,
I had rather mixed feelings this time,
since I most likely not be around in Heidelberg for another AC.
For this reason I am very happy that the organisers
gave me the chance to once again make a contribution to this great event.
This time I was asked to repeat my interactive introductory talk
about the Bohrium automatic parallelisation framework.
I already presented about Bohrium in one of my c¼h lectures
at the Heidelberg Chaostreff earlier this year
and in fact this talk turned out to be very similar to the previous one.
Compared to the points mentioned in my
earlier blog post,
one should probably add a few things,
which have changed in Bohrium in the recent months.
First of all Bohrium has made quite some progress
regarding the interoperability with other efforts like cython, pyopencl
and pycuda for improving the performance of python scripts.
In fact Bohrium and these projects can now be used side-by-side
and will work together flawlessly to accelerate algorithms written in python.
Along a similar line, Bohrium started to look into mechanisms,
which could be used to speed up places where one would typically require
a plain python
Whilst this destroys the full compatibility with numpy on the one hand,
this allows on the other hand to increase performance in settings,
which are hard to write only as array operations.
On top of that the recent integration into the Spack
package manager makes it comparatively easy to install Bohrium
on any machine (including HPC clusters) to give it a try in a production
Spack section of the Bohrium documentation
for more details.
If you want to find out more about Bohrium,
I suggest you read my previous post
watch the recording of my previous talk.
For completeness I attach below the demonstration script I
used for both Bohrium presentations.