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In an introductory Computational physics class of the type that many of us
give, time constraints lead to hard choices on topics. Since everyone likes to
include their own research in such a class I give fair time to visualzation,
but try to also give an overview of many areas.

Since about 2007  I have also included 2 lectures on parallel programming
algorithms  using MPI. Both the principle and the need to break the
``fear barrier'' of using  a large machine with a queuing system via ssh
have been sucessfully passed on. Due to the plateau in chip development and
to power considerations  future HPC hardware choices will include heavy use
of GPUs. Thus the need to introduce these at the level of an introductory
course has arisen. Just as for parallel coding explanation of the benefits
and simple examples to guide the hesitant  first time user have been selected.

I proposd several student projects using GPUs that include how-to pages, in
the style described in [1], so that these examples would  provide me with
material. I will describe  two of the more successful ones (a
lattice Boltzman and a Finite Element code, two topics that I previously
gave in short overviews), and link to new lecture pages that we developed.

[1] D. Mazvovsky, G. Halioua and Joan Adler, ``The role of projects in
(Computational) Physics Education'', Physics Procedia, 2012, Vol 34, p 1-5. 

Image at upper left is from a talk about my Computational Physics class.

See link at: http://phycomp.technion.ac.il/~newphr76ja/ath13/talktitle.html.