> Hi Bjoern,
> Is there anything else we (the Galaxy community) can do to help
> sort out the ATLAS installation problems?

Thanks for asking. I have indeed a few things I would like some

> Another choice might be to use OpenBLAS instead of ATLAS, e.g.
> http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration

I have no experience with it. Does it also need to turn off CPU
throttling? I would assume so, or how is it optimizing itself? 

> However, I think we build NumPy without using ATLAS or any
> BLAS library. That seems like the most pragmatic solution
> in the short term - which I think is what Dan tried here:
> http://testtoolshed.g2.bx.psu.edu/view/blankenberg/package_numpy_1_7

I can remove them if that is the consensus.

A few points:
- fixing the atlas issue can speed up numpy, scipy, R considerably (by
400% in some cases)
- as far as I understand that performance gain is due to optimizing
itself on specific hardware, for atlas there is no way around to disable
CPU throttling (How about OpenBlas?)
- it seems to be complicated to deactivate CPU throttling on OS-X
- binary installation does not make sense in that case, because ATLAS is
self optimizing
- Distribution shipped ATLAS packages are not really faster

Current state:
- Atlas tries two different commands to deactivate CPU throttling. Afaik
that only works on some Ubuntu versions, where no root privileges are
- If atlas fails for some reason, numpy/R/scipy installation should not
be affected (that's was at least the aim)

- Is it worth the hassle for some speed improvements? pip install numpy,
would be so easy?

- If we want to support ATLAS, any better idea to how to implement it?
Any Tool Shed feature that can help? -> interactive installation?
        - can we flag a tool dependency as optional? So it can fail?

- Can anyone help with testing and fixing it?

Any opinions/comments?

> Thanks,
> Peter

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