[cctbxbb] Install OpenBLAS or MKL in base

[Billy Poon]

Hi Luc,

Also, we are exploring the use of conda for installing the dependencies
instead of compiling from scratch. There is some preliminary work in
https://github.com/cctbx/conda_build for dependencies and the
"conda_compiler" branch in cctbx_project for building. The
"cctbx_dependencies" conda package already installs mkl and it would be
easy to install openblas.

This should be ready in a few months once some other details (e.g. building
releases, filling in missing dependencies) are worked out.

--
Billy K. Poon
Research Scientist, Molecular Biophysics and Integrated Bioimaging
Lawrence Berkeley National Laboratory
1 Cyclotron Road, M/S 33R0345
Berkeley, CA 94720
Tel: (510) 486-5709
Fax: (510) 486-5909
Web: https://phenix-online.org


On Wed, Jun 20, 2018 at 9:48 AM Nigel Moriarty <nwmoriarty at lbl.gov> wrote:

> Luc
>
> I like the idea. I may be able to convince a new collaborator to drop
> FORTRAN in favour of the cctbx.
>
> Cheers
>
> Nigel
>
> ---
> Nigel W. Moriarty
> Building 33R0349, Molecular Biophysics and Integrated Bioimaging
> Lawrence Berkeley National Laboratory
> Berkeley, CA 94720-8235
> Phone : 510-486-5709     Email : NWMoriarty at LBL.gov
> Fax   : 510-486-5909       Web  : CCI.LBL.gov
>
> On Wed, Jun 20, 2018 at 5:13 AM, Luc Bourhis <luc_j_bourhis at mac.com>
> wrote:
>
>> Hi,
>>
>> years ago, I had wrote on this discussion list about a project of mine to
>> dramatically accelerate scitbx.lstbx by using optimised BLAS libraries.
>> That work had been dormant on a branch but now we are planning to move
>> forward with the help of Pascal Parlois who will do the actual coding in
>> the coming months. In order for this new code to be exercised by nightly
>> tests, we, the smtbx people, need to write code so that that optimised BLAS
>> library is installed during bootstrap. Right now, as a stopgap, I used
>> conda to install either OpenBLAS and MKL but that won’t do with the cctbx
>> philosophy.
>>
>> There are basically two choices: either OpenBLAS or MKL. When I started
>> this project, I chose OpenBLAS because MKL was not freely redistributable,
>> and I had got the green light to install it as needed. However now MKL has
>> become completely free, thus becoming a possible choice. MKL is more
>> performant on Intel but OpenBLAS is better on AMD (OpenBLAS actually runs
>> on pretty much any processor out there but we don’t care in the context of
>> cctbx): c.f. Julia people conclusions
>> <https://discourse.julialang.org/t/openblas-is-faster-than-intel-mkl-on-amd-hardware-ryzen/8033>
>> Now I realise that most of you may not care one way or another!?! But we
>> won’t move forward before we got the green light…
>>
>> Note that I am taking about installing a BLAS usable from C++ here, not
>> getting a BLAS-accelerated numpy. The latter could be an alternative
>> though. But that would require to modify the bootstrap code to compile a
>> MKL- or OpenBLAS-enabled numpy anyway.
>>
>> Best wishes,
>>
>> Luc J Bourhis
>>
>>
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>>
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