[QE-users] [QE-GPU] Performance of the NGC Container

[Paolo Giannozzi]

The GPU acceleration of DFT-D3, using openacc,  as well as its MPI
parallelization, was implemented no more than a few days ago and will
appear in the next release (soon). Apparently DFT-D3 takes a non-negligible
amount of time. Without MPI parallelization or GPU acceleration, it may
easily become a bottleneck when running on many processors, or on GPUs.

Paolo

On Tue, Jul 6, 2021 at 7:44 PM Jonathan D. Halverson <
halverson at princeton.edu> wrote:

> Hello (@Louis Stuber),
>
> The QE container on NGC (
> https://ngc.nvidia.com/catalog/containers/hpc:quantum_espresso) appears
> to be running very well for us on a node with two A100's for the "AUSURF112,
> Gold surface (112 atoms), DEISA pw" benchmark. We see a speed-up of 8x in
> comparison to running on 80 Skylake CPU-cores (no GPUs) where the code was
> built from source.
>
> The procedure we used for the above is here:
>
> https://researchcomputing.princeton.edu/support/knowledge-base/quantum-espresso
>
> However, for one system we see a slow down (i.e., the code runs faster
> using only CPU-cores). Can you tell if the system below should perform well
> using the container?
>
> "My system is basically just two carbon dioxide molecules and doing a single point
> calculation on them using the PBE-D3 functional and basically just altering
> the distance between the two molecules in the atomic coordinates."
>
> Can someone comment in general on when one would expect the container
> running on GPUs to outperform a build-from-source executable running on
> CPU-cores?
>
> CUDA-aware MPI is nice. It appears that the container is configured to use
> the MPI libraries in the container instead of those installed for the local
> cluster. Is this true? Can users take advantage of their local CUDA-aware
> MPI libraries?
>
> Jon
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-- 
Paolo Giannozzi, Dip. Scienze Matematiche Informatiche e Fisiche,
Univ. Udine, via delle Scienze 206, 33100 Udine, Italy
Phone +39-0432-558216, fax +39-0432-558222
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