[QE-developers] Best Practices for GPU HPC Implementation (Spack, NCG Singularity, etc.)

[Hironori Kondo]

Hello QE dev team,

I hope this email finds you well. I am working on implementing QE in an HPC
environment, with GPU functionality. I know there are many questions of
this variety in the archives, but I think my question is more specific and
has not been discussed thus far. My university HPC support team recommended
that I contact the QE primary authors for the best guidance.

>From what I've seen, most people are installing QE with CUDA using the
standard GNU toolchain-style process with HPC SDK. My university is not too
keen on this, and recommends something tidier, if possible---Singularity,
Mamba/Conda, Spack, etc.

On the Singularity front, the latest image offered by Nvidia NCG is 7.1. I
suspect this is due to the transition to OpenAcc, but that's just a guess.
I would like to stick with 7.3.1, especially with improvements in EPW.

On the Conda front, there doesn't seem to be a GPU option by default. I
could try building within my Mamba environment with the standard process,
but that seems less neat.

On the Spack front, I see that there is QE 7.3.1, and there is a CUDA
variant: https://packages.spack.io/package.html?name=quantum-espresso#
However, it relies on the CUDA package (linked within the Spack page
above), not the separate HPC SDK package:
https://packages.spack.io/package.html?name=nvhpc
I'm not sure what implications this has.


I would be much obliged for your guidance on the best approach here. If it
is relevant, I am managing my HPC workflow in quacc by Princeton's Andrew
Rosen, which interfaces with QE via ASE.

Best,
Hiro

*Hironori Kondo*
Harvard College | Class of 2025
A.B. Candidate in Applied Mathematics
Concurrent A.M. in Chemistry
hirokondo at college.harvard.edu | hkondo at mit.edu
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