[QE-users] R: Data Parallelism and GPU Support for Quantum Espresso

[Pietro Davide Delugas]

Dear Prashant

For what concerns GPU, the branch you are referring to is outdated.
More recent releases of QE are available on the download page
https://www.quantum-espresso.org/download-page/
and they can all be compiled for CUDA GPUs

About the parallel execution: in QE, there is the manypw.x application that can run many inputs in parallel.

But for such a large number of systems, also considering that you'll be running a workflow for each of them, it is
much better to use a workflow manager, for example, AiiDA
https://www.aiida.net/sections/about.html

Best regards and greetings
Pietro
________________________________
Da: users <users-bounces at lists.quantum-espresso.org> per conto di Prashant Govindarajan via users <users at lists.quantum-espresso.org>
Inviato: giovedì 29 giugno 2023 22:49
A: users at lists.quantum-espresso.org <users at lists.quantum-espresso.org>
Oggetto: [QE-users] Data Parallelism and GPU Support for Quantum Espresso

Greetings,

I have been using Quantum Espresso for performing SCF calculations to compute the band gap and energies of crystal structures. Is there a way to perform DFT simulations on a large number of input crystals parallely using QE, i.e., data parallelism across multiple inputs (in addition to parallel calculations for each crystal)? For instance, what is the most optimal way to run the pw.x command for say 5000+ crystals?

Further, I am aware that there is GPU support for QE. There are issues while trying to install GPU-enabled QE which I am trying to figure out, but I was wondering if it provides reasonable speedup especially when I am dealing with multiple crystals. Also, I've been referring to https://gitlab.com/QEF/q-e-gpu for GPU-enabled QE installation. If there are any other useful resources please let me know.

I am using QE as an evaluation scheme for deep learning outputs, so an effective way for GPU-based DFT simulation would be of great help.

Thanks and Regards

Prashant Govindarajan
1st year PhD Student
Mila-Quebec AI Institute
Montreal, QC


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.quantum-espresso.org/pipermail/users/attachments/20230630/cde41bb4/attachment.html>