many modern HPC workloads are being ported to include tensorflow so they can do all sorts of in-process event categorization. https://www.nersc.gov/news-publications/nersc-news/science-n...

ML (not AI) will probably end up driving a lot of supercomputer workloads because distributed ML training resembles modern HPC simulation codes.

See https://www.olcf.ornl.gov/wp-content/uploads/2019/05/frontie...

The DOE is responsible for the USA's nuclear arsenal so I expect a few simulations of that nature.

this system is mostly for non-classified work; not clear just how much stockpile stewardship will occur on it.

Yep, actually all of our user projects are unclassified at the OLCF.

... except for on Summit.

"Exascale Deep Learning for Climate Analytics" won the 2018 Gordon bell using summit.

They have done that and it got an award because it was new and cool, but that is not where the majority of cycles goes. That is spent on fluid codes such as Raptor, XGC or Flash and to some extend kinetic simulations with codes such as VPIC or quantum codes such as QFCPACK.

There should be more information at https://www.olcf.ornl.gov/leadership-science/

I agree that the majority of the cycles go to traditional codes, but there is quite a bit of ML/AI happening on Summit.

From your link:

"New Frontiers for Material Modeling via Machine Learning Techniques"

"Large scale deep neural network optimization for neutrino physics"

"High-Fidelity Simulations of Gas Turbine Stages for Model Development using Machine Learning"

"HPC4mfg – Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput"

"Advances in Machine Learning to Improve Scientific Discovery"