Comment by cr0sh | Hacker News Reader

Comment by cr0sh | Hacker News Reader

So - on a more "applies to ordinary mortals" level - the fact that they are going to use all AMD components is intriguing.

In reference to AI, NVidia has things "locked up" with CUDA, versus 2nd cousin AMD's OpenCL.

From what I understand, it is possible to recompile TensorFlow (for instance - not that ORNL will be using TF) for OpenCL - but I don't know how well it works. Personally, I've only used TF with CUDA.

Does this mean we might see greater/better support for OpenCL in the AI realm? Might we seem it become on-par with CUDA because of this collaboration for this HPC?

Or will things stay as-is, at least "down here" in the consumer/business realm of AI hardware and applications? Do things like this trickle down, or are things so customized and/or proprietary for the needs of HPC at ORNL (or elsewhere) that anything to do with AI on this machine will have little to no bearing outside of the lab?

Ultimately, I'd just like to see another choice (a lower cost choice!) for GPU in the world of consumer/enthusiast/hobbyist AI/DL/ML - while today's higher-end GPUs, no matter the manufacturer, tend to be fairly expensive, AMD still has an edge here that make them attractive to users (not to mention the fact that their Linux drivers are open-source, which is also a plus).

I doubt they are going to run much AI on that machine. The national labs mostly run "traditional HPC" workloads such as fluid codes that simulate (magneto)hydrodynamics in one way or another.

... 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"

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.

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...

HIP (https://rocm-documentation.readthedocs.io/en/latest/Programm...) is basically 1-1 with CUDA, and can be almost automatically generated from CUDA (https://github.com/ROCm-Developer-Tools/HIP/tree/master/hipi...). The datasheet doesn't mention OpenCL so evidently it isn't a priority any more.

I'd tend to assume, with the amount of resources going into this, that the software will be coded at a lower level here than it would be in a typical dev environment and so NVidia's library advantages will be less salient?

They'll probably use ROCm rather than OpenCL.

But ROCm isn't an API is it? It's a "platform," whatever that means, and so software still has to use either hip or OpenCL, right?

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