Comment by dekhn | Hacker News Reader

Comment by dekhn | Hacker News Reader

Parent Full thread

There's many variables that go into supercomputers, of which "company/country propaganda" is just one of them.

Supercomputer admins would love to have a single code that used the whole machine, both the compute elements and the network elements, at close to 100%. In fact they spend a significant fraction on network elements to unblock the compute elements, but few codes are really so light on networking that the program scales to the full core count of the machine. So, instead they usually have several codes which can scale up to a significant fraction of the machine and then backfill with smaller jobs to keep the utilization up (because the acquisition cost and the running cost are so high).

Supercomputers have limited utility- beyond country bragging rights, only a few problems really justify spending this kind of resource. I intentionally switched my own research in molecular dynamics away from supercomputers (where I'd run one job on 64-128 processors for a 96X speedup) to closet cllusters, where I'd run 128 indpendent jobs for a 128X speedup, but then have to do a bunch of post-processing to make the results comparable to the long, large runs on the supercomputer (https://research.google/pubs/cloud-based-simulations-on-goog...). I actually was really relieved when my work no longer depended on expensive resources with little support, as my scientific productivity went up and my costs went way down.

I feel that supercomputers are good at one thing: if you need to make your country's flagship submarine about 10% faster/quieter than the competition.

Ever used GPT-3, DALL-E, or other LLMs?

The GPUs used to train them only existed because the DoE explicitly worked with Nvidia on a decade-long roadmap for delivery in it's various supercomputers, and would often work in tandem with private sector players to coordinate purchases and R&D (for example, protein folding and just about every Big Pharma company).

Hell, the only reason AMD EPYC exists is for the same reason.

Yes. In my computational history I have: used the largest non-classified DOE supercomputers, built my own modest closet clusters, developed an embarassingly parallel computing system using Google's idle prod cycles, and helped debug training of LLMs when I worked on the TPU team at Google. I work for big pharma now (and my phd is in biophysics) and I'm also comfortable with other HPC domains.

I know the DOE/Nvidia history quite well as the Chief Scientist of NVIDIA visited LBL around 2005(6? 7?) and talked about their new hardware they were just starting to build and sell, with the goal of getting them into supercomputers.

We asked if they had double precision performance yet (because that was a must for many supercomputer jobs), but at the time, nvidia DP was still lagging SP (I guess it still does?) and we also quibbled about their non-compliance with some esoteric details in IEEE 754. The best part of the whole talk was when he walked us through the idea of visualizing our operations by drawing the matrices as textures, because you can easily see the NaNs- they render as nvidia Green!

I left DOE (Berkeley Lab) shortly after to work in industry because it was clear that ML wasn't going to be innovated in the government labs.

Thank you for that comment, as someone who has had exposure to the DOE as well (and LBL at that) I can only echo your sentiment. I'd even go further and state that is hard for me to believe that any innovation can happen in the calcified structures of governmental labs. Maybe the classified ones are different.

The "DOE made NVIDIA" myth is a story I haven't seen pushed outside the DOE complex. It is true that the supercomputers the DOE pushes could be considered industry subsidies, by providing industry companies with a steady customer with a very high tolerance for unfinished products. That applies to NVIDIA, AMD, Intel, HPE/Cray and IBM more or less equally.

I also want to stress what often gets overlooked: supercomputers are hell to operate and use. Aurora runs on Slingshot, a Cray interconnect. Those things look good on paper. Examples: Cray Aries (and "network quiesces") or Cray DataWarp. Who knows how Slingshot actually works in practice, for a hero run it only needs to hold things together for a few hours. As long as you get a high TOP500 ranking, a supercomputer is a success.

There is no market for those things anymore and they are beholden to the same economics as everything else, hence codes that can't afford an army of PostDocs to work around the bugs and design decisions that are only necessary due to scale of those systems are better suited to plain old mid-range clusters. And I haven't even mentioned the eccentric userland of supercomputers.

There are many reasons the DOE affords to run those behemoths. Some more trivial and petty than most people would like to believe. Like the author of the parent post, I have come to believe that the best bang for the buck on scientific output can be found elsewhere.

> I have come to believe that the best bang for the buck on scientific output can be found elsewhere.

The best bang for the buck is never at the very top. The top is just for the biggest bang.

>The GPUs used to train them only existed because the DoE explicitly worked with Nvidia

Do you have a source for this claim? Isn't eg. an H100 basically just a RTX GPU with more and faster memory? (Or, at least, an RTX GPU with the same VRAM as an H100 would perform similarly.) And these GPUs were created to run video games. Unless you are referring to something like NVLink?

> Unless you are referring to something like NVLink

Yep

> an H100

H100 NVL is the SKU for HPC.

> I feel that supercomputers are good at one thing: if you need to make your country's flagship submarine about 10% faster/quieter than the competition.

Or doing Numerical Weather Prediction. :-)

But seriously, as a cluster sysadmin, the “128 jobs, followed by post-processing” is great for me, because it lets those separate jobs be scheduled as soon as resources are available.

> expensive resources with little support

Unfortunately, there isn’t as much funding available in places for user training and consultation. Good writing & education is a skill, and folks aren’t always interested in a job that has is term-limited, or whose future is otherwise unclear.

Not all jobs can be parallelized in that manner without communication.

Thanks for the great reply and for linking the research paper - I am going to check it out. Could you suggest a good review paper for someone new to get into modern HPC?

It depends what you are looking for. This provides a good overview:

https://hpc.llnl.gov/documentation/tutorials/introduction-pa...

This is a very detailed free book focusing on programming:

https://theartofhpc.com

But HPC is very diverse. Some care about compute performance, others about memory bandwidth and others about IO performance. Some run a ton of small jobs while others run a single large job.

Reply on news.ycombinator.com