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Comment by jampekka | Hacker News Reader

It would be interesting to see how much these "supercomputers" are actually used, and what parts of them are used.

I use my university's "supercomputer" every now and then when I need lots of VRAM, and there are rarely many other users. E.g. I've never had to queue for a GPU even though I use only the top model, which probably should be the most utilized.

Also, I'd guess there can be nvidia cards in the grid even if "the computer" is AMD.

Of course it doesn't matter for AMD whether the compute is actually used or not as long as it's bought, but lots of theoretical AMD flops standing somewhere doesn't necessarily mean AMD is used much for compute.

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It is a pretty safe bet that if someone builds a supercomputer there is a business case for it. Spending big on compute then leaving it idle is terrible economics. I agree with Certhas in that although this is not a consumer-first strategy it might be working. AMDs management are not incapable, for all that they've been outmanoeuvred convincingly by Nvidia.

That being said, there is a certain irony and schadenfreude in the AMD laptop being bricked from the thread root. The AMD engineers are at least aware that running a compute demo is an uncomfortable experience on their products. The consumer situation is not acceptable even if strategically AMD is doing OK.

I find it a safer bet that there are terrible economics all over. Especially when the buyers are not the users, as is usually the case with supercomputers (just like with all "enterprise" stuff).

In the cluster I'm using there's 36 nodes, of which 13 are currently not idling (doesn't mean they are computing). There are 8 V100 GPUs and 7 A100 GPUs and all are idling. Admittedly it's holiday season and 3AM here, but this it's similar other times too.

This is of course great for me, but I think the safer bet is that the typical load average of a "supercomputer" is under 0.10. And the less useful the hardware, the less will be its load.

It is not a reasonable assumption to compare your local cluster to the largest clusters within DOE or their equivalents in Europe/Japan. These machines regularly run at >90% utilization and you will not be given an allocation if you can’t prove that you’ll actually use the machine.

I do see the phenomenon you describe on smaller university clusters, but these are not power users who know how to leverage HPC to the highest capacity. People in DOE spend their careers working to use as much as these machines as efficiently as possible.

In Europe at least supercomputer are organised in tiers. Tier 0 are the highest grade, tier 3 are small local university clusters like the one you describe. Tier 2 or Tier 1 machines and upward usually require you to apply for time. They are definitely highly utilised. Tier 3 the situation will be very different from one university to the next. But you can be sure that funding bodies will look at utilisation before deciding on upgrades.

Also this amount of GPUs is not sufficient for competitive pure ML research groups from what I have seen. The point of these small decentral underutilized resources is to have slack for experimentation. Want to explore ML application with a master student in your (non-ML) field? Go for it.

Edit: No idea how much of the total hpc market is in the many small instalks, vs the fewer large ones. My instinct is that funders prefer to fund large centralised infrastructure, and getting smaller decentralised stuff done is always a battle. But that's all based on very local experience, and I couldn't guess how well this generalises.

    > It is a pretty safe bet that if someone builds a supercomputer there is a business case for it.

As I understand, most (95%+) of the market for supercomputers is gov't. If wrong, please correct. Else, what do you mean by "business case"?

When you ask your funding agency for an HPC upgrade or a new machine, the first thing they will want from you are utilisation numbers of current infrastructure. The second thing they will ask is why you don't just apply for time on a bigger machine.

Despite the clichés, spending taxpayer money is really hard. In fact my impression is always that the fear that resources get misused is a major driver of the inefficient bureaucracies in government. If we were more tolerant of taxpayer money being wasted we could spend it more efficiently. But any individual instance of misuse can be weaponized by those who prefer for power to stay in the hands of the rich...

At least where I'm from, new HPC clusters aren't really asked for by the users, but they are "infrastructure projects" of their own.

With the difficulty of spending taxpayer money, I fully agree. I even think HPC clusters are a bit of a symptom of this. It's often really hard to buy a beefy enough workstation of your own that would fit the bill, or to just buy time from cloud services. Instead you have to faff with a HPC cluster and its bureaucracy, because it doesn't mean extra spending. And especially not doing a tender, which is the epitome of the inefficiency caused by the paranoia of wasted spending.

I've worked for large businesses, and it's a lot easier to spend in those for all sorts of useless stuff, at least when the times are good. When the times get bad, the (pointless) bureaucracy and red tape gets easily worse than in gov organizations.

> At least where I'm from, new HPC clusters aren't really asked for by the users, but they are "infrastructure projects" of their own.

Because the users expect them to be renewed and improved. Otherwise the research can’t be done. None of our users tell us to buy new systems. But they cite us like mad, so we can buy systems every year.

The dynamics of this ecosystem is different.

> It would be interesting to see how much these "supercomputers" are actually used, and what parts of them are used.

I’m in that ecosystem. Access is limited, demand is huge. There’s literal queues and breakneck competition to get time slots. Same for CPU and GPU partitions.

They generally run at ~95% utilization. Even our small cluster runs at 98%.

Did your university not have a bioinformatics department?

It does. And meteorology, climatology and cosmology for example.

Well then I'm really unsure what's happening. Any serious researcher in either of those fields should be able and trying to expand into all available supercompute.

Maybe they just don't need them? At least a bioinformatics/computational science professor I know runs most of his analyses on a laptop.

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