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Comment by TheRealPomax | Hacker News Reader

How did this make it to the top 30... This "race" has been a global endeavour since the 50's, and no one keeps the title because that's not how progress works.

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The rankings come out every June and November. They’re always talked about here because big computers are really cool.

I don’t think we ever talk about them here. The rankings are fairly irrelevant to what most of us do, and the benchmarks used to derive them are too narrow anyways.

I mean I don’t really want to argue about it because it seems like a silly thing to argue about, but just because you aren’t interested doesn’t mean nobody else is. Here’s a thread from the November list: https://news.ycombinator.com/item?id=15707800

> no one keeps the title because that's not how progress works.

Wrong [0]

> This "race" has been a global endeavour since the 50's

From the article:

> But the United States retook the lead thanks to a machine, called Summit, built for the Oak Ridge National Laboratory in Tennessee.

When computers are used for nuclear weapons simulation, it's clearly not a global endeavor.

[0] https://www.top500.org/lists/2017/11/

Just a point of order: Summit isn’t used for weapons work. You have to go down to 6 and 7 on the current list to find one that is.

The DOD stopped reporting many of their computers to the list though, so it’s anyone’s guess where those would land.

Edit: US weapons work that is. I would imagine China uses at least one of theirs for classified work.

Google & Amazon also aren't reporting their computer fleet as well. However its likely Google is #1 on that list on raw compute available to R&D efforts.

No, it's not. Google's data centers would probably perform like crap on the kind of workloads run on supercomputers.

I'm not so sure. It's normally about the interconnect with supercomputers, but modern datacenters have really pushed that boundary quite a bit. They still have higher latency in some regards, but they have a lot of reconfigurable hardware on the ethernet adapters that pushes down the latency in interesting ways.

I think he/she is talking about google TPU pods which can do 11.2 petaflops each. That's what they are offering google compute customers. Internally they probably have faster and larger pods by now as they usually publish about this stuff 6-12 months after they have moved on to something better.

It depends what you mean by a supercomputer. Having a lot of computers in data centers doesn't mean it's a supercomputer. Of course you can change the definition and call it # of cpus or compute "capacity" and such.

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