Some problems are too big to run even on these supercomputers. These machines are bound by extremely fast (speed/latency-wise) networks, and the work can't be scaled to multiple sites.
So yes, when you have a grand challenge, and no computer, you can't solve that challenge. You can continue playing with smaller versions of it though, but it won't give you the accuracy or detail you need/want.
So, the count is important, yes, but the capabilities of the clusters you have is also important.
> But at least US researchers cannot claim to be in an disadvantaged position compared to other countries?
I can't give a definitive answer to that, unfortunately. For one, I'm not working in the USA. Next, Europe has a lot of collaborations and cooperation under its belt in this regard. Lastly, China's ecosystem and prowess is completely opaque from me (both secrecy and not being interested/have time).
However, the technology is evolving pretty fast. NVIDIA's DPU, AMD/NVIDIA GPUs and AMD processors change the landscape a lot.
Intel's systems are not the best systems for heavily GPU accelerated clusters for a long time. So, they might look powerful in enterprise, but HPC is a completely different landscape. Intel is not the clear leader everything computing for some time now. They're not the worst, but they're not the best, either.
From what I understand it's not so much a "disadvantaged" problem as it is a time-sharing problem. There are a lot of demands on these and the wait list to perform calculations on these machines starts long before the machines are built.
If you're a researcher waiting on your turn to come up and it gets pushed out four years... and you still don't have an actual end in sight... Well a lot of those grants do expire and you may no longer be able to pay for the compute at all...
You might not need a whole machine, but part of it, and your turn may never come due to limited capacity. We operate on a different model, so our problems are a bit different, however we see other sites which operate with this model, and the researchers’ feelings and anxiety about getting their time and use it to its fullest.
Expiration of compute time or the time window before finishing a research is really crippling in some scenarios.
Some problems are too big to run even on these supercomputers. These machines are bound by extremely fast (speed/latency-wise) networks, and the work can't be scaled to multiple sites.
So yes, when you have a grand challenge, and no computer, you can't solve that challenge. You can continue playing with smaller versions of it though, but it won't give you the accuracy or detail you need/want.
So, the count is important, yes, but the capabilities of the clusters you have is also important.