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Comment by alpineidyll3 | Hacker News Reader

I have my Ph.D. in this exact field from UC Berkeley. My thesis was about polynomial scaling algorithms for quantum molecular systems. I was a postdoc at Harvard. I received an NSF career award. I know exactly what I'm talking about lol.

These computer platforms are drastically inefficient on a flop / $ basis. They exist to funnel money into the pockets of the companies who assemble them. They never ever achieve even a tiny fraction of their peak rated flops on any calculation that has any scientific meaning.

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It sounds like your experience is exclusively with open-science DoE user facility machines. What you're saying is true for the bulk of working scientists, not enough of whom are given funding to make efficient code -- just to make working code. However, even on those systems there is some good competition for the Gordon Bell prize each year.

Meanwhile, Defense and closed-science systems of similar scale continue to be used at very good efficiency on problems that are strictly non-feasible on smaller clusters. The leadership-class systems are prestigious, and that prestige helps drive needed technological advances, even if the places that need them aren't in the university system.

Can I ask you what's standing in critical path to obtaining scientifically efficient compute?

Asking as a private user of commercially non trivial compute, but very short on the research depth required to translate optimal thinking into efficiency.

Edit: we're similarly bound by eg PDE solutions. We've found the greatest improvements in rolling our own storage. Not purely capex improvements but orders in ingest.

Probably, everyone is coding software using electron.

(While I am picking on electron, the truth is, if compute power exists, it seems DEVs never need to reign in code, or worry about efficiency.

Where I work, we purposefully set DEVs loose in VMs with minimal RAM, minimal CPU. If your app can't work with small RAM, and small CPU, how on Earth will it scale to 100s of requests per second? Compute costs.)

If your pde has spatial locality, parallelization works. I suspect you might be talking about multidimensional diffusions such as occur in finance. These decompose locally in price space. If the pde is nonlocal there is very little that works in general. I wrote a paper on parallelization in the time dimension, but it works poorly.

> These computer platforms are drastically inefficient on a flop / $ basis. They exist to funnel money into the pockets of the companies who assemble them. They never ever achieve even a tiny fraction of their peak rated flops on any calculation that has any scientific meaning.

Well... yeah.

Because these supercomputers also need communication networks so that they can actually work on such a large problem together. So any "large computer" is going to be slower than any "small personal computer" because communication costs grow with the size of a computer.

A computer with 1-million cores needs more than 1-million times the communication than a computer with 1-core. That's just the innate issues of complexity theory, Ahmdal's law, and other such fundamental compute problems.

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But the "small personal computer" is *impossible* to work on a larger problem. The "small computer" doesn't even have enough RAM to even hold a problem that these supercomputers work on, let alone the time/energy needed to finish the problem within 2 months.

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At a minimum, supercomputers are needed to solve and verify the models of the next-generation of computers. Its not like these chips with 8-billion transistors in them are correct on their first design. The design is iterated upon, simulated, and verified before hardware is made. These simulation steps happen on a computer, and a rather large one at that.

most supercomputer codes have communication patterns that scale sublinearly with node count.

Also this is not just a hardware issue. Many HPC systems in academic research have very mixed workloads that are hard to optimise the design of a HPC system for. The majority of researchers have little interest or time to spend optimising their code, they are seldom have enough experience in programming or software engineering to know how optimise without assistance (in our case we pick out the worst system hogs that are affecting other users on our HPC systems).

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