i don't understand the concept of needing a specific amount of performance in a supercomputer to do research. You go to publication with the system you have. I;m sure some researcher calculated that if they had X more resources, their simulation would be able discover new things in regime Y, but you know what? Often computer scientists find ways to solve problems that traditionally required a supercomputer- but can be solved on a desktop.

Chasing larger supercomputers is a folly, one we do to remain visually competitive with other countries who want to take the US's crown. But they benefit very few while costing a lot- not a good investment except for problems like "I need my submarine to be 5% more streamlined to remain competitive".

It's not necessarily about the machine's raw performance, but capacity. The compute is a shared utility, and the utilization of supercomputer is quite high. We expect science to grow exponentially. So, compute demand grows exponentially too. The cloud is crazy expensive compared to supercomputing -- you don't want missing compute to hold back research.

If science grows exponentially (rather than linearly or sublinearly), you're screwed.

I don't think the cloud is expensive compared to supercomputing- so long as you consider TCO and you're doing proper deal negotiation. I would much rather than an evergreen cluster in the cloud with fast interconnect that I turn on for myself and shutdown when I'm done, than share some resource and try to keep utilization high.

This is an odd take. Do we even need 6GHz CPUs or should we be content to stay with 500MHz and better algorithms? There's a limit to what algorithmic improvements can do and improving algorithms is hard. If I had a 6,000 core 60THz computer I could make use of it.

500 MHz? Don't be ridiculous. I had 75 MHz when I was a kid and it was enough.

Good question. It's because some extreme scale computer science and application work can only be valid with the high core counts/networking available on what is now just one single supercomputer in the U.S. - That machine is now booked, leaving many unable to complete research or have to wait until 2024. Some research can only be done with that scope of system, and people have been waiting years to test exascale software on this machine. I hope this answers the question.

I question your fundamental premise. For example, I used to use supercomputers to run molecular dynamics simulations. When I found that the supercomputer folks didn't want me to run codes (because at the time, AMBER didn't scale to the largest supercomputers) on their machines, I moved to cloud and grid computing and then built a system to use all of Google's idle cycles to run MD. We achieved our scientific mission without a supercomputer! In the meantime, AMBER was improved to scale on large machines, which "justifies" running it on supercomputers (the argument being that if you spend 15% of the cost of the machine on interconnect, the code better use the interconnect well to scale, but can't be embarassingly parallel).

I've seen scientists who are captive to the supercomputer-industrial complex and it's not that they need this specialized tool to answer a question definitively. It's to run sims to write the next paper and then wait for the next supercomputer. Your cart is pushing the horse.

You know the term "embarrassingly parallel" but you seem to ignore that this term exits because there are other classes of problem which lack this characteristic.

Quite a few important problems are heavily dependent on interconnects, e.g. large-scale fluid dynamics and simulations that are coupled with such dynamics: aerodynamics, acoustics, combustion, weather and climate, oceanographic, seismic, astrophysics and nuclear. A primary component of the simulation is fast wavefronts that propagate globally through the distributed scalar and/or vector fields.

As long as there is a future where computers are growing to increase the scope, fidelity, and speed of these applications, there is also a need for infrastructure research to validate or develop new methods to target these new platforms. There are categories of grants that are written to a roadmap, with interlocking deliverables between contracts. These researchers do not have the luxury to only propose work that can be done with COTS materials already in the marketplace.

And conversely, if your application just needs a lot of compute and doesn't need the other expensive communication and IO aspects of these new, leading-edge machines, it _does_ make sense that your work get redirected to other less expensive machines for high-throughput computing. This is evidence of the research funding apparatus working well to manage resources, not evidence of mismanagement or waste.

One thing I've learned is that even when folks think their problem can only be solved in a particular way (fast interconnect to implement the underlying physics) there is almost always another way, that is cheaper and solves the problem, mainly by applying cleverer ideas.

I'll give (yet another) AMBER example. At some point in the past AMBER really only scaled on fast interconnects. But then somebody realized the data being passed around could be compressed before transmit and then decompressed on the other end- all faster than it could be sent over the wire. Once the code was rewritten, the resulting engine scaled better- on all platforms, including ones that had wimply (switched gigabit) interconnect. It reduced the cost of doing the same experiments significantly, by making it possible to run identical problems on less/cheaper hardware.

Second- I really do know a fair amount in this field, having worked on both AMBER on supercomputers (with strong scaling) and Folding@Home (which explicitly demonstrated that many protein folding problems never needed a "supercomputer").

I do not know much about your field of molecular dynamics. But, it is my lay understanding that it tends to have aspects of sparse models in space, almost like a finite-element model in civil engineering. Upon this, you have higher level equations and geometry to model forces or energy transfer between atoms. It may involve quadratic search for pairwise interactions and possibly spatial search trees like kdtrees to find nearby objects. Is that about right? And protein folding is, as I understand it, high throughput because it is a vast search or optimization problem on very small models.

Compared with fluid dynamics, I think your problem domain has much higher algorithmic complexity per stored byte of model data. Rather than representing a set of atoms or other particles, typical fluid simulations represent regions of space with a fixed set of per-location scalar or vector measurements. A region is updated based on a function that always views the same set of neighbor regions. Storage and compute size scales with the spatial volume and resolution, not with the amount of matter being simulated. These other problems are closer in spirit to convolution over a dense matrix, which often has so few compute cycles per byte that it is just bandwidth-limited in ripping through the matrix and updating values. But, due to the multiple dimensions, it is also ugly traversals rather than a simple linear streaming problem.

Intel has been locked in MBA sociopath organizational antipatterns for a decade now. There's nothing controversial about your post, it's just stating the obvious.

As others pointed out, the real controversy should be ... did Intel just take the government money and run? Even worse, did they sit on these funds while AMD was perfectly capable of delivering exascale and prevent AMD from providing a second computer? In terms of government boondoggles it probably is peanuts though.

The most shocking part of your post is that AMD got funding to deliver a computer somewhere else. I would have thought Intel could have blocked that with lobbying.

Lobbying is just businesses asking congress people for things. You can't offer them anything of value in return, then it becomes bribery which is highly illegal. Not saying it doesn't happen, but don't equate lobbying with bribery.

>Lobbying is just businesses asking congress people for things. You can't offer them anything of value in return, then it becomes bribery which is highly illegal.

Some of those "congress people" (or their family members) were employed earlier in the firms they pass legislation for, and some are employed later. Still corruption in a different way.

As someone who spent a bit of time in academia, looking back at it, those folks (grad students and postdocs) really under-invest in understanding good career development.

Academics really should know better than to make bets like these.

When I read this, my thought isn't "Intel screwed several researcher's lives", but "Postdocs/grad students need much better coaching". Unfortunately, we know that professors are the wrong people to provide such guidance. If you want to improve things, I would suggest putting your effort into highlighting the larger problem.

Had Intel delivered, the problem I speak of would still be widespread and the hurt will continue.

when it comes to research, never put all your eggs in one basket. This is something that should be learned at the early stages of graduate school, not at the postdoc level.

Hype is hype.

It’s blows that they over promised and got bit by it. And others being bit by it also blows. But betting your academic and professional career in the promises of a third party company, /especially a semiconductor one/, is naive at best and stupid at worst.

Claiming “Intel failed science” is the worst kind of dramatic hyperbole.

Also, this whole idea that some people can’t finish their post docs etc because the computer isn’t ready wreaks of poor planning on their part simply because they were never guaranteed that their project would even properly run/work/scale/whatever on the new system without potentially years more of work.

This is why I wish this thread hadn't gone more mainstream.

It's nuanced and specific to how things work for researchers at labs.

I do not expect non-HPC folks to get why it is a big deal and why the strong language was needed. Intel failed the science community in the U.S. that relies on the limited systems large enough to handle the very few applications that consume massive numbers of cores/parallelism. That's not the whole of science, but this system was central to some of the grandest-scale problem solving that exists (think planet-scale climate simulations in high resolution).

I respect your opinion but my opinion wasn't for you. It was for the HPC comm.

You know that many of us here are HPC folks who know the nuances... and simply disagree? I mean, sure, I don't think Intel should have taken the contract or gotten any positive PR for this, but at the fundamental level, many people on this site did supercomputing and HPC at national labs or universities, and now work on machine learning HPC on the cloud. My experience spanning both makes me think that chasing time on the fastest supercomputers is not the best way for scientists to be productive.