71–80 of 172 posts
It kind of shows the difference in priority spending, when nuclear labs get >1000 petaflop super computers, and the weather service (that helps with disasters that affect many Americans each year) gets a new one that is 1.2% of the speed.
https://www.noaa.gov/media-release/us-to-triple-operational-....
Since they are using AMD's accelerators as well [1], I do wonder whether any usage of these will trickle down and give us improvements in ROCm. Surely the people at these labs will want to run ordinary DL frameworks at some point - or do they have the money and time to always build entirely custom stacks? [1] AMD Instinct MI250x in this case.
[1]: https://github.com/ECP-CANDLE/Benchmarks
[2]: https://www.exascaleproject.org/research-project/candle/
Earlier quoted context omitted.
> Surely the people at these labs will want to run ordinary DL frameworks at some point I don't know about that. A lot of these labs are doing physics simulations and are probably happy to stick with their dense-matrix multiply / BLAS routines. Deep learning is a newer thing. These national labs can run them of course, but these national labs have existed for many decades and have plenty of work to do without deep le…
> Given all the talk about OpenMP compatibility and Fortran... my guess is that they're largely running legacy code in Fortran. The must used linear algebra library is written in Fortran. There's nothing "legacy" about it, it's just that nobody was able to replicate its speed in C.
Like, it's always seemed like there's a certain amount of fatalism around Undefined Behavior in C/C++, like this is somehow how it has to be to write fast code but... it's not. You can just declare things as actually forbidden rather than just letting the compiler identify a boo-boo and silently do whatever the hell it wants.
Of course it's not the right tool for every task, I don't think you'd write bit-twiddling microcontroller stuff in fortran, or systems programming. But for the HPC space, and other "scientific" code? Fortran is a good match and very popular despite having an ancient legacy even by C/C++ standards (both have, of course, been updated through time). Little less flexible/general, but that allows less-skilled programmers (scientists are not good programmers) to write fast code without arcane knowledge of the gotchas of C/C++ compiler magic.
I wonder if having one supercomputer with x number of chips or having eight supercomputers each with x/8 number of chips would be the more practical working setup. Weather forecasting for example is basically a complex probabilistic algorithm, and there's a notion that running eight models in parallel and then comparing and contrasting the results will give better estimates of actual outcomes than running one model o…
You can run many programs on one supercomputer simultaneously, yes. Check out XSEDE. Cost-wise one big is going to be cheaper than 8 small due to infrastructure issues - cooling, maintenance, space, etc.
[1] - https://www.hpcwire.com/off-the-wire/nsf-announces-upcoming-...
Since they are using AMD's accelerators as well [1], I do wonder whether any usage of these will trickle down and give us improvements in ROCm. Surely the people at these labs will want to run ordinary DL frameworks at some point - or do they have the money and time to always build entirely custom stacks? [1] AMD Instinct MI250x in this case.
I’m not using Frontier, but I am using Setonix which is a large AMD cluster being rolled out in Australia. All of AMD’s teaching materials are about ROCm so this is very much how they’re expecting it to be used. The real pain for us is that there’s no decent consumer grade chips with ROCm compatibility for us to do development on. AMD have made it very clear they only care about the data centre hardware when it comes…
(I work for a DOE lab but views are my own, etc.)
[1] As an example, see the approach in: https://github.com/flatironinstitute/cufinufft/pull/116
What blows my mind is the newest NOAA super computer (that triples the speed of the last one) is a whopping 12 petaflops. It comes online this summer. It kind of shows the difference in priority spending, when nuclear labs get >1000 petaflop super computers, and the weather service (that helps with disasters that affect many Americans each year) gets a new one that is 1.2% of the speed. https://www.noaa.gov/media-rel…
Research spending is based on the potential for discovery. As a species we have studied weather since the beginning of time. How long have we been doing nuclear research? A century?
Is there even an opportunity cost here? Or is it an economy of scale? As we build more supercomputers the costs go down. So NOAA and ORNL both get what they need for less.
What blows my mind is the newest NOAA super computer (that triples the speed of the last one) is a whopping 12 petaflops. It comes online this summer. It kind of shows the difference in priority spending, when nuclear labs get >1000 petaflop super computers, and the weather service (that helps with disasters that affect many Americans each year) gets a new one that is 1.2% of the speed. https://www.noaa.gov/media-rel…
Earlier quoted context omitted.
> Surely the people at these labs will want to run ordinary DL frameworks at some point I don't know about that. A lot of these labs are doing physics simulations and are probably happy to stick with their dense-matrix multiply / BLAS routines. Deep learning is a newer thing. These national labs can run them of course, but these national labs have existed for many decades and have plenty of work to do without deep le…
> Given all the talk about OpenMP compatibility and Fortran... my guess is that they're largely running legacy code in Fortran. The must used linear algebra library is written in Fortran. There's nothing "legacy" about it, it's just that nobody was able to replicate its speed in C.
The best BLAS libraries use C and Assembly. This is because BLAS is the de-facto standard interface for Linear Algebra code, and so it is worthwhile to optimize it to an extreme degree (given infinite programmer-hours, C can beat any language, because you can embed assembly in C).
But for those numerical codes which aren't incredibly hand-optimized, Fortran makes nice assumptions, it should be able to optimize the output of a moderately skilled programmer pretty well (hey we aren't all experts, right?).
What blows my mind is the newest NOAA super computer (that triples the speed of the last one) is a whopping 12 petaflops. It comes online this summer. It kind of shows the difference in priority spending, when nuclear labs get >1000 petaflop super computers, and the weather service (that helps with disasters that affect many Americans each year) gets a new one that is 1.2% of the speed. https://www.noaa.gov/media-rel…
What blows my mind is the newest NOAA super computer (that triples the speed of the last one) is a whopping 12 petaflops. It comes online this summer. It kind of shows the difference in priority spending, when nuclear labs get >1000 petaflop super computers, and the weather service (that helps with disasters that affect many Americans each year) gets a new one that is 1.2% of the speed. https://www.noaa.gov/media-rel…
Would a faster computer improve outcomes for victims of natural disaster? How much is left undiscovered about weather? Research spending is based on the potential for discovery. As a species we have studied weather since the beginning of time. How long have we been doing nuclear research? A century? Is there even an opportunity cost here? Or is it an economy of scale? As we build more supercomputers the costs go down…
The US is way behind on weather modelling, in part due to lack of computing power available to do the grids at sufficiently small cells compared to Europe and other parts of the world. That means less accurate predictions and less advance notice of impending disasters, which means more risk of loss of life and impact on infrastructure and the economy (and vice versa, inaccuracy can lead to more caution than is necessary, which has economic impact too). The US has to lean on Europe etc. for predictions.
https://cliffmass.blogspot.com/2020/02/smartphone-weather-ap...
Talks about the fact that IBM / Weather.com actually uses a more accurate system than the NWS uses, because the NWS is still stuck on GFS (been several years now since congress passed an act to force NOAA to update away from it, and unfortunately it takes time)