This is exciting news! What's also exciting is that it's not just C++ that can run on this supercomputer; there is also good (currently unofficial) support for programming those GPUs from Julia, via the AMDGPU.jl library (note: I am the author/maintainer of this library). Some of our users have been able to run AMDGPU.jl's testsuite on the Crusher test system (which is an attached testing system with the same hardware configuration as Frontier), as well as their own domain-specific programs that use AMDGPU.jl.

What's nice about programming GPUs in Julia is that you can write code once and execute it on multiple kinds of GPUs, with excellent performance. The KernelAbstractions.jl library makes this possible for compute kernels by acting as a frontend to AMDGPU.jl, CUDA.jl, and soon Metal.jl and oneAPI.jl, allowing a single piece of code to be portable to AMD, NVIDIA, Intel, and Apple GPUs, and also CPUs. Similarly, the GPUArrays.jl library allows the same behavior for idiomatic array operations, and will automatically dispatch calls to BLAS, FFT, RNG, linear solver, and DNN vendor-provided libraries when appropriate.

I'm personally looking forward to helping researchers get their Julia code up and running on Frontier so that we can push scientific computing to the max!

Library link: <https://github.com/JuliaGPU/AMDGPU.jl>

There's a bit of drama in that there are unofficial reports of two systems in China with higher performance [0], the arXiv paper listed below talks about a 40 million core system with around double theoretical performance than Frontier, and there's apparently a second system online with similar performance. I personally suspect that they didn't submit benchmarks to the top500 simply because those don't run well enough in the systems

[0] https://arxiv.org/pdf/2204.07816.pdf

> and relies on gigabit ethernet for data transfer.

This seems suprising to me, I would have expected 10Gb at least, if not something like inifiniband.

> 8,730,112 total cores

This must include the GPUs, otherwise it'd be 136,408 sockets. For a 42U rack with 4P 1U servers (not that that's what's in use, but to give an understandable napkin figure), that'd be 812 racks.

Frontier's own page says 74 "cabinets"/racks, and this is just for the compute (and perhaps switching and/or power? storage is elsewhere). Made up of 9408 nodes, with 4 MI250X gpu accelerators each- those accelerators being dual chip + 8x HBMe2 memory a piece monsters. From Anandtech[1], we can see the liquid-cooled half-width sleds are dual socket, and packed packed packed.

[1] https://www.anandtech.com/show/17074/amds-instinct-mi250x-re...

Back in the 2010 timeframe, there were articles about how an Exascale Supercomputers might be impossible. Would be interesting if someone could go back and assess where those predictions were wrong and where they held, and how the architecture changed to get around those true scaling limits.

I used to be really excited about supercomputers. It's part of why I pursued HPC-related work.

But I think that having no interest in their actual applications has curbed my enthusiasm. I wish I could make a good living in something that interested more.

The spec sheet mentions they're moving from CUDA powering their prior supercomputer to "HIP" for this one. This is the first I've heard of HIP, does anyone have experience with it? My impression was that GPU programming tended to mean CUDA, which isn't cross platform (as opposed to HIP).

https://developer.amd.com/resources/rocm-learning-center/fun....

I remember in early 2000's trying to convince people to use linux and being mocked that it was a "toy" or "not professional enough". While at the time I tried to argue how it was more stable, more secure and better performant than competition and even arguing that it was improving continuously, some people still made fun of me. It is a good thing I've been able, for a long time, to see this: https://www.top500.org/statistics/list/ , chose Category:"Operating system family" and click "Submit".

> "This HPE Cray EX system is the first US system with a peak performance exceeding one ExaFlop/s."

So, it's not actually the first one? And another one already exists outside the US?

21 MW power! Insane.

Interestingly, the second one is 30 MW.

Onward to zettaflops around 2037, assuming an order of magnitude every five years. Thats been pretty much the case for 60 years.

If they truly wanted to solve world problems, they need to allow an AGI company like DeepMind or OpenAI to use it. The people now using it are likely wasting so much money using outdated technologies.

It feels like it's been a long time since supercomputers were interesting. They're just oodles of identical processors connected together like legos. "We can afford more bricks than the next guy" is not exciting. When was the last time we had a "fastest supercomputer" that could do something the second-fastest couldn't also do?

For comparison, 2000 SP Power3 375 MHz in Oak Ridge National Laboratory did the same order of magnitude GFlops as iPhones with A14 chip can do.

TL;DR: Wow! ~9 million cores, 21 megawatts, >2x the performance of #2 but pulling less power (compared to 30MW). #3 is 0.15EFLOPS, but also 3MW.

Speed is just the measure of how fast it does something not a measure of what it's capable of doing. I wouldn't expect to divine more information like "what new things can it do" from that number alone outside "things we didn't have enough compute time for before we do now".

Lego style supercomputers are still very interesting in my eye though. As the technical complexity involved in scaling the raw compute performance has simplified to a "how many do you want" problem the technical complexity in the interconnects has remained interesting and innovative both for connectivity intra and inter node. You won't really see that in the FLOPS number that makes the headlines but the interconnect can be the difference between a type of workload being feasible or not. The main push here is how large you can make certain levels of shared memory access happen at what latencies to run larger jobs instead of just more jobs.

Well fundamentally all super computers are turing machines. So one can do X while Y can not doesn't really make sense in that context.

However the second-fastest (ARM based Fugaku) absolutely wipes the floor with the fastest in certain tasks due to a difference in interconnect topology. Fugaku futhermore has no GPUs unlike many other super computers and instead a CPU with some vector instructions, leading to a different programming model.

If you are more into specialized hardware, Anton3 is amazing.

> They're just oodles of identical processors connected together like legos.

That's the Cloud, not supercomputing. Supercomputing is all about interconnect.

Building the communication fabric it takes to make those oodles of identical processors to exchange and share data quickly so they don't get bogged down in their own communication overhead is a profoundly interesting problem, and by "profoundly interesting" I mean "call Richard Feynman in to help you solve it":

https://longnow.org/essays/richard-feynman-connection-machin...

Besides which, at that level the goal is not to go "look at this cool thing we built", it's more like "how do we cheaply and effectively build something that can solve these massive weather/nuclear explosion/human brain/etc. simulation problems we have?" and if ganging together lots of off-the-shelf CPUs/GPUs achieves that goal with less time, effort, and cost than building super-custom, boutique-schmoutique hardware, so be it.

Not sure about exciting, but I'd think the technical challenges, particularly regarding intra-cluster communication, can be interesting to some. There's a lot of money in it, they better do something useful (more useful then running Linpack or calculating digits of Pi), rather then being just show cases.

Said that, #1 is about twice as fast as #2, which is about three times as fast as number #3. Those gaps are much wider then I would have expected this late in the game.

You can still get the NEC SX series, which is a non-x86, non-arm vector super. They're pretty nifty. "Fastest" has gone in a different direction tho.