6 ms·
In comparison, 18 A100 GPUs would have 5.6 petaflops and 1.4 TB vram, consuming 5.6 kw. The speed of processing and interconnect is orders of magnitude faster
by observationist 3y ago
In comparison, 18 A100 GPUs would have 5.6 petaflops and 1.4 TB vram, consuming 5.6 kw.
The speed of processing and interconnect is orders of magnitude faster for an A100 cluster - 1 8 gpu pod server will cost around $200k, so around $600k more or less beats the supercomputer performance (price I'm searching seems wildly variable, please correct me if I'm wrong.)
- martinpw 3y agoThe supercomputer flops are FP64. The A100 stats you are using are FP16.
- jeffbee 3y agoIt's fine. We will simply run weather forecast in BF16 mode and hallucinate the weather.
- adgjlsfhk1 3y agoweather forecasting is actually moving to reduced precision. none of the input data is known to more than a few digits, and it's a chaotic system so the numerical error is usually dominated by the modeling and spacial discretization error
- dgacmu 3y agoIntroducing our next supercomputer, Peyote.
- CamperBob2 3y agoA black Sharpie marker is even cheaper...
- mk_stjames 3y agoThe Cheyenne numbers are 5.34 petaflops of *FP64*. The 5.6PF you quote for 18 A100's would be in BF16. Not comparable. The A100 can only do 9.746 TFLOPS in FP64. So you would need 548 A100's to match the FP64 performance of the Cheyenne.
- observationist 3y agoThanks, glad you guys caught that - could be generous and allow the tensor core tflops, since you'd more than likely be using a100 pods for something cuda optimized, in which case 19.5 tflops fp64 at peak per GPU, roughly 267 would be needed, or 34 pods, at $6.8 million, with 21.76 TB vram and 81 kw power consumption. Double those for raw fp64.
- latchkey 3y agoAMD MI300x is 163.4 TFLOPS in FP64. 33 of them, which would also have 6,336TB of memory. I'll have way more than that in my next purchase order. It is really fun to build a super computer.
- mk_stjames 3y agoI'm an amateur, but I have code that I think could probably dispatch threads pretty efficiently on the Cheyenne thru it's management system simply because it's all xeons distributed. If I can run it on my personal 80-core cluster, I could get it to run on Cheyenne back then. But hitting the roofline on those AMD GPGPU's? I'd probably get nowhere fucking close. That is the thing that Cheyenne was built for. People doing CFD research with x86 code that was already nicely parallelized via OpenMPI or whathaveyou.
- latchkey 3y agoIt is wild how much compute has grown. I put dual Epyc 9754 into my first box of MI300x. That's 256 cores + 8x MI300x, in a single box. Agreed, it is a great solution for CFD, which is definitely one workload I'd love to host.
- dekhn 3y agoI used to build small clusters and use supercomputers and I can't imagine it's fun to build a super computer. It requires a massive infrastructure and significant employee base, and individual component failures can take down entire jobs. Finding enough jobs to keep the system loaded 24/7 while also keeping the interconnect (which was 15-20% of the total system cost) busy, and finding the folks who can write such jobs, is not easy. Even then, other systems will be constantly nipping at your heels with newer/cheaper/smaller/faster/cooler hardware.