71–80 of 112 posts
How bad is this for the environment?
https://sustainable.ufl.edu/campus-initiatives/neutral-uf-co...
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Not sure why you're downvoted, HPC systems don't last more than ~6 years before they are decommissioned because they've become too power-hungry to justify their continued use. I just checked some of the systems I'm familiar with and they were all decommissioned after around 6 years of use (e.g. SuperMUC, 2012-2018; two smaller cluster at the university where I work: 08/2012-04/2017, 01/2014-03/2020). I also attended…
Out of curiosity, do you know what happens to the decomissioned hardware? is it scrapped for useful parts like perhaps the PSUs? or is gold and other metals extracted from the chips and the metal sold for recycling? Is there anything useful someone could do with it or it's just too much of a problem to set it up and repurpose it?
I cannot state with 100% clarity what happens after the systems are acquired by surplus vendors, but I can say that we have received certificates of what was "recycled". Once the surplus vendors take possession of the hardware, it's theirs and they can do what they want with it. In fact, we recently had to purchase EOL'ed, refurbished Infiniband switches to continue to support an interconnect fabric still in use (~2016). Interestingly enough, some of the switches still had "core" and "edge" labels on them.
> Is there anything useful someone could do with it or it's just too much of a problem to set it up and repurpose it?
In my opinion, it really depends on the node. If the chassis supports hot swappable & redundant hardware (HDD, PSU, etc.), then we'll typically cannibalize several chassis to create an administrative and/or infrastructure node. Case in point, a large portion of our older 12 core nodes have been put aside to serve as administrative nodes, all fully redundant (RAID1 HDD's, dual PSU's, ECC memory, etc.). Since we have a stack of these, we're fairly confident that these will serve us for the next few years, worry free, given the abundance of parts lying around.
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There is a trend towards larger models in the state of the art of deep learning research. The total cost of the on-chip memory on the NVidia GPU is still a good value proposition compared to current custom deep learning accelerators. This cost to get to large on-chip memory, combined with the flexibility of CUDA for other types of scientific applications, makes it harder for such accelerators to compete with Nvidia i…
Is this really true? At least for inference I've seen data showing the trend is rather for smaller models that exploit sparsity.
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There is a trend towards larger models in the state of the art of deep learning research. The total cost of the on-chip memory on the NVidia GPU is still a good value proposition compared to current custom deep learning accelerators. This cost to get to large on-chip memory, combined with the flexibility of CUDA for other types of scientific applications, makes it harder for such accelerators to compete with Nvidia i…
Is this really true? At least for inference I've seen data showing the trend is rather for smaller models that exploit sparsity.
Does anyone know why everyone is still buying Nvidia instead of custom AI accelerators from other vendors? For example, on paper the new Graphcore machines look like an easy win, or at least a risk worth taking. (I see this particular supercomputer was funded by Nvidia but my question is about the general trend).
Graphcore looks great! But no one outside Graphcore has used them so who knows. Intel's Nervana looked great on paper, right up until they dumped it.
There are some interesting accelerator options around for inference. But for training it's NVidia for almost everything, and TPUs as a good option is a few cases. But you can't buy TPUs (except for the inference-only Coral board), and universities like to own hardware.
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It's $50 million. That's all. See https://blogs.nvidia.com/blog/2020/07/21/university-of-flori... for the numbers. It's probable the hardware is being offered at a deep discount. The building and infrastructure will cost an additional $20m, covered by the University.
https://lambdalabs.com/blog/demystifying-gpt-3/ According to ^, that's about 11 GPT-3s trainings worth in the cloud.
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In addition to what everyone else has said, UF has over 50,000 students, making it one of the five largest universities in the USA, iirc.
Correct. Florida has 3 of the top 10 by enrollment. UCF, UF, FIU, and USF. Georgia made the list, go dawgs.
The first time I walked on the campus, it blew my mind. I'd never heard about Florida International University as an American, but it's easily the biggest and most beautiful I've seen.
All these big companies giving free stuff to university students to hook them into their proprietary technology. I remember some of my friends graduating and realizing marlin wasn’t free.