5 ms·
Will supercapacitors come to AI's rescue?
- paulkrush 1y ago"Thousands of GPUs all linked together turning on and off at the same time." So supercapacitors allow for simpler software?, reduced latency? at a low cost?
- mjevans 1y agoThey service 'spot demand moderation' as an extension of UPS and power smoothing. In this case it's flattening out spikes to smooth slopes.
- Animats 1y agoIs that kind of load variation from large data centers really a problem to the power grid? There are much worse intermittent loads, such as an electric furnace or a rolling mill.
- paulkrush 1y agoEdit: It's interesting the GPU's are causing issues on the grid before they cause issues with the data center's power.
- mystified5016 1y agoRead the article.
- toast0 1y agoI suspect it's more of a problem for the data center's energy bill. My understanding is that large electric customers pay a demand charge in addition to the volumetric charge for the kWh's use at whatever rates given time of use / wholesale rates. The demand charge is based on the maximum kW used (or sometimes just the connection size) and may also have a penalty rate if the power factor is poor. Smoothing over small duration surges probably makes a lot of things nicer for the rate payer, including helping manage fluctuations from the utility. There's probably something that could be done on the individual systems so that they don't modulate power use quite so fast, too; at some latency cost, of course. If you go all the way to the extremes, you might add a zero crossing detector and use it to time clock speed increases.
- deleted 1y ago[deleted]
- timewizard 1y agoIf you have a working thermometer you can predict when furnaces are going to run. If you want to smooth out data centers then you need hourly pricing to force them to manage their demand into periods where excess grid capacity is not being used to serve residential loads.
- hinkley 1y agoLarge customers pay not by wattage but by… I’m spacing on the word but essentially how much their power draw fucks up the sine waves for voltage and current in the power grid. I imagine common power rail systems in hyperscaler equipment helps a bit with this, but for sure switching PSUS chop up the input voltage and smooth it out. And that leads to very strange power draws.
- murderfs 1y agoYou're probably thinking of power factor, which is usually not a big deal for datacenters. All of your power supplies are going to have active PFC, and anything behind a double conversion UPS is going to get PFC from the UPS. The biggest contributors are probably going to be the fans in the air conditioning units.
- Animats 1y agoThis isn't about power factor. That's a current vs. voltage thing within one cycle. It's about demand side ramp rate - how fast load can go up and down. Ramp rate has been a generation side thing for a century. Every morning, load increases from the pre-dawn low, and which generators can ramp up output at what speed matters. Ramp rate is usually measured in megawatts/minute. Big thermal plants, atomic and coal, have the lowest ramp rates, a few percent per minute. Ramp rate demand side, though, is a new thing. There are discussions about it [1] but it's not currently something that's a parameter in electric energy bills. [1] https://www.aceee.org/files/proceedings/2012/data/papers/0193-000178.pdf https://www.aceee.org/files/proceedings/2012/data/papers/019...
- changoplatanero 1y agoYes its a problem for the grid and the power companies don't allow large clusters to oscillate their power like this. The solution that AI have to do during their training big runs is to fill in the idle time on the GPUs with dummy operations to keep the power load constant. Having capacitors would be able to save on power usage.
- nancyminusone 1y agoInb4 a startup is created to sell power load idle cycle compute time in AI training data centers.
- mystified5016 1y agoThose loads aren't nearly as intermittent. Your furnace likely runs for tens of minutes at a time. These datacenters are looking at second-to-second loads. Drawing high intermittent loads at high frequency likely makes the utility upset and leads to over-building supply to the customer to cope with peak load. If you can shave down those peaks, you can use a smaller(cheaper) supply connection. A smoother load will also make the utility happy. Remember that electricity generation cannot ramp up and down quickly. Big transient loads can cause a lot of problems through the whole network.
- sonium 1y agoOr you simply use the pytorch.powerplant_no_blow_up operator [1] [1] https://www.youtube.com/watch?v=vXsT6lBf0X4 https://www.youtube.com/watch?v=vXsT6lBf0X4
- deleted 1y ago[deleted]
- janalsncm 1y agoPretty much. From the article: > Another solution is dummy calculations, which run while there are no spikes, to smooth out demand.
- metaphor 1y agoParaphrasing this[1]? [1] https://github.com/pytorch/pytorch/pull/132936/files#diff-98e548dc46290c54900149a6fcf818fe33f775196ace51405978a709976c39e9R41 https://github.com/pytorch/pytorch/pull/132936/files#diff-98...
- 0cf8612b2e1e 1y agoOne solution is to rely on backup power supplies and batteries to charge and discharge, providing extra power quickly. However, much like a phone battery degrades after multiple recharge cycles, lithium-ion batteries degrade quickly when charging and discharging at this high rate. Is this really a problem for an industrial installation? I would imagine that a properly sized facility would have adequate cooling + capacity to only run the batteries within optimal spec. Solar plants are already charging/discharging their batteries daily.
- jeffbee 1y agoIn addition to what you said, nothing is forcing or even encouraging anyone to use lithium-ion batteries in fixed service, such as a rack full of computers.
- pixl97 1y agoEh, I think part of the problem here is the speed of load switching. From the article it looks like the loads could generate dozens to hundreds of demand spikes per minute. With most battery operated loads that I've ever messed with we're not switching loads like that. It's typically 'oh a fault, switch to battery' then some time later you check the power circuit to see if it's up and switch back. This looks a whole lot more like high frequency load smoothing. Really it seems to me like a continuation of a motherboard. Even if you have a battery backup on your PC you still have capacitors on the board for voltage fluctuations.
- lstodd 1y agoin a properly designed install you can actually use the compressors and fans for smoothing load spikes. won't be much, but why not. edit: otherwise I'm not getting what the entire article is about. it's as contrary to what I know about datacenter design as it can get. it's.. just wrong.
- touisteur 1y agoI'm thinking of sequences of 'put the sharded dataset through the ten thousand 2kW GPUs, then wait on network - all-reduce - then spike again - a mostly-synchronous all-on/all-off loop. Watching how quick they get to boost-frequency I can see where the worries come from.
- lawlessone 1y ago[flagged]
- eikenberry 1y agohttps://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
- amelius 1y agoMaybe a superconducting superinductor would be a better fit.
- lstodd 1y agothat would be a blackhole bomb.
- janalsncm 1y agoI am curious about what the load curves look like in these clusters. If the “networking gap” is long enough you might just be able to have a secondary workload that trains intermittently. Slightly related, you can actually hear this effect depending on your GPU. It’s called coil whine. When your GPU is doing calculations, it draws more power and whines. Depending on your training setup, you can hear when it’s working. In other words, you want it whining all the time.
- touisteur 1y agoYou might need more memory for this secondary training workload. But yeah, donating/selling the 'network' time for high-intensity, low memory footprint workloads (thinking number crunching, monte-carlo stuff, maybe brute-force through a series of problems...) might end up making sense.
- hulitu 1y ago> Will Supercapacitors Come to AI's Rescue? Yes, just like the octopussies. /s
- blt 1y agoWhat is causing demand bursts in AI workloads? I would have expected that AI training is almost the exact opposite. Load a minibatch, take a gradient step, repeat forever. But the article claims that "each step of the computation corresponds to a massive energy spike."
- wmf 1y agoIf the cores go idle (or just much less loaded) in between steps because they're waiting for network communication that would cause the problem.
- sdenton4 1y agoBad input pipelines are a big cause of spikiness - you might have to wait a non-trivial fraction of a second for the next batch of inputs to arrive. If you can run 20+ training steps per second on. adecent batch size, it can take some real engineering to get enough data lined up and ready to go fast enough. (I work on audio models, where data is apparently quite heavy compared to images or text...)
- lstodd 1y agoif that is the case, my, I am appaled. where do you get those ntfractions of seconds? network? storage?
- sdenton4 1y agoIt's not soooo appalling once you dig further into what's happening. There's been a tug-of-war between compute bottlenecks and memory bottlenecks in accelerators for quite a while. Big increases in FLOPs requires faster memory access, so you tend to get hardware releases alternating between emphasizing compute gains and bus bandwidth gains. Data I/O is just the next layer out... once your base training is running fast enough, it becomes progressively harder to keep the model fed with data.
- deleted 1y ago[deleted]
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- tzs 1y ago> Another solution is dummy calculations, which run while there are no spikes, to smooth out demand. This makes the grid see a consistent load, but it also wastes energy doing unnecessary work. Oh god...I can see it now. Someone will try to capitalize on the hype of LLMs and the hype of cryptocurrency and try to build a combined LLM training and cryptocurrency mining facility that that runs the mining between training spikes.
- ludicity 1y agoOh man, I really, really wish that you hadn't said this and also that you were wrong.
- ijustlovemath 1y agoYCW27
- candiddevmike 1y agoFrom the same founders who brought you (or didn't, actually) maritime fusion
- FridgeSeal 1y agoIt’s ok though because YC invests in the team, better just give them another chance!!
- x-complexity 1y ago
- Merrill 1y agoWouldn't it be better to arrange the network and software to run the GPUs continuously at optimal usage? Otherwise a lot of expensive GPU capital is idle between bursts of computation. Didn't DeepSeek do something like this to get more system level performance out of less capable GPUs?
- WatchDog 1y agoSounds like an issue that would be cheaper to address by just adjusting the software.
- bcoates 1y agoAre these GPU DCs entirely passive cooled? I'm surprised it's not cheaper to modulate all those compressor motors they presumably already have
- gitroom 1y agolmao the amount of weird fixes folks float for this problem is insane - tbh i feel like half of it really comes down to software folks not wanting to tweak their pipelines
- rini17 1y agoUhm, I was under impression you are contractually obliged not to do that to the grid? As a wholesale customer, not small kettle picker I mean. Or that's just my european bureaucratically minded approach and in the US everyone just rides the grid raw?
- krunck 1y agoI wonder what voltage is used on the caps? The higher the voltage the greater the energy density(assuming the dielectric can handle it): E = (CV^2)/2 where E is the stored energy, C is the capacitance, and V is the applied voltage