Whereas the
Top500 record ranks the five hundred greatest high-performance computer systems (HPCs) on the planet, its cousin the Green500 re-ranks the identical 500 supercomputers in response to their power effectivity. For the final three iterations of the record, Henri—a small supercomputer operated by the Flatiron Institute in New York—has been named the world’s most power environment friendly high-performance laptop. Constructed within the fall of 2022, Henri was the primary system to make use of Nvidia’s H100 GPU’s, aka Hopper.
To study the secrets and techniques of constructing and sustaining probably the most energy-efficient supercomputer, we caught up with Henri’s architect,
Ian Fisk, who’s co-director of the Scientific Computing Core on the Flatiron Institute. Flatiron is an inside analysis division of the Simons Basis that brings collectively researchers utilizing trendy computational instruments to advance our understanding of science.
The Flatiron Insitute’s Ian Fisk on…
IEEE Spectrum: The place did the identify Henri come from?
Ian Fisk: The identify happened for a foolish cause. Our earlier machine was referred to as Rusty. So, when requested by the seller what the machine identify was going to be, we stated, ‘properly, by our naming conference, it’ll be Rusty, and it’s utilizing [Nvidia’s] H100 chip, so it’d be Rusty Hopper.’ However Rusty Hopper seems like a rustic singer from the Nineteen Eighties, so that they didn’t wish to name it that. And one of many Nvidia engineers who determined that you simply may have the ability to truly construct a machine that will make the top500 and be the highest of the Green500 had simply had a son named Henri. So, we had been requested by the seller if we’d take into account naming it after that individual, which we thought was candy.
Because the Green500 measures efficiency per watt, it doesn’t matter how briskly you’re, it issues how briskly you’re for what number of watts you used. —Ian Fisk, Flatiron Institute
Did you got down to construct the world’s greenest supercomputer?
Fisk: Nvidia offered us that gear at an academic low cost value partly as a result of we had been aiming for this benchmark. It was good for us as a result of it gave us some publicity, however we actually wished the {hardware} for the scientists, and it was a approach for us to get entry to H100s very early. However to try this, we needed to do the take a look at in November 2022. So the tools got here to the loading dock in October, and it was assembled into a pc after which examined in report time. If there was an award for the quick 500, we’d even be the winner.
The numbers within the first take a look at run [November 2022] had been not so good as the second time [June 2023]. The second time when there was a little bit bit extra time to breathe, we upgraded the machine. It was larger: it was 80 GPUs the primary time and 144 the second time. It’s 2.7 petaflops, which for 2 racks of kit is an inexpensive measurement. It’s round 250 on the highest 500 largest supercomputers record. After which primary on the inexperienced 500 record.
Are you able to clarify your design choices when constructing Henri? Why Nvidia’s H100s?
Fisk: Our expertise with Nvidia, which works all the best way again to
K40s, was that each technology was about two to a few instances sooner than its predecessor. And that was definitely true of all of the issues that led as much as it, just like the V100 and the A100. It’s about two and a half instances higher. We already had two racks of A100s, and when it got here time to improve the power, H100s had been the factor to purchase.
The H100 on the time had been solely obtainable within the PCI linked model, they didn’t have the NV-link possibility but. And so they didn’t have any water-cooled ones, so we had been utilizing air cooled methods once more. The GPUs earlier than that machine and after have all been water cooled methods, as a result of they’re just a bit bit extra environment friendly, and simpler to function as a result of you’ll be able to do away with much more warmth. However we selected it as a result of we had been anticipating very good efficiency numbers. And we obtained them, finally. With Nvidia, the software program and the {hardware} form of come out on the similar time. And the efficiency tends to get higher over time as issues get optimized correctly.
The factor that separates a pc from a supercomputer is the low-latency cloth. And on virtually all methods proper now, that low-latency cloth is InfiniBand. The one individuals who present it’s Mellanox [Technologies], which was not too long ago acquired by the NVIDIA Company, so that they personal the entire stack.
[What] has allowed us to face on high has been that know-how has developed to make use of extra energy relatively than be extra environment friendly. We didn’t count on to win greater than as soon as. —Ian Fisk, Flatiron Institute
There was one design selection that was form of thrust upon us that we’re revisiting proper now. After we purchased the system, the one chassis that you might purchase had been PCI Gen 4, and the H100s use PCI Gen 5. As a result of it was Gen 4, we had been restricted by the communication pace to the GPUs and to the InfiniBand playing cards. After we began, we had HDR playing cards at 100 gigabits every. And we quickly found that that wasn’t going to be ample to do a superb take a look at for the Green500. So, we upgraded to 400 gigabits of InfiniBand on every node, and that helped some. Have we had PCIe Gen 5, we may have had two instances 400 gigabits, and that will have been even higher.
What optimizations did you need to do for the Green500 take a look at?
Fisk: I feel doing the Green500 run is a little bit bit like being a hypermiler. You’ve a Honda Civic and also you drive throughout the nation getting sixty miles per gallon with the home windows closed AC off, and accelerating very slowly, however that’s not precisely the best way you’d drive it in a rush to get someplace. As an illustration, whenever you do the Green500 run, the whole lot that doesn’t generate efficiency is turned down. There are huge solid-state drives on all the methods of this kind whenever you’re working in manufacturing, as a result of it is advisable serve coaching samples to machine studying functions. However they use energy, they usually don’t offer you any efficiency, so these get turned off. It’s a little bit bit like a hypermiler taking the spare tire out of their automotive as a result of they wished to get higher mileage, but it surely’s not how they’d truly drive it on a regular basis.
How have you ever been capable of maintain the number one spot for nearly two years?
Fisk: Actually, the factor that can knock Henri off its perch would be the subsequent technology of {hardware}. However I feel the factor that has allowed us to face on high has been that know-how has developed to make use of extra energy relatively than be extra environment friendly. We didn’t count on to win greater than as soon as, we had been anticipating that folks would come together with the water-cooled model of H100’s and be extra environment friendly than us, however that hasn’t occurred to this point.
The H100 is available in two fashions, the PCI model that plugs into the board as a card and the motherboard mount, it’s referred to as an SXM5. And the SXM5 is the NV-linked model. The large distinction is that there the SXM5 has a communication protocol between the GPUs that enables them to speak to one another at 900 gigabytes per second. It’s dramatically higher than something on InfiniBand. It’s actually what permits them to resolve issues like giant language fashions, as a result of whenever you’re doing these sorts of calculations, at every epoch, there is usually a super quantity of knowledge that has to circulation forwards and backwards. So these communication hyperlinks are crucial, however in addition they use extra electrical energy. The LINPACK benchmark that they do for the green500 take a look at advantages from a superb communication layer, however not at that degree.
The explanation why nobody has overwhelmed the machine but is that the SXM5s truly use much more electrical energy, they use 700 watts per GPU whereas ours solely use 350, and the efficiency [on things like the LINPACK test] is just not an element of two completely different. Because the green500 measures efficiency per watt, it doesn’t matter how briskly you’re, it issues how briskly you’re for what number of watts you used. And that’s the factor that we see with these PCI linked H100s, they’re very onerous to beat as a result of they don’t use a variety of electrical energy they usually have comparable efficiency to the a lot greater wattage stuff on these sorts of calculations.
Do you count on to be the greenest supercomputer once more in Might?
Fisk: Effectively, we’re constructing a brand new machine with 96 GPUs, these would be the SXM5s, water-cooled NV-linked units. We are going to know quickly if they’ll have higher efficiency. As I discussed, they could be sooner, however they is probably not extra environment friendly. However, one factor we discovered with our A100s was that many of the efficiency is offered within the first half the wattage, so that you get 90 % of the efficiency within the first 225 Watts. So, one of many issues that we’re going to strive with the water-cooled system is to run it in energy capped mode, and see what sort of efficiency we get.
The longer term goes to be costly. And the longer term goes to be very excessive powered. —Ian Fisk, Flatiron Institute
One good factor in regards to the water-cooled model is that it doesn’t want followers, as a result of the followers rely towards your wattage. When these items are working, it’s about 4 kilowatts of energy per three items of house (3U). So it’s like forty 100 watt gentle bulbs in a small field. Cooling that down requires blowing an amazing quantity of air throughout it, so you’ll be able to have a number of 100 watts of followers. And with water cooling, you simply have a central pump, which implies vital financial savings. The warmth capability of water is about 4000 instances the warmth capability of air by quantity, so you need to use quite a bit much less of it.
It’s going to be fascinating to see the subsequent Green500 record in Might of this yr. We’ll see who comes alongside and whether or not no person beats us, or any individual beats us, or we beat ourselves. It’s all doable.
Fisk: The longer term goes to be costly. And the longer term goes to be very excessive powered.
After we began, the GPU was a specialised useful resource that was excellent for machine studying and sure sorts of linear algebra calculations. Firstly, everybody used a single GPU. Then they began utilizing them collectively in teams the place they’d match their computation throughout a number of nodes, as much as eight nodes. Now, we’re seeing increasingly more individuals who wish to do tightly linked giant language fashions, the place it requires 100 GPUs or a number of 100s of GPUs linked in ways in which we by no means would have imagined.
For the subsequent set of assets we’re shopping for, the community connectivity is 16 instances higher than those that got here earlier than that. It’s an analogous set of kit, however these ones have 1.6 terabit of communication per node, as in comparison with 100 gigabits. And it makes the machines very costly, as a result of all of the sudden the community cloth is a big issue within the buy value, since you want tons and many InfiniBand switches and many cables. And these are 800 gigabit—unique, very excessive efficiency cables.
With tightly linked GPUs you may get fashions which have 10 to the facility of 10 parameters. And that is what’s actually driving that exact know-how. —Ian Fisk, Flatiron Institute
We count on there’ll be a number of people who find themselves working standard high-performance computing codes. However now there’s this new neighborhood that desires to make use of huge chunks of very useful assets, and we’re making an attempt to assist these individuals. It’s difficult, partly as a result of we’re competing with industries that do that, too. These sorts of assets are very onerous to purchase, they’ve lengthy lead instances, they’re very costly, partly as a result of it’s pushed by the AI gold rush that is happening proper now. We’re making an attempt to determine our place in that, and so we’re shopping for a medium scale machine. And we don’t know what occurs after that.
Fisk: It’s a combination. I’d say, nonetheless 75 or 80 % is what I’d take into account canned machine studying functions. That is PyTorch primarily, the place persons are constructing fashions to make both simulation or prediction of varied issues, discovering correlations. This runs throughout the entire spectrum. We’ve obtained people who find themselves taking a look at how you can perceive the AI and construct higher fashions. We even have people who find themselves engaged on issues like structural methods biology, on the lookout for correlations of microbiome within the intestine. We now have individuals engaged on protein construction, gene perform, taking a look at gene sequences, and utilizing machine studying methods to establish what’s happening.
The newest undertaking is named
Polymathic AI. A simplistic abstract can be one thing like ChatGPT for science. The concept is to make a big sufficient basis mannequin for science, the place you educate the AI algorithms quite a bit about bodily processes, after which ask them to do issues like fluid dynamics simulations. It’s a really bold undertaking. And so they’re making an attempt to determine how you can get larger, how you can scale up their work. And the thought behind that is that with tightly linked GPUs you may get fashions which have 10 to the facility of 10 parameters. And that is what’s actually driving that exact know-how.
Henri is a workhorse machine. For those who go into the queue proper now, it’s fully full. If I wished to run one other inexperienced 500 take a look at and say: ‘I’m going to take this factor offline for 2 weeks,’ I’d have riot on my palms. There can be pitchforks exterior my workplace. So sure, it’s a really inexperienced environment friendly laptop. However on the finish of the day, its legacy is all the superb science it permits.
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