> Liquid cooling is a familiar concept to PC enthusiasts, and has a long history in enterprise compute as well.
And the trend in data centers was to move towards more passive cooling at the individual servers and hotter operating temperatures for a while. This is interesting because it reverses that trend a lot, and possibly because of the per-row cooling.
Those style of jobs worked well but as Google has realized it has more high performance computing with unique workload characteristics that are mission-critical (https://cloud.google.com/blog/topics/systems/the-fifth-epoch...) their infrastructure has had to undergo a lot of evolution to adapt to that.
Google PR has always been full of "look we discovered something important and new and everybody should do it", often for things that were effectively solved using that approach a long time ago. MapReduce is a great example of that- Google certainly didn't invent the concepts of Map or Reduce, or even the idea of using those for doing high throughput computing (and the shuffle phase of MapReduce is more "interesting" from a high performance computing perspective than mapping or reducing anyway).
Liquid cooling at Google scale is different than mainframes as well. Mainframes needed to move heat from the core out to the edges of the server where traditional data center cooling would transfer it away to be conditioned. Google liquid cooling is moving the heat completely outside of the building while it’s still liquid. That’s never been done before as far as I am aware. Not at this scale at least.
We have been doing this for decades, it's how refrigerants work.
The part that is new is not having an air-interface in the middle of the cycle.
Water isn't the only material being looked at, mostly because high pressure PtC (Push to Connect) fittings, and monitoring/sensor hardware has evolved. If a coolant is more expensive but leaks don't destroy equipment, and can be quickly isolated then it becomes a cost/accounting question.
I wasn’t clear when I was writing but this was the point I was trying to make. Heat from the chip is transferred in the same medium all the way from the chip to the exterior chiller without intermediate transfers to a new medium.
IMO, it's not a big difference. There are probably many details more noteworthy than this. And yeah, mainframes are that way because the vendor only creates them up to the hack-level, while Google has the "vendor" design the entire datacenter. Supercomputers have had single-vendor datacenters for decades too, and have been using large pipes for a while too.
The next step is probably evaporative cooling, with liquid coolant ("freon") pumped to individual racks.
There's also all the fun experiments with dunking the whole server into oil, but I'll give you that again I've only seen setups described with secondary cooling loops - probably because of corrosion and wanting to avoid contaminants.
I do think Google must be doing something right, as their quoted PUE numbers are very strong, but nothing about what's in the linked chipsandcheese article seems groundbreaking at all architecturally, just strong micro-optimization. The article talks a lot about good plate/thermal interface design, good water flow management, use of active flow control valves, and a ton of iteration at scale to find the optimal CDU-to-hardware ratio, but at the end of the day it's the same exact thing in the diagram from 1965.
[I am still annoyed at how many people are dismissive of Google’s datacenter work simply because “severs have been water cooled before” which completely misses the point of datacenter level cooling. I also learned that AWS is doing this already, along with some elements of OVH] =)
Unless Google has discovered a way to directly transfer heat to the aethereal plane, nothing they’re doing is new. Mainframes were moving chip and module heat entirely outside the building decades ago. Immersion cooling? Chip, module, board, rack, line, and facility-level work? Rear-door and hybrid strategies? Integrated thermal management sensors and controls? Done. Done. Done. Done. Richard Chu, Roger Schmidt, and company were executing all these strategies at scale long before Google even existed.
Dean, Ghemawat, and Google at large deserve credit not for inventing map and reduce—those were already canonical in programming languages and parallel algorithm theory—but for reframing them in the early 2000s against the reality of extraordinarily large, scale-out distributed networks.
Earlier takes on these primitives had been about generalizing symbolic computation or squeezing algorithms into environments of extreme resource scarcity. The 2004 MapReduce paper was also about scarcity—but scarcity redefined, at the scale of global workloads and thousands of commodity machines. That reframing was the true innovation.
Google isn't claiming to have invented water cooling. This article recaps their talk at Hot Chips where they showed some details of their implementation.
Data center cooling is also a different beast than supercomputer cooling. Datacenter cooling operates at a larger scale and has different needs for maintenance operations like maintenance cycles.
There are also some interesting notes in there about new things Google is doing, like the direct-die cooling.
Water cooling is a big field. Data center operations is a big field. There is interesting content in the article.
The “Map” in MapReduce does not originally stand for the map operation, it comes from the concept of “a map” (or, I guess, a multimap). MapReduce descends from “the ripper”, an older system that mostly did per-element processing, but wasn't very robust or flexible. I believe the map operation was called “Filter()” at the time, and reduce also was called something else. Eventually things were cleaned up and renamed into Map() and Reduce() (and much more complexity was added, such as combiners), in a sort of backnaming.
It may be tangential, but it's not like the MapReduce authors started with “aha, we can use functional programming here”; it's more like the concept fell out. The fundamental contribution of MapReduce is not to invent lambda calculus, but to show that with enough violence (and you should know there was a lot of violence in there!), you can actually make a robust distributed system that appears simple to the users.
and with the internal usage of the program (I only started in 2008, but spoke to Jeff extensively about the history of MR as part of Google's early infra) where the map function can be fed with recordio (list containers) or sstable (map containers).
As for the ripper, if you have any links to that (rather than internal google lore), I'd love to hear about it. Jeff described the early infrastructure as being very brittle.
I worked on the MapReduce team for a while (coincidentally, around 2008), together with Marián Dvorský, who wrote up a great little history of this. I don't think it was ever made public, though.
> As for the ripper, if you have any links to that (rather than internal google lore), I'd love to hear about it. Jeff described the early infrastructure as being very brittle.
I believe it's all internal, unfortunately.
Building out an idea for a bespoke supercomputer and making an iteration of that idea that applies to globally scaled workloads is a different thing. They are building computing factories, as is Amazon, MS, Facebook, etc.
That said, PR is PR, and companies always crow about something for their own reasons.
Google wants you to know it recycles its water. It's free points.
Edit: to clarify, normal social media is being flooded with stories about AI energy and water usage. Google isn't greenwashing, they're simply showing how things work and getting good press for something they already do.
Last year, U.S. data centers consumed 17 billion gallons of water. Which sounds like a lot, but the US as a whole uses 300 billion gallons of water every day. Water is not a scarce resource in much of the country.
The correct metric is something like, what's the probability that the launch of data center in a location results in nearby communities to drop significantly in these water metrics.
It's mostly a play for density.