Google's Liquid Cooling

It’s very odd when mainframes (S/3x0, Cray, yadda yadda) have been extensively water-cooled for over 50 years, and super-dense HPC data centers have used liquid cooling for at least 20, to hear Google-scale data center design compared to PC hobbyist rigs. Selective amnesia + laughably off-target point of comparison.

From the article:

> 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.

We've basically been watching Google gradually re-discover all the tricks of supercomputing (and other high performance areas) over the past 10+ years. For a long time, websearch and ads were the two main drivers of Google's datacenter architecture, along with services like storage and jobs like mapreduce. I would describe the approach as "horizontal scaling with statistical multiplexing for load balancing".

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).

As to MapReduce, I think you're fundamentally mistaken. You can talk about map and reduce in the lambda calculus sense of the term, but in terms of high performance distributed calculations, MapReduce was definitely invented at Google (by Jeff Dean and Sanjay Ghemawat in 2004).

CERN was doing the equivalent of MapReduce before Google existed.