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May 20, 2008


Simon Waters

I think it is just the Press looking for big numbers and focusing only on processing speed.

The big supercomputers are often built for specific tasks, such as climate modeling, and similar numerical modeling tasks. For these tasks you often need to exchange lots of data between many processing nodes on a routine basis (one system I worked with needed to do global sums for each time step of the simulation for conservation calculations, as well as routine exchange of information between nodes modeling adjacent bits of fluids). So what matters is the combination of processing performance, and interconnect speed, and memory storage and I/O.

Google have a very different mix of the above, compared to a traditional supercomputers problem domain.

What was fascinating when I was trained on long forgotten Cray machines, was how beautifully tuned the hardware was for solving particular types of elliptical equations numerically.

It wasn’t the fastest machine processing wise at the time, but it was clearly engineered with great care to be able to handle these specific problems such that disks could feed the solid state storage just fast enough to get data in and results out, and the solid state storage fed memory, which fed processors, which did maths in a conveyor belt fashion. Every step has just enough bits to keep the next faster bit of the system busy for the particular task at hand.

So I don’t doubt Google have one of the most fascinating computing system on the planet, but it is likely designed and built to solve very different problems. For a start given bits of the Google system are in various different data centers around the world, I’m guessing their interconnect latency is many orders of magnitude larger than the peak latency between nodes of a traditional supercomputer. But it probably doesn’t matter if your search query takes 50ms to get to the right place.

It may be like comparing a train and an aircraft carrier, sure aircraft carriers generate a million horsepower, but pretty useless for getting commuters into work in the morning. Collectively the trains that do the London commute in the morning have more horse power than any aircraft carrier, but you wouldn’t want to fight a war with them.

Train engineers and aircraft carrier engineers share knowledge, but I wouldn’t be surprised if the aircraft carrier engineers had conferences where no train engineers turned up, and vice-versa.

Brough Turner

Simon, thanks! You are of course entirely correct that supercomputer designs are typically tuned to particular algorithms. It’s also true that, once a specific supercomputer is available, inventive folks find ways to tweak their algorithms to best utilize the particular system’s ratio of processing, memory and inter-processor communication bandwidth.

As to the specifics of the Google supercomputer, it’s interconnectivity is rather like that of many Beowulf clusters, i.e. Gigabit Ethernet between servers that each have substantial local disk storage. Typically Google has thousands or tens of thousands of machines in a given data center, so speed of light delays between data centers around the world are probably not an issue for most computations.

The Top 500 supercomputer list (http://www.top500.org/list/2007/11) requires that all the machines in a cluster be located within one data center. If you agree with that criteria (it seems a dated idea to me), Google might not come out at the top of the list, but they would occupy 20+ separate locations on the list.

In any event, there are some very interesting papers published by Googlers listed here: http://research.google.com/pubs/papers.html#category4

In particular, their Map-reduce algorithm should be a joy to any LISP programmer. http://labs.google.com/papers/mapreduce-osdi04.pdf

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