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0 pointsnostrademons4mo ago0 comments

I think this is a classic hill-climbing dilemma. If you start in the same place, and one org has worked very hard and spent a lot of money optimizing the system, they will probably come out on top. But if you start in a different place, reimagining the problem from first principles, you may or may not find yourself with a taller hill to climb. Decisions made very early on in your hill-climbing process lock you in to a path, and then the people tasked with optimizing the system later can't fight the organizational inertia to backtrack and pick a different path. But a new startup can.

It's worth noting that Google actually did succeed with a wildly different architecture a couple years later. They figured "Well, if CPU performance is hitting a wall - why use just one CPU? Why not put together thousands of commodity CPUs that individually are not that powerful, and then use software to distribute workloads across those CPUs?" And the obvious objection to that is "If we did that, it won't be compatible with all the products out there that depend upon x86 binary compatibility", and Google's response was the ultimate in hubris: "Well we'll just build new products then, ones that are bigger and better than the whole industry." Miraculously it worked, and made a multi-trillion-dollar company (multiple multi-trillion-dollar companies, if you now consider how AWS, Facebook, TSMC, and NVidia revenue depends upon the cloud).

Transmeta's mistake was that they didn't re-examine enough assumptions. They assumed they were building a CPU rather than an industry. If they'd backed up even farther they would've found that there actually was fertile territory there.

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cpgxiii4mo ago

> It's worth noting that Google actually did succeed with a wildly different architecture a couple years later. They figured "Well, if CPU performance is hitting a wall - why use just one CPU? Why not put together thousands of commodity CPUs that individually are not that powerful, and then use software to distribute workloads across those CPUs?" And the obvious objection to that is "If we did that, it won't be compatible with all the products out there that depend upon x86 binary compatibility", and Google's response was the ultimate in hubris: "Well we'll just build new products then, ones that are bigger and better than the whole industry." Miraculously it worked, and made a multi-trillion-dollar company (multiple multi-trillion-dollar companies, if you now consider how AWS, Facebook, TSMC, and NVidia revenue depends upon the cloud).

Except "the cloud" at that point was specifically just a large number of normal desktop-architecture machines. Specifically not a new ISA or machine type, running entirely normal OS and libraries. At no point did Google or Amazon or Microsoft make people port/rewrite all of their software for cloud deployment.

At the point that Google's "bunch of cheap computers" was new, CPU performance was still rapidly improving. The competition was traditional "big iron" or mainframe systems, and the novelty was in achieving high reliability through distribution, rather than building on fault-tolerant hardware. By the time the rate of CPU performance improvement was slowing in the mid 2000s, large clusters smaller machines were omnipresent in supercomputing and HPC applications.

The real "new architecture(s)" of this century are GPUs, but much of the development and success of them is the result of many iterations and a lot of convergent evolution.

jasonwatkinspdx4mo ago

> At the point that Google's "bunch of cheap computers" was new

It wasn't even new, people just don't know the history. Inktomi and HotBot were based on a fleet of commodity PC servers with low reliability, whereas other large web properties of the time were buying big iron like Sun E10K. And of course Beowulf clusters were a thing.

And as far as I know, google's early ethos didn't come as some far sighted strategy, but just the practical reality of Page and Brin building the first versions of their search engine on borrowed/scavenged hardware as grad students and then continuing that trajectory.

onecommentman4mo ago

The “bunch of cheap computers” approach was being studied and implemented at the National Labs years before Google showed up. Revisionist history?

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fajitaforce54mo ago

That’s revisionist. Transmeta set out to write a software like cpu core. That will always lose to dedicated hardware.

hinkley4mo ago

> Well we'll just build new products then, ones that are bigger and better than the whole industry.

With blackjack, and hookers!

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