Utilization is not a lie, it is a measurement of a well-defined quantity, but people make assumptions to extrapolate capacity models from it, and that is where reality diverges from expectations.

Hyperthreading (SMT) and Turbo (clock scaling) are only a part of the variables causing non-linearity, there are a number of other resources that are shared across cores and "run out" as load increases, like memory bandwidth, interconnect capacity, processor caches. Some bottlenecks might come even from the software, like spinlocks, which have non-linear impact on utilization.

Furthermore, most CPU utilization metrics average over very long windows, from several seconds to a minute, but what really matters for the performance of a latency-sensitive server happens in the time-scale of tens to hundreds of milliseconds, and a multi-second average will not distinguish a bursty behavior from a smooth one. The latter has likely much more capacity to scale up.

Unfortunately, the suggested approach is not that accurate either, because it hinges on two inherently unstable concepts

> Benchmark how much work your server can do before having errors or unacceptable latency.

The measurement of this is extremely noisy, as you want to detect the point where the server starts becoming unstable. Even if you look at a very simple queueing theory model, the derivatives close to saturation explode, so any nondeterministic noise is extremely amplified.

> Report how much work your server is currently doing.

There is rarely a stable definition of "work". Is it RPS? Request cost can vary even throughout the day. Is it instructions? Same, the typical IPC can vary.

Ultimately, the confidence intervals you get from the load testing approach might be as large as what you can get from building an empirical model from utilization measurement, as long as you measure your utilization correctly.

It might be a lie, but it surely is a practical one. In my brief foray into site reliability engineering I used CPU utilisation (of CPU-bofund tasks) with queueing theory to choose how to scale servers before big events.

The %CPU suggestions ran contrary to (and were much more conservative than) the "old wisdom" that would otherwise have been used. It worked out great at much lower cost than otherwise.

What I'm trying to say is you shouldn't be afraid of using semi-crappy indicators just because they're semi-crappy. If it's the best you got it might be good enough anyway.

In the case of CPU utilisation, though, the number in production shouldn't go above 40 % for many reasons. At 40 % there's usually still a little headroom. The mistake of the author was not using fundamentals of queueing theory to avoid high utilisation!

Reminds me of Brendan Gregg's "CPU Utilization is Wrong" but this blog fails to discuss that blog's key point that CPU utilization is a measure of whether or not the CPU is busy, including whether the CPU is waiting [0]. That blog also explains that the IPC (instructions per cycle) metric actually measures useful work hidden within that busy state.

[0]: https://www.brendangregg.com/blog/2017-05-09/cpu-utilization...

This is bang on, you can't count the hyperthreads as double the performance, typically they are actually in practice only going to bring 15-30% if the job works well with it and their use will double the latency. Failing to account for loss in clockspeed as the core utilisation climbs is another way its not linear and in modern software for the desktop its really something to pay careful attention to.

It should be possible from the information you can get on a CPU from the OS to better estimate utilisation involving at the very least these two factors. It becomes a bit more tricky to start to account for significantly going past the cache or available memory bandwidth and the potential drop in performance to existing threads that occurs from the increased pipeline stalls. But it can definitely be done better than it is currently.

The way they refer to cores in their system is confusing and non-standard. The author talks about a 5900X as a 24 core machine and discusses as if there are 24 cores, 12 of which are piggybacking on the other 12. In reality, there are 24 hyperthreads that are pretty much pairwise symmetric that execute on top of 12 cores with two sets of instruction pipeline sharing same underlying functional units.

There's many ways CPU utilization fails to work as expected.

I didn't expect an article on this style. I was expecting the normal Linux/Windows utilization but wtf it's all RAM bottlenecked and the CPU is actually quiet and possibly down clocking thing.

CPU Utilization is only how many cores are given threads to run by the OS (be it Windows or Linux). Those threads could be 100% blocked on memcpy but that's still CPU utilization.

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Hyperthreads help: if one thread is truly CPU bound (or even more specifically: AVX / Vector unit bound), while a 2nd thread is hyperthreaded together that's memcpy / RAM bound, you'll magically get more performance due to higher utilization of resources. (Load/store units are separate from AVX compute units).

In any case, this is a perennial subject with always new discoveries about how CPU Utilization is far less intuitive than many think. Still kinda fun to learn about new perspectives on this matter in any case.

Author discovers that performance does not scale proportionally to %CPU utilisation, and gets instead to the conclusion that %CPU utilisation is a lie.

There are many reasons for the lack of a proportional relationship, even in the case where you do not have hyperthreading or downclocking (in which cases you just need to interpret %CPU utilisation in that context, rather than declare it "a lie"). Even in apple silicon where these are usually not an issue, you often do not get an exactly proportional scaling. There may be overheads when utilising multiple cores wrt how data is passed around, or resource bottlenecks other than CPU.

This hits so close to home. I once tried to explain to a manager that a server at 60% utilization had zero room left, and they looked at me like I had two heads. I wish I had this article back then!

The benchmark is basically application performance testing, which is the most accurate representation you can get. Test the specific app(s) your server is running, with real-world data/scenarios, and keep cranking up the requests, until the server falls over. Nothing else will give you as accurate an indication of your server's actual maximum performance with that app. Do that for every variable that's relevant (# requests/s, payload size, # parameters, etc), so you have multiple real-world maximum-performance indicators to configure your observability monitors for.

One way to get closer to reliable performance is to apply cpu scheduler limits to what runs your applications to keep them below a given threshold. This way you can better ensure you can sustain a given amount of performance. You don't want to run at 100% cpu for long, especially if disk i/o becomes hampered, system load skyrockets, and availability starts to plummet. Two thousand servers with 5000ms ping times due to system load is not a fun day at the office.

(And actually you'll never get a completely accurate view, as performance can change per-server. Rack two identical servers in two different racks, run the same app on each, and you may see different real-world performance. One rack may be hotter than the other, there could be hidden hardware or firmware differences, etc. Even within a server, if one CPU is just nearer a hotter component than on another server, for reasons)

Uses stress-ng for benchmarking, even though the stress-ng documentation says it is not suitable for benchmarking. It was written to max out one component until it burns. Using a real app, like Memcached or Postgres would show more realistic numbers, closer to what people use in production. The difference is not major, 50% utilization is closer to 80% in real load, but it breaks down faster. Stress-ng is nicely linear until 100%, memcached will have a hockey stick curve at the end.