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.
I agree. If you actually know what you're doing you can use perf and/or ftrace to get highly detailed processor metrics over short periods of time, and you can see the effects of things like CPU stalls from cache misses, CPU stalls from memory accesses, scheduler effects, and many other things. But most of these metrics are not very actionable anyway (the vast majority of people are not going to know what to do with their IPC or cache hit or branch hit numbers).
What most people care about is some combination of latency and utilization. As a very rough rule of thumb, for many workloads you can get up to about 80% CPU utilization before you start seeing serious impacts on workload latency. Beyond that you can increase utilization but you start seeing your workload latency suffer from all of the effects you mentioned.
To know how much latency is impacted by utilization you need to measure your specific workload. Also, how much you care about latency depends on what you're doing. In many cases people care much more about throughput than latency, so if that's the top metric then optimize for that. If you care about application latency as well as throughput then you need to measure both of those and decide what tradeoffs are acceptable.
> 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.
I think this is probably one of the most important points... similarly, is this public facing work dealing with any kind of user request, or is it simply crunching numbers/data to build an AI model from a stable backlog/queue?
My take has always been with modern multi-core, hyper-threaded CPUs that are burstable is to consider ~60% a "loaded" server. That should have work split if it's that way for any significant portion of a day. Mostly dealing with user-facing services. So bursts and higher traffic portions of the day are dramatically different from lower utilization portions of the day.
A decade ago, this lead to a lot of work for cloud provisioning on demand for the heavier load times. Today it's a bit more complicated when you have servers with 100+ cores as an option for under $30k (guestimate based on $10k CPU price). Today, I'd lean to over-provisioning dedicated server hardware and supplement with cloud services (and/or self-cloud-like on K8s) as pragmatically as reasonable... depending on the services of course. I'm not currently in a position where I have this level of input though.
Just looking at how, as an example, StackOverflow scaled in the early days is even more possible/prudent today to a much larger extent... You can go a very long way with a half/full rack and a 10gb uplink in a colo data center or two.
In any case, for me... >= 65% CPU load for >= 30m/day means it's at 100% effective utilization, and needs expansion relatively soon. Just my own take.
IEEE Hot Interconnects just wrapped up and they discussed latency performance tuning for Ultra Ethernet where it looks smooth on 2- or 5- sec view but at 100ms you see the obvious frame burst effects. If you don't match your profiling to the workload a false negative compounds your original problem by thinking you tested this so better look elsewhere.
That's all true, and the % part is still a lie. As you note, CPU utilization isn't linear, and percentages are linear measures. CPU utilization isn't a lie, % CPU utilization is.
What about 2 workloads that both register 100% CPU usage, but one workload draws significantly more power and heats the CPU up way more? Seems like that workload is utilizing more of the CPU, more of the transistors or something.
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!
Agree. Another example of this is for metrics as percentiles per host that you have to average, vs histograms per host that get percentile calculated at aggregation time among hosts. Sure an avg/max of a percentile is technically not a percentile, but in practice switching between one or the other hasn’t affected my operations at all. Yet I know some people are adamant about mathematical correctness as if that translates to operations.
Combination of CPU% and loadavg would generally tell how system is doing. I had systems where loadavg is high, waiting on network/io, but little cpu%. Tracing high load is not always straightforward as cpu% though, you have to go through io%, net%, syscalls etc.
I noticed exactly the same thing. The author is saying something that has been repeatedly written in queueing theory books for decades, still they are noticing this only now.
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.
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.
To complicate things more HT performance varies wildly between CPU architectures and workloads. e.g. AMD implementation, especially in later Zen cores, is closer to a performance of a full thread than you'd see in Intel CPUs. Provided you are not memory bandwidth starved.
For memory-bound applications the scaling can be much better. A renderer I worked on was primarily memory-bound walking the accelerator structure, and saw 60-70% increase from hyperthreads.
Back when I got an i7-3770K (4C/8T), I did a very basic benchmark using POV-Ray.
Going from 1 thread to 2 threads doubled the speed as expected. Going from 2 to 4 doubled it again. Going from 4 to 8 was only ~15% faster.
I imagine you could probably create a contrived benchmark that actually gives you nearly double the performance from SMT, but I don't know what it would look like. Maybe some benchmark that is written to deliberately constantly miss cache?
Side note, I should run that POV-Ray test again. It's been years since I've even use POV-Ray.
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.
Years ago, when trying to explain hyper threading to my brother, who doesn't have any specialized technical knowledge, he came up with the analogy that it's like 2-ply toilet paper. You don't quite have 24 distinct things, but you have 12 that are roughly twice as useful as the individual ones, although you can't really separate them and expect them to work right.
Thanks for the feedback. I think you're right, so I changed a bunch of references and updated the description of the processor to 12 core / 24 thread. In some cases, I still think "cores" is the right terminology though, since my OS (confusingly) reports utilization as-if I had 24 cores.
Will be interesting when (if?) Intel ships software defined cores which are the logical inverse of hyper threading.
Instead of having a big core with two instruction pipelines sharing big ALUs etc, they have two (or more) cores that combine resources and become one core.
If both SMT cores are being asked to do the same workload they will likely contend for the same resource and execution units internally so the boost from SMT will be less. If they have different workloads the boost will be more. Now throw in P and E cores on newer CPU's, turbo and non-turbo, everything gets very complicated. I did see a study that adding SMT got a much better performance per watt boost than adding turbo which was interesting/useful.
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!
Up to a hair over 60% utilization the queuing delays on any work queue remain essentially negligible. At 70 they become noticeable, and at 80% they've doubled. And then it just turns into a shitshow from there on.
The rule of thumb is 60% is zero, and 80% is the inflection point where delays go exponential.
The biggest cluster I ran, we hit about 65% CPU at our target P95 time, which is pretty much right on the theoretical mark.
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.
The advantage of stress-ng is that it's easy to make it run with specific CPU utilization numbers. The tests where I run some number of workers at 100% utilization are interesting since they give such perfect graphs, but I think the version where I have 24 workers and increase their utilization slowly is more realistic for showing how production CPU utilization changes.
I remember being stuck in a discussion with management one time, that went something like this:
Manager: CPU utilisation is 100% under load! We have to migrate to bigger instances.
Me: but is the CPU actually doing useful work?
(chat, it was not. busy waiting is CPU utilisation too)
These days I treat CPU usage as just a hint, not a conclusion. I also look at response times, queue lengths, and try to figure out what the app is actually doing when it looks idle.
How many times has hyperthreading been an actual performance benefit in processors? I cannot count how many times an article has come out saying you'll get better performance out of your <insert processor here> by turning off hyperthreading in the BIOS.
It's gotta be at least 2 out of every 3 chip generations going back to the original implementation, where you're better off without it than with.
HT provides a significant benefit to many workloads. The use cases that benefit from actually disabling HT are likely working around pessimal OS scheduler or application thread use. (After all, even with it enabled, you're free to not use the sibling cores.) Otherwise, it is an overgeneralization to say that disabling it will benefit arbitrary workloads.
For whatever it’s worth, operational database systems (many users/connections, unpredictable access patterns) are beneficiaries of modern hyperthreading.
I’m familiar with one such system where the throughput benefit is ~15%, which is a big deal for a BIOS flag.
IBM’s POWER would have been discontinued a decade ago were it not for transactional database systems, and that architecture is heavily invested in SMT, up to 8-way(!)
To be fair, in most of these tests hyperthreading did provide a significant benefit (in the general CPU stress test, the hyperthreads increased performance by ~66%). It's just confusing that utilization metrics treat hyperthread usage the same as full physical cores.
Those weird Xeon Phi accelerators had 4 threads per core, and IIRC needed at least 2 running to get full performance. They were sort of niche, though.
I guess in general parallelism inside a core will either be extracted by the computer automatically with instruction-level-parallelism, or the programmer can tell it about independent tasks, using hyperthreads. So the hyperthread implementations are optimistic about how much progrmmers care about performance, haha.
For me today it's definitely a pessimation because I have enough well-meaning applications that spawn `nproc` worker threads. Which would be fine if they're the only process running, but they're not.
Going from 1 core to 2 hyperthreads was a big bonus in interactivity. But I think it was easy to get early systems to show worse throughput.
I think there's two kinds of loads where hyperthreads aren't more likely to hurt than help. If you've got a tight loop that uses all the processor execution resources, you're not gaining anything by splitting that in two, it just makes things harder. Or if your load is mostly bound by memory bandwidth without a lot of compute... having more threads probably means you're that much more oversubscribed on i/o and caching.
But a lot of loads are grab some stuff from memory and then do some compute, rinse and repeat. There's a lot of potential for idle time while waiting on a load, being able to run something else during that time makes a lot of sense.
It's worth checking how your load performs with hyperthreads off, but I think default on is probably the right choice.
In the old days it had made the difference between my multimedia game like application not working at all with hyperthreading off to working just fine with it on.
Funny that it talks about matrixprod, which I think is not that relevant as benchmark — unless you care about x87 performance specifically. I recently sent a pull request to try to address that in a generic manner: https://github.com/ColinIanKing/stress-ng/pull/561
Yet I'm still surprised by this benchmark. On both Zen2 and Zen4 in my tests (5900X from the article is Zen3), matrixprod still benefits from hyperthreading and scales a bit after all the physical cores are filled, unlike what the article results show.
All of this is tangential of course, as I'd tend to agree that CPU utilization% is just an imprecise metric and should only be used as a measure of "is something running".
I think looking at power consumption is potentially a more interesting canary when using very high core count parts.
I've ran some ML experiments on my 5950x and I can tell that the CPU utilization figure is entirely decoupled from physical reality by observing the amount of flicker induced in my office lighting by the PWM noise in the machine. There are some code paths that show 10% utilization across all cores but make the cicadas outside my office window stop buzzing because the semiconductors get so loud. Other code paths show all cores 100% maxed flatline and it's like the machine isn't even on.
This has been my experience running production workloads as well. Anytime CPU% goes over 50-60% suddenly it'll spike to 100% rather quickly, and the app/service is unusable. Learned to scale earlier than first thought.
The lie is that hyper thread "cores" are equal to real "cores". Maybe this is what happens when an over 20-year old technology (hack) becomes ubiquitous and gets forgotten about? (We have to rediscover why our performance measurements don't seem to make sense?)
The other thing I think we have a hard time visualizing is that processor is only either executing (100%) or its waiting to execute (0%) and that happens over varying timescales... so trying to assign a % in between inherently means you're averaging over some arbitrary timescale...
In TaskManager, click the "Performance" tab and see the simple stats.
While on the Performance tab, then click the ellipsis (. . .) menu, so you can then open ResourceMonitor.
Then close TaskManager.
In ResourceMonitor, under the Overview tab, for the CPU click the column header for "Average CPU" so that the processes using the most CPU are shown top-down from most usage to least.
In Overview, for Disk click the Write (B/sec) column header, for Network click Send (B/sec), and for Memory click Commit (KB).
Then under the individual CPU, Memory, Disk, and Network tabs click on the similar column headers. Under any tab now you should be able to see the most prominent resource usages.
Notice how your CPU settles down after a while of idling.
Then click on the Disk tab to focus your attention on that one exclusively.
Let it sit for 5 or 10 minutes then check your CPU usage. See if it's been climbing gradually higher while you weren't looking.
I like his empirical approach to get to the root significance of the cpu %-age indicator. Software engineers and data analysts take discrete "data" measurements and statistics for granted.
"data" / "stats" are only a report, and that report is often incorrect.
They've been advocating against SMT for a long while, citing security risks and inconsistent performance gains. I don't know which HW/CPU bug in the long series of rowhammer, meltdown, spectre, etc prompted the action, but they've completely disabled SMT in the default installation at some point.
The core idea by itself is fine: keep the ALUs busy. Maybe security-wise, the present trade-off is acceptable, if you can instruct the scheduler to put threads from the same security domain on the same physical core. (How to tell when two threads are not a threat to each other is left up as an exercise.)
Read kernel code to see how CPU utilisation is calculated. In essence, count scheduled threads to execute and divide by number of cores. Any latency (eg. wait for memory) is still calculated as busy core.
A worse lie is memory usage reporting, I think in every major OS it is understated and misreported. In case with Linux, I wanted to know who is using memory, and tried to add PSS values for every process, I never got back the total memory usage. In case with Windows/Mac I judge by screenshot of their tools which show unrealistically small values.
As for the article, the slowdown can be also caused by increased use of shared resources like caches, TLBs, branch predictors.
The memory usage is interesting, where different kind of shared memory is obvious hard to visualize, just two values per process doesn’t say enough.
Most users actually wants a list of ”what can I kill to make the computer faster”, I.e. they want an oracle (no pun) that knows how fast the computer will be if different processes are killed.
CPU utilization alone is misleading. Pair it with per core load average or runqueue length to see how threads are actually queuing. That view often reveals the real bottleneck, whether it is I/O, memory, or scheduling delays.
Wait until you encounter GPU utilization. You could have two codes listing 100% utilization and have well over 100x performance difference from each other. The name of these metrics creates natural assumptions that are just wrong. Luckily it is relatively easy to estimate the FLOP/s throughput for most GPU codes and then simply compare to the theoretical peak performance of the hardware.
Don't forget that theoretical peak performance is (probably) half the performance listed on the nvidia datasheet because they used the "with sparsity" numbers! I've seen this bite folks who miss the * on the figure or aren't used to reading those spec sheets.
Yeah, the obvious thing with processors is to do something similar:
(1) Measure MIPS with perf (2) Compare that to max MIPS for your processor
Unfortunately, MIPS is too vague since the amount of work done depends on the instruction, and there's no good way to measure max MIPS for most processors. (╯°□°)╯︵ ┻━┻
I think it was always a mistake to pretend hyperthreading doubles your core count. I always assumed it was just due to laziness; the operating system treats a hyperthreaded core as two "virtual cores" and schedules as two cores, so then every other piece of tooling sees double the number of actual cores. There's no good reason I know of that a CPU utilization tool shouldn't use real cores when calculating percentages. But, maybe that's hard to do given how the OS implements hyperthreading.
>There's no good reason I know of that a CPU utilization tool shouldn't use real cores when calculating percentages
On AMD, threads may as well be cores. If you take a Ryzen and disable SMT, you're basically halving its parallelism, at least for some tasks. On Intel you're just turning off an extra 10-20%.
tl;dr: guy vibecodes a thing to measure something he doesn't fully understand and then realizes his methodology is wrong. Ends up with a catchy "X is a lie" title, which itself can be considered a lie.
ot|6 months ago
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.
eklitzke|6 months ago
What most people care about is some combination of latency and utilization. As a very rough rule of thumb, for many workloads you can get up to about 80% CPU utilization before you start seeing serious impacts on workload latency. Beyond that you can increase utilization but you start seeing your workload latency suffer from all of the effects you mentioned.
To know how much latency is impacted by utilization you need to measure your specific workload. Also, how much you care about latency depends on what you're doing. In many cases people care much more about throughput than latency, so if that's the top metric then optimize for that. If you care about application latency as well as throughput then you need to measure both of those and decide what tradeoffs are acceptable.
tracker1|6 months ago
I think this is probably one of the most important points... similarly, is this public facing work dealing with any kind of user request, or is it simply crunching numbers/data to build an AI model from a stable backlog/queue?
My take has always been with modern multi-core, hyper-threaded CPUs that are burstable is to consider ~60% a "loaded" server. That should have work split if it's that way for any significant portion of a day. Mostly dealing with user-facing services. So bursts and higher traffic portions of the day are dramatically different from lower utilization portions of the day.
A decade ago, this lead to a lot of work for cloud provisioning on demand for the heavier load times. Today it's a bit more complicated when you have servers with 100+ cores as an option for under $30k (guestimate based on $10k CPU price). Today, I'd lean to over-provisioning dedicated server hardware and supplement with cloud services (and/or self-cloud-like on K8s) as pragmatically as reasonable... depending on the services of course. I'm not currently in a position where I have this level of input though.
Just looking at how, as an example, StackOverflow scaled in the early days is even more possible/prudent today to a much larger extent... You can go a very long way with a half/full rack and a 10gb uplink in a colo data center or two.
In any case, for me... >= 65% CPU load for >= 30m/day means it's at 100% effective utilization, and needs expansion relatively soon. Just my own take.
jimmySixDOF|6 months ago
SAI_Peregrinus|6 months ago
SirMaster|6 months ago
unknown|6 months ago
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unknown|6 months ago
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kqr|6 months ago
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!
therealdrag0|6 months ago
Agree. Another example of this is for metrics as percentiles per host that you have to average, vs histograms per host that get percentile calculated at aggregation time among hosts. Sure an avg/max of a percentile is technically not a percentile, but in practice switching between one or the other hasn’t affected my operations at all. Yet I know some people are adamant about mathematical correctness as if that translates to operations.
mayama|6 months ago
saagarjha|6 months ago
zekrioca|6 months ago
mustache_kimono|6 months ago
[0]: https://www.brendangregg.com/blog/2017-05-09/cpu-utilization...
4gotunameagain|6 months ago
PaulKeeble|6 months ago
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.
c2h5oh|6 months ago
magicalhippo|6 months ago
But overall yeah.
Sohcahtoa82|6 months ago
Going from 1 thread to 2 threads doubled the speed as expected. Going from 2 to 4 doubled it again. Going from 4 to 8 was only ~15% faster.
I imagine you could probably create a contrived benchmark that actually gives you nearly double the performance from SMT, but I don't know what it would look like. Maybe some benchmark that is written to deliberately constantly miss cache?
Side note, I should run that POV-Ray test again. It's been years since I've even use POV-Ray.
tgma|6 months ago
saghm|6 months ago
BrendanLong|6 months ago
sroussey|6 months ago
Instead of having a big core with two instruction pipelines sharing big ALUs etc, they have two (or more) cores that combine resources and become one core.
Almost the same, yet quite different.
https://patents.google.com/patent/EP4579444A1/en
Neil44|6 months ago
bboreham|5 months ago
dragontamer|6 months ago
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.
freehorse|6 months ago
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.
saagarjha|6 months ago
judge123|6 months ago
hinkley|6 months ago
Up to a hair over 60% utilization the queuing delays on any work queue remain essentially negligible. At 70 they become noticeable, and at 80% they've doubled. And then it just turns into a shitshow from there on.
The rule of thumb is 60% is zero, and 80% is the inflection point where delays go exponential.
The biggest cluster I ran, we hit about 65% CPU at our target P95 time, which is pretty much right on the theoretical mark.
PunchyHamster|6 months ago
unknown|6 months ago
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0xbadcafebee|6 months ago
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)
CCs|6 months ago
BrendanLong|6 months ago
kristopolous|6 months ago
bionsystem|6 months ago
swiftcoder|6 months ago
(chat, it was not. busy waiting is CPU utilisation too)
kristianp|6 months ago
ChaoPrayaWave|6 months ago
hinkley|6 months ago
It's gotta be at least 2 out of every 3 chip generations going back to the original implementation, where you're better off without it than with.
loeg|6 months ago
twoodfin|6 months ago
I’m familiar with one such system where the throughput benefit is ~15%, which is a big deal for a BIOS flag.
IBM’s POWER would have been discontinued a decade ago were it not for transactional database systems, and that architecture is heavily invested in SMT, up to 8-way(!)
BrendanLong|6 months ago
bee_rider|6 months ago
I guess in general parallelism inside a core will either be extracted by the computer automatically with instruction-level-parallelism, or the programmer can tell it about independent tasks, using hyperthreads. So the hyperthread implementations are optimistic about how much progrmmers care about performance, haha.
tgma|6 months ago
The primary trade-off is the cache utilization when executing two sets of instruction streams.
duped|6 months ago
esseph|6 months ago
https://www.tomshardware.com/pc-components/cpus/zen-4-smt-fo...
toast0|6 months ago
I think there's two kinds of loads where hyperthreads aren't more likely to hurt than help. If you've got a tight loop that uses all the processor execution resources, you're not gaining anything by splitting that in two, it just makes things harder. Or if your load is mostly bound by memory bandwidth without a lot of compute... having more threads probably means you're that much more oversubscribed on i/o and caching.
But a lot of loads are grab some stuff from memory and then do some compute, rinse and repeat. There's a lot of potential for idle time while waiting on a load, being able to run something else during that time makes a lot of sense.
It's worth checking how your load performs with hyperthreads off, but I think default on is probably the right choice.
FpUser|6 months ago
tom_|6 months ago
Aissen|6 months ago
Yet I'm still surprised by this benchmark. On both Zen2 and Zen4 in my tests (5900X from the article is Zen3), matrixprod still benefits from hyperthreading and scales a bit after all the physical cores are filled, unlike what the article results show.
All of this is tangential of course, as I'd tend to agree that CPU utilization% is just an imprecise metric and should only be used as a measure of "is something running".
bob1029|6 months ago
I've ran some ML experiments on my 5950x and I can tell that the CPU utilization figure is entirely decoupled from physical reality by observing the amount of flicker induced in my office lighting by the PWM noise in the machine. There are some code paths that show 10% utilization across all cores but make the cicadas outside my office window stop buzzing because the semiconductors get so loud. Other code paths show all cores 100% maxed flatline and it's like the machine isn't even on.
N_Lens|6 months ago
morning-coffee|6 months ago
The other thing I think we have a hard time visualizing is that processor is only either executing (100%) or its waiting to execute (0%) and that happens over varying timescales... so trying to assign a % in between inherently means you're averaging over some arbitrary timescale...
fennecfoxy|6 months ago
It wouldn't really make sense to include all parts of the CPU in the calculation.
fuzzfactor|6 months ago
Ctrl-Alt-Del then launch TaskManager.
In TaskManager, click the "Performance" tab and see the simple stats.
While on the Performance tab, then click the ellipsis (. . .) menu, so you can then open ResourceMonitor.
Then close TaskManager.
In ResourceMonitor, under the Overview tab, for the CPU click the column header for "Average CPU" so that the processes using the most CPU are shown top-down from most usage to least.
In Overview, for Disk click the Write (B/sec) column header, for Network click Send (B/sec), and for Memory click Commit (KB).
Then under the individual CPU, Memory, Disk, and Network tabs click on the similar column headers. Under any tab now you should be able to see the most prominent resource usages.
Notice how your CPU settles down after a while of idling.
Then click on the Disk tab to focus your attention on that one exclusively.
Let it sit for 5 or 10 minutes then check your CPU usage. See if it's been climbing gradually higher while you weren't looking.
tonymet|6 months ago
"data" / "stats" are only a report, and that report is often incorrect.
rollcat|6 months ago
They've been advocating against SMT for a long while, citing security risks and inconsistent performance gains. I don't know which HW/CPU bug in the long series of rowhammer, meltdown, spectre, etc prompted the action, but they've completely disabled SMT in the default installation at some point.
The core idea by itself is fine: keep the ALUs busy. Maybe security-wise, the present trade-off is acceptable, if you can instruct the scheduler to put threads from the same security domain on the same physical core. (How to tell when two threads are not a threat to each other is left up as an exercise.)
saagarjha|6 months ago
gbin|6 months ago
smallstepforman|6 months ago
codedokode|6 months ago
As for the article, the slowdown can be also caused by increased use of shared resources like caches, TLBs, branch predictors.
biggusdickus69|6 months ago
Most users actually wants a list of ”what can I kill to make the computer faster”, I.e. they want an oracle (no pun) that knows how fast the computer will be if different processes are killed.
HPsquared|6 months ago
aaa_2006|6 months ago
steventhedev|6 months ago
System load is well defined, matches user expectations, and covers several edge cases (auditd going crazy, broken CPU timers, etc).
pama|6 months ago
spindump8930|6 months ago
BrendanLong|6 months ago
(1) Measure MIPS with perf (2) Compare that to max MIPS for your processor
Unfortunately, MIPS is too vague since the amount of work done depends on the instruction, and there's no good way to measure max MIPS for most processors. (╯°□°)╯︵ ┻━┻
saagarjha|6 months ago
PathOfEclipse|6 months ago
fluoridation|6 months ago
On AMD, threads may as well be cores. If you take a Ryzen and disable SMT, you're basically halving its parallelism, at least for some tasks. On Intel you're just turning off an extra 10-20%.
throwmeaway222|6 months ago
1gn15|6 months ago
unknown|6 months ago
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bdhcuidbebe|6 months ago
unknown|6 months ago
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unknown|6 months ago
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kunley|6 months ago
timzaman|6 months ago
therealdrag0|6 months ago
saagarjha|6 months ago