BrendanLong

I'd love to explain, but you'd need to change your name to Brendan first.

Fun data point though, I just ran three data points of the Phoronix nginx benchmark and got these results:

- Pinned to 6 cores: 28k QPS

- Pinned to 12 cores: 56k QPS

- All 24 cores: 62k QPS

I'm not sure how this applies to realistic workloads where you're using all of the cores but not maxing them out, but it looks like hyperthreading only adds ~10% performance in this case.

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. (╯°□°)╯︵ ┻━┻

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.

Some esoteric methods of measuring CPU utilizations are to calculate either the current power usage over the max available power, or the current temperature over the max operating temperature. Unfortunately these are typically even more non-linear than the standard metrics (but they can be useful sometimes).

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.

A big part of this is that CPU utilization metrics are frequently averaged over a long period of time (like a minute), but if your SLO is 100 ms, what you care about is whether there's any ~100 ms period where CPU utilization is at 100%. Measuring p99 (or even p100) CPU utilization can make this a lot more visible.

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.

Expanding on that last point, one of the examples in the article is someone making ~$70 per month in cryptocurrency. The CI people -could- send a lawyer to Vietnam to try to collect that $70 but even if they succeed it's very not worth it.

They're not starting a CI job per hash (that would be too slow). I'm not sure exactly how each of these cryptocurrencies works, but presumably what they're doing is starting a miner which attempts hashes for a while and then stops. And the only reason the jobs stop at all is that it would be too obvious if they ran continuously.

Investment info sites seem to be skeptical that there's anything wrong with naked shorts. They became illegal after the 2008 crisis, but they're bad in the sense of being extremely risky, not that they're fraudulent.

It's also unclear if naked shorting is actually happening here. It's entirely possible for the short interest to be far above 100%. Consider this setup:

- WidgetCo floats exactly one stock - Alice buys the one stock (short interest = 0%) - Alice lends the stock to Bob - Bob sells the stock to Carol (short interest = 100%) - Carol lends the stock to Dave - Dave sells the stock to Eve (short interest = 200%) - Repeat until short interest reaches the moon

Bob and Dave both legitimately borrowed a shared and sold it, so there's no naked shorting going on.

I suspect what's happening with GameStop is that the stock price is just so absurdly overvalued that short interest has reached a level that people previously thought was impossible.