https://www.hp.com/us-en/shop/tech-takes/why-should-i-upgrad...

Here's Reddit's take

https://www.reddit.com/r/explainlikeimfive/comments/66vy2r/e...

With typical database servers, hyperthreading provides about 1.4x the performance "for free" because it allows execution units to do something useful while another thread is waiting for memory.

Where it isn't useful is typical game engines, except in some corner-cases as you've noticed.

Typical game engines like to have dedicated cores with the highest possible performance. Hyperthreading improves overall throughput at the expense of single-threaded throughput.

Similarly, game engines tend to run task-per-thread with different code, which tends to pollute the L1 code cache too much. So if two different threads are running on the same core but are doing different things, they'll fight over this precious resource.

Intel measured something like a +20% overall improvement averaged out across a wide range of workloads. A few had regressions, and a few (like ray tracing) had huge benefits.

IMHO, hyperthreading makes the most sense for servers, or laptops with a small number of cores (2-4). For high-end gaming, 16 cores with HT off will likely have the best performance.

More recently we did some DirectX 12 MT-rendering tests; since there's more considerable overhead for breaking up the work into more batches so we wanted to be sure so we checked carefully.

On a i7-12700K we found that distributing Dx12 work to SMT or E-Cores had no benefit, even in a contrived rendering stress-test.

However, on an unnamed game console, we found that scheduling additional work to the SMT cores improved draw performance by maybe 7%.

YMMV but my impression is the workload needs stall on memory enough to get signfican gains by interleaving the ALU from another thread and this might benefit low-power systems like laptops or mobile phones more than workstations.

* hyperthreading is useless for purely-numerical code (the inner loop of math/science software), and for this kind of code it is also often harmful per the next point

* hyperthreading is harmful for code that is highly cache-sensitive within the scheduling quantum (though multiple cores may share caches too, and for the degenerate case of RAM-as-cache-for-swap people generally think in terms of "too many threads means more thrashing"; hyperthreading really isn't special there). This is more likely for "busy" tasks, but "background" tasks are likely to have no problem here.

* hyperthreading is beneficial for OO-like code that does a lot of pointer-chasing (which almost all software is, at least in part)

* hyperthreading might beat threads in separate cores for contended atomics

If you're trying to optimize particular code, it's generally better to eliminate the horrible memory patterns in the first place than expect a handwaved hardware "solution". Where it wins is for broader code that is too spread out for you to focus your attention on.

And of course, there is still a lot of code that is fundamentally single-threaded (at least in the critical path), and if allowed to hog the CPU will not see any benefit from hyperthreading.

For a long time hyperthreading was any easy win on desktop for all the various single-threaded background tasks. Now, with increasing core counts, specialized E- vs P-cores (and focus on power management in general), and Spectre/Meltdown, I'm not sure.

And when your very legitimate workload does stall, inevitably, it's nice if the CPU has another thread able to issue while it waits. In fact hyperthreading benefits almost all but the most heavily tuned workloads. Pretty much everyone stalls on a routine enough basis to make it worthwhile.

Now, in the post-spectre world where you need to spend transistors on firewalling/tagging to prevent information leaks, maybe it's not as big a benefit. But it's mischaracterizing history to claim that it was never worthwhile.

Just like HT.

CPUs target low latency (they switch often). GPUs target high troughput (they switch rarely, only when needed).

High troughput algorithms dont have problem with a lot of threads. Low latency algorithms have problem with a lot of threads (they need lot of cache memory because of constant switching).