In fact the CPU stagnation is worse. In contrast to the shrinking chip size and higher frequencies of past scaling laws, CPU core count does not easily translate to performance gains. It needs specialized and careful designed software to tap the capability. In fact if CPU core count did translate more easily to performance gains I think already with the existing CPU's we'd have a fairly signficant one-time boost. May…
My guess is very few cores are used on average. I did some testing with Solvespace to see which build options contributed most to performance:
https://github.com/solvespace/solvespace/issues/972
Obviously using OpenMP for multi-core was the big win. But what's not shown is that in typical usage (not the test I ran) if you're dragging some geometry around it will use all cores (in my case 4 cores / 8 threads) at about 50 percent utilization. That percentage probably drops as more cores are thrown at it due to Amdahl's Law. In other words, throwing double the cores at it will give a good boost to a lot of code that is already taking less than half the time (wall clock time, not CPU time).
We added OpenMP to a number of functions for significant performance gains. And in fact, any remining single-thread operation that gets the parallel treatment is likely to have a significant impact on overall performance since that is where most of the time is spent now. At this point we're more focused on features and bugs.
Algorithmic improvements are possible and I'd like to do those in the future, but they are much harder to do than sprinkling some #pragmas around critical loops. That will improve the scalability though, where multithreading really did not.