Which I again stress can't be done for most workloads.
Look, we did experiments like this back in the 80's. MIT built a supercomputer with several THOUSAND CPU's inside it. And they discovered that most S/W loads do NOT scale well. And even those that do tend to drop off after 16-32, due to various reasons (I/O bottlenecks, locks, the OS scheduler, etc).
Sure, SOME applications can scale well close to infinity. SQL databases are largely independent on a per cell basis, so you can scale at close to 100%. Rendering is another example, hence GPU's. But most workloads do NOT scale in this way. Simple examples: You can't do 3d sound propagation without knowing the current geometry, so sound and rendering touch. That overlap hinders scaling. You can't do AI without inputs from the geometry and sound, so more overlap there. And so on. You don't have totally independent threads, and every time a thread touches another, you have a performance bottleneck.
Finally, you have the scheduler to consider. Developers do NOT use hardcoded thread logic (put thread "x" on core "y"), because there is no way to know current and future workloads on that core (EG: you are not the only thread running). So the cores threads are assigned to are left up to the OS scheduler. In theory, you put threads on the core with the least amount of current work, dynamically adjusting every time a thread gets run. But then you have to consider the CPU architecture (shared cache or dedicated cache per core? This matters if you share a lot of data between threads) as well, so even that gets sticky.
My point being simple: For the majority of workloads, applications can not, and will not, scale beyond a couple of CPU cores. No amount of coding by developers is going to change this very simple fact.
I also note that trying to determine scaling based on Task Managers 1 second sample rate is kind of silly when you think about it, considering most threads will run, finish their work, and be replaced with some other thread LONG before Task Manager gets its next workload sample.