You assume that the function is concave. What is the reason for that? I could imagine that the real function is monotonic but non-strict, hence non-concave.
Thanks! That's a good question and it is, of course, just an assumption.
My main reasoning was that as we increase the power level more and more, I would expect diminishing returns, i.e., a saturation curve as we would see in many physical (and especially electromagnetical) processes. Hence concavity. I would find a relation where we could get essentially limitless compute out of the die if we just provide enough power much less likely.
I could be wrong about that, of course. It could be that the function is not (globally) concave and just has a cut-off at some upper limit (basically when the CPU blows up). Or maybe the real function is concave but doesn't follow the exponential that I used here.
In the end I settled at the current formulation somewhat out of convenience and because it seemed to be able to explain/fit the (limited) data already quite well.
I did some experimentation using concave (again!) polynomials, but found the that in that case it was much more difficult to achieve convergence - and when it did, the results weren't much different than the current ones.
Do you have any particular suggestion for an alternative model in mind? I will probably continue experimenting with this a bit more in the future.
My main reasoning was that as we increase the power level more and more, I would expect diminishing returns, i.e., a saturation curve as we would see in many physical (and especially electromagnetical) processes. Hence concavity. I would find a relation where we could get essentially limitless compute out of the die if we just provide enough power much less likely.
I could be wrong about that, of course. It could be that the function is not (globally) concave and just has a cut-off at some upper limit (basically when the CPU blows up). Or maybe the real function is concave but doesn't follow the exponential that I used here.
In the end I settled at the current formulation somewhat out of convenience and because it seemed to be able to explain/fit the (limited) data already quite well.
I did some experimentation using concave (again!) polynomials, but found the that in that case it was much more difficult to achieve convergence - and when it did, the results weren't much different than the current ones.
Do you have any particular suggestion for an alternative model in mind? I will probably continue experimenting with this a bit more in the future.