[ATsch]

I definitely agree with it being a useful model, after all that is the the way GPUs are presented to programmers. But when getting into detailed performance comparisons, especially with CPUs, that simplified model breaks down quickly and becomes a hindrance. Which is why I think it's unfortunate that NVidia (deliberately) uses the term "core" that leads people to believe they can make a direct comparison to CPUs. Comparisons that coincidentally lead people to believe GPUs are much more special than they actually are.

[jjoonathan]

In detailed comparisons all simplified models break down. Tallying max theoretical compute is only appropriate if you're going to put in the effort to actually use it, which is exceedingly rare, even in compute kernels that have supposedly had lots of love and attention already paid to them. So the human factor has to be included in the model and the human factor consistently de-rates SIMD more than it does SIMT.

I realize that under the covers this is more of a compiler/language thing than a compute model thing, but for whatever reason I just don't see much SIMT code targeting CPUs, so again, human factor.

[ATsch]

My primary objection is not to the SIMT model, but NVidia reusing a term with an established meaning ("core") for something completely different and incomparable. Other companies terms like "execution units", "compute units" and "stream processors" (although perhaps not as much the last) are much more truthful about the nature of GPUs without hindering the programming model at all.

[jjoonathan]

From the standpoint of a programmer who doesn't want to suffer through constant DIY chunking and packing (very close to my personal definition of hell), a CUDA core looks a lot like a CPU core. From the point of view of someone not writing the code, a CUDA core looks like merely another FPU.