> Another interesting alternative is SIMT. Instead of having a handful of special-case instructions combined with heavyweight software-switched threads, we could have had every instruction SIMDified. It requires structuring programs differently, but getting max performance out of current CPUs already requires SIMD + multicore + predictable branching, so we're doing it anyway, just in a roundabout way.

Is that not where we're already going with the GPGPU trend? The big catch with GPU programming is that many useful routines are irreducibly very branchy (or at least, to an extent that removing branches slows them down unacceptably), and every divergent branch throws out a huge chunk of the GPU's performance. So you retain a traditional CPU to run all your branchy code, but you run into memory-bandwidth woes between the CPU and GPU.

It's generally the exception instead of the rule when you have a big block of data elements upfront that can all be handled uniformly with no branching. These usually have to do with graphics, physical simulation, etc., which is why the SIMT model was popularized by GPUs.