Jim Keller had an interesting talk recently [1] about ways of doing parallel processing to better us the billions of transistors we have - assuming the task is parallelizable. There's the scalar core (i.e the basic CPU) which is easy to program realtively. Then a scalar core with vector instructions - difficult to program efficiently. Then there are arrays of scalar cores, i.e. GPUs, so relatively easy to program again, and now a lot of startups with arrays of scalar cores each with vector engines, so expected to be most difficult to program. He didn't go into why vector instructions are hard to use efficiently, and hard for compiler writers, but I'd be interested if anyone here could explain that.
1. https://youtu.be/8eT1jaHmlx8