No, they're not. Straight front the horse's mouth:

https://software.intel.com/en-us/mkl/features/benchmarks

In no case will you see it get close to 10TFLOPS. GPUs easily do this, and can approach 100TFLOPS with tensor cores.

I don't dispute this. If you can remove memory transfer overhead and meet the requirements I mentioned, then GPUs will be much better. But there are many problems that do not fit that (SIMD per warp) regime. In short, GPUs are no panacea and have their own trade-offs and bottleneck sensitivities, just like everything else.

I agree that they are no panacea, but we also have an example of the Intel Xeon phi, which did have high speed hbm memory. That too did not compete with GPUs. I think the majority of it has to do with the GPU chip itself being extremely simple, with no Branch prediction, no branch difference, very simple caching behavior, etc. They are able to allocate a much larger portion of the die to just throw fma units at it.

> comparing GPU to naive implementations (and sometimes, implementations written in dynamic languages) instead of to highly tuned BLAS or MKL implementations

In your experience, are these comparisons using naive GPU implementations as well? If so, that still seems like a valuable comparison.