You're just making up definitions now, which perhaps explains why no-one can follow your reasoning. A DPU isn't a generic accelerator and you shouldn't be referring to accelerators as DPUs. If you want to speed up raytracing, adding a raytracing accelerator doesn't mean adding a DPU (although if you can add a DPU as a class of Tensilica architecture and have that do the job). Same with AI or video decoding. These accelerators will conform to a processor architecure as defined by whatever taxonomy one uses, for which I don't think there's an officially accepted one. So some accelerators will be DSPs, and others will be ultra wide SIMD processors, etc.
Considering people asked for a definition of DPU earlier to be able to enter into this discussion, and you left it to others of us before offering your definition which none of us could guess, it's no wonder this is another pointless thread that no-one can do anything with.
Stop.
Math has always been the foundational core of all computer anything. Triangles are no exception. Triangles were selected for a variety of reasons over quads for instance. Alongside the GPU we had and still have Larger register extensions. Before that we had math coprocessors. Your explanation sucks. GPUs are good at large matrices math. Even then, there are still particular math functions it will not be good at with certain hardware.
You're all over the place, I don't even need to read anything of what you've written to know that. There are many things we've done that can be done differently but that doesn't necessarily mean it's going to be faster or slower for that matter. Things have become the way they are as a proper form of evolution over time and we can tie a lot of decisions back to costs.
Someone needed a simple example & I gave a simple example.