But GPUs have thousands of cores, which means if you want any benefit, the smallest data type you must use is something like array[n * 1024], or your GPU is wasted doing nothing. So you must shape your programs into that memory model or you're just wasting your time (and GPU).
That's >50% of the problem.
Some OS/hardware have separate GPU memory (Nvidia), some have unified memory (Macos, modern consoles), but moving memory is usually not the problem.
Things don't "run on CPU by default", it's up to the programmer where to run it. But the CPU and GPU paradigm of "running a program" are very different, since on GPU you're running SIMD (i.e. running your code on arrays) or else there is no point to use a GPU.
the key for me is thinking in terms of sets—much of your conditional logic is probably about sorting things into sets anyway.