As for the second part, one needs a great deal more thought into the definitions of transcendentals before proceeding. The 22-bit mantissa approximations of transcendentals on GPUs are fantastic for most scientific computing and machine learning applications. Any error they introduce is more than offset by errors introduced by the assumptions that led to those algorithms in the first place.
(1)We agree that the farther you move up the programmer hierarchy, the harder it is to make a reproducible algorithm. My solution is to defy the trend and implement the bowels of the hierarchy, but I'd be happy for a computer scientist to design something from the ground up that was bitwise-reproducible (and please please strongly typed so it's L1-friendly without doing functional programming backflips to make it so). I really don't think it should be that hard given how easily and repeatedly I have done so from the guts outward.
I'm just saying, like OP, that this work shouldn't be needed. You should get this result for free and not even have to think about it.
The instruction sets certainly aren't irreproducible and I shouldn't have implied that, although there are plenty of IEEE extensions not well-standardized (eg, transcendentals). The problem is that their specifications grow weaker as you move up the programming hierarchy. Optimizers rearranging algebra, etc. And the result that's presented to the normal programmer is that floating-point is analog computing.