alt Hacker News> Elsewhere on HA you’ll find my extended rant about how strange it is that we don't have a language that elegantly abstracts computation over threads, SIMD, GPUs etc. Compilers can do this sort of thing now, just not optimally.
Autoparallelization has been a hot topic for literally decades, quite possibly longer than you've been alive.
The problem is that the techniques you need to do to write good SIMD code versus good GPU code versus good multithreaded code versus distributed computation are all different. Taking just memory concerns: a SIMD code needs you to carefully arrange memory so that every thread is accessing an adjacent memory location. GPU code likes locality, but you have large group sizes that can share all the local memory pretty cheaply, and loading from global memory to local memory is relatively expensive, so now you have to do a lot of tuned blocking. With multithreaded code, you now want to avoid sharing between different threads (which generally requires distributing loop iterations among threads very differently). And with a distributed platform, now you're primarily worrying about the overhead of communication of data between different nodes, and you're trying to minimize that.
There is better than even odds I'm older than you, so I'd recommend you rethink using phrases like "possibly longer than you've been alive", it's not ... polite regardless of people's age.
The point (and I'd encourage you to find that thread to not retread ground) is that we absolutely can compile most computation heavy code for these different targets reasonably well - what we cannot garentee is that the resulting code is optimal given context. But gosh we can do so much - I’d encourage you to look into in profile guided, target aware, and autotuning optimization etc. (and then of course, there are LLM guided optimizations, but that's a whole other kettle of fish)