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You can parallelize with mutations, just split the data and do a tiny bit of extra work when the data overlaps. There aren't that many real world cases where yo

You can parallelize with mutations, just split the data and do a tiny bit of extra work when the data overlaps. There aren't that many real world cases where you want to run expensive computations on highly connected data, and in those cases you mostly just write GPU code instead of bothering with functional CPU languages. That is what the engineers who works on the extreme end of performance does, if you want to learn functional programming that is efficient and performant then go and learn GPU programming, the techniques you learn there also works for many CPU applications, there are decades of top end industry practices behind those techniques.

zelphirkalt 4y ago
There is certainly some kind of middle ground between single core CPU and massive parallelization on a GPU. What am I going to use my 16 CPU cores for? Should they sit idle forever, only ever handing parallel work over to the GPU? Also not all work is parallelizable in the way graphics are parallelizable or follow the relatively simple fork-join structure. What about things like actor systems, where each actor may run on another core, each doing different kind of work? Sometimes one might want to also have good single core performance in parallelized settings, so that the work for a single core gets finished quickly. I am not convinced the picture is really as black and white as "do parallelized things on GPU, single core things on CPU".