Otherwise I think the rest of Erlang's performance is because it's optimized for reliability and distribution rather than raw performance.
One is promises (async/await). This takes the burden off the programmer for explicitly managing concurrency, and potentially allows lazy evaluation in many cases (if non-side-effecting). I've never worked in Lisps but it seems like a viable model in many ways.
The one I wanted to bring up was probabilistic programming. Imagine a tree where there's not just one place to put something, there's many, possibly distributed. As such you greatly reduce the chances of contention from multiple threads. They would also probably (almost necessarily) reduce the amount of rebalancing work.
GPUs seem like the perfect model here in multiple ways, not only do you have the massive degree of threading that surfaces any problems in such things but you also have the degree of threading to deterministically search these kind of structures. GPUs also highly discourage things like CAS or mutexing that we need to move away from.
(PS nice username)
GPUs are useful for the kinds of parallelism that has always been known to be easy - fork join with all threads writing to new memory. There is no mystery to how to do this.
> CAS or mutexing that we need to move away from
compare and swap is the backbone of lock free programming. Mutexes to me seem to rarely be ideal, but there is no getting away from compare and swap as far as I know.
If you think about the heart of the problem, it is really synchronization, and that only needs to happen in certain places in programs. Anything you can break up into pieces that can be transformed independently can be leveraged for concurrency, which also implies that independent stages in a pipeline can be concurrent as well.
Right now we are two steps removed. There aren't even many good data structure implementations out there. There is moodycamel::concurrent_queue and different memory allocators like jemalloc and the new rpmalloc, but the vast majority of programmers likely don't even realize that malloc locks and kills concurrency. Maybe they grab something from boost that just surrounds a data structure with a mutex, which is lazy bullshit.
I actually think the majority of software that runs slow can be made to be sped up nearly linearly using dozens of threads, but the few niche libraries that help are overly complicated to compile and use, bloated with enormous dependencies and end up being a minefield of usage. At the moment many people's idea of multi-threaded is mostly fork-join techniques like openMP and that will never scale to using all cores throughout the whole program, since is one very narrow technique.