> What's preventing optimisations from being built for maths to be at least as fast as something like Numpy? Is it just the realtime nature of the BEAM makes it difficult?
You have to take immutability and multi-processing into account. Each process has a separate heap as a design assumption, so if you want to pass data between them, you must copy. Data is immutable as another design assumption, and this means that if you want to change something in a bigger data structure, you also must copy.
Theoretically, the compiler is free to optimize the latter - if it notices that some code can be safely optimized in a mutating way, it is free to do that, but then, someone needs to write that optimization - and the fact that this is not a priority for BEAM means that it is unlikely that someone will spend time to implement it if there are more pressing matters for them to do so. Then, not only it must be written, it must be proven that this optimization does not break anything.
You have to take immutability and multi-processing into account. Each process has a separate heap as a design assumption, so if you want to pass data between them, you must copy. Data is immutable as another design assumption, and this means that if you want to change something in a bigger data structure, you also must copy.
Theoretically, the compiler is free to optimize the latter - if it notices that some code can be safely optimized in a mutating way, it is free to do that, but then, someone needs to write that optimization - and the fact that this is not a priority for BEAM means that it is unlikely that someone will spend time to implement it if there are more pressing matters for them to do so. Then, not only it must be written, it must be proven that this optimization does not break anything.