I have another gripe with these kinds of immutability / FP praising articles: The assumption that this must be faster, because multicore. As an example, many if

I have another gripe with these kinds of immutability / FP praising articles: The assumption that this must be faster, because multicore. As an example, many if not most of scientific applications (for which Haskell wants to position itself) are memory bandwidth bounded, not CPU bounded. These applications also deal with Gigabytes of data for each timestep, applied over dozens if not hundreds of kernels. So we're talking about thousands to tens of thousands of functional updates over data objects, each on the order of Gygabytes. The way this is dealth with efficiently in many cases is by doing pointer swapping - after each timestep, or even for intermediate kernels, the old output becomes the new input and the old input is overwritten by the new output. Swapping allows this to be an O(1) operation. Allocating new memory at each kernel launch for the output would absolutely kill the performance - we're talking one to two orders of magnitude here. Even if an FP compiler comes along that is able to optimize this, if this ever breaks I have zero trust that it will be a simple thing to figure out why the compiler doesn't do what I wanted, and at that point I'd rather just program it procedurally. To my knowledge, game programming is pretty much the same thing, so these usecases are rather wide spread. I think there must be a (probably not yet invented) language somewhere that would allow me to write computations in immutable FP style, either as pointwise operations (i.e. stencils), or as matrix operations, but allows me to pass in some information that directly instructs the backend what it should do with the memory. I.e. I want to be able to program my own memory pools and link pointers to memory slots needed for the computations. Maybe that part could even be done using visual programming, akin to linking up GUI elements to properties in XCode: Let the compiler figure out where a memory slot is needed, do a dependency analysis for the memory slots and show graphically what slots are linked to what input and output. Then, let me add constraints to that linking and recompute a new solution whenever I move something around. The next step would be to do a performance model for each processor and figure out the best solution using machine learning, but let me actually see what the solution is without me having to go look at assembly (which, frankly, for FP frontends seems near impossible since it has even less to do with the frontend code than if you compile down something like C or Fortran).