Which languages are you using?

In my experience, the main problem with expressing your algorithms in that way is "What do you do when the primitives you are suing (e.g. maps and scans) do not produce optimal code?".

For example, suppose that you are doing two scans with two maps, whose efficient implementation does a single memory traversal with the two scans and maps fused into a single kernel. If whatever abstraction you are using isn't able to do this type of kernel fusion, then you easily bump to a 4x larger memory bandwidth, which might be translated to 4x slower performance.

I don't know of any language or optimizing runtime that's able to do these transformations and match the performance of the CUDA code one would have written by hand.