Even the "on-paper" analyses can take some of this into account, with algorithm analysis models that take into account cache and NUMA architectures. But, people do tend to just look at the classic big-O analyses, which may not be right.
In a way that kind of dependence isn't new. If you look at old versions of Knuth's TAOCP, for many algorithms he has separate analyses for the in-RAM case and the on-tape case, using two different memory models, and in some cases which algorithm is better differs a lot between the two cases. But it may be that algorithms reference books are doing a worse job keeping up now than they used to.
Knuth is probably the only (major) author that I can think of off-hand who took the application of his algorithms and data structures so seriously (going so far as to invent his own little RISC instruction set, MIX (later MMIX.) in a book that concerned itself with the theory as aswell as the practical aspects. Most books focus almost exclusively on one or the other.
I think aside from things like cache coherency, memory models and things like SIMD, computer scientists will have to think of their algorithms (or at least cover it when they write books for undergrads or professionals) in terms of commutativity and associativity so people can see how parallelisable an algorithm is or can be.
In a way that kind of dependence isn't new. If you look at old versions of Knuth's TAOCP, for many algorithms he has separate analyses for the in-RAM case and the on-tape case, using two different memory models, and in some cases which algorithm is better differs a lot between the two cases. But it may be that algorithms reference books are doing a worse job keeping up now than they used to.