Using emlmex to speed up FFT/IFFT is not generally possible. MATLAB already uses FFTW, which is compiled and optimized, selects an algorithm that seems especially fast for the data and hardware. Currently, emlmex generates a generic radix-2 FFT/IFFT and then compiles it with your C compiler. How your C compiler manages the cache, however well it generates code, that's how it's going to work relative to what is even possible with a generic radix-2 FFT. Well, that's almost true. There are some arbitrary cutoffs there for whether the twiddle factors are pre-computed in a lookup table or computed on the fly using sine and cosine. That sort of thing matters. Especially considering that data has to marshaled to and from a mex file, the only way emlmex-generated FFT going to be competitive with FFTW is if we're talking about a small enough vector that the FFTW overhead dominates, which is pretty small, in my experience.
In general emlmex does not speed up anything that in MATLAB is already executed in compiled libraries, e.g. LAPACK-type linear algebra computations, including linear system solves, SVD, EIG, and, of course, FFT. The kinds of things that emlmex speeds up are operations that are heavier on the MATLAB interpreter, e.g. computations that require lots of logic and/or loops. It also tends to speed up fixedpoint applications dramatically.