Re: [LAD] vectorization

[...] Another strategy for Complex Multiply Add would be to organize the data in vertical arrays of the same type. This is to say that there should be one array of Real followed by another array of Imaginary, perhaps something like this: typedef struct { float r[N] __attribute__ ((aligned(16))); float i[N] __attribute__ ((aligned(16))); } cvec_t; cvec_t cA,cB,cD; It can be argued that the data is now scattered all over the place, and twice as many moves are needed to perform a single cmadd. This may be true in some cases as we shall later see, but when you are operating on vectors you'd usually want to load and store (at least) 4 variables at a time. The routine to calculate them all becomes: for (i = 0;i < N; ++i) { cD.r[i] += cA.r[i] * cB.r[i] - cA.i[i] * cB.i[i]; cD.i[i] += cA.r[i] * cB.i[i] + cA.i[i] * cB.r[i]; } This auto-vectorizes really well with icc -O3 -msse, here looped a billion times and compared to the original routine: It is not too bad with gcc -O3 -msse -ftree-vectorize either: But all things are not so rosy in the vector-department if we should also plan to distribute this code fragment as part of a binary generic i386 package, say conservatively compiled with gcc -O2: That was pretty bad! Your trusted 100MHz pentium suddenly got downgraded to 66MHz :-/ So to conclude: A more than 10 times speedup of cmadd() of large arrays is possible by a) rearangement of the data in a format that fits modern machines and b) switch of compiler (which perhaps not everybody is willing to do.) /j