Fastest way to calculate minimum euclidean distance between two matrices containing high dimensional vectors

Question

I started a similar question on another thread, but then I was focusing on how to use OpenCV. Having failed to achieve what I originally wanted, I will ask here exactly what I w

Answer 1

Matrix multiply generally uses the worst possible cache access pattern for one of the two matrices, and the solution is to transpose one of the matrices and use a specialized multiply algorithm that works on data stored that way.

Your matrix already IS stored transposed. By transposing it into the normal order and then using a normal matrix multiply, your are absolutely killing performance.

Write your own matrix multiply loop that inverts the order of indices to the second matrix (which has the effect of transposing it, without actually moving anything around and breaking cache behavior). And pass your compiler whatever options it has for enabling auto-vectorization.