fast matrix multiplication in Matlab

Question

I need to make a matrix/vector multiplication in Matlab of very large sizes: \"A\" is an 655360 by 5 real-valued matrix that are not necessarily sparse and \"B\" is a 655360

Answer 1

Your #1 option, if this is your bottleneck, is to re-examine your algorithm. See this question Optimizing MATLAB code for a great example of how choosing a different algorithm reduced runtime by three orders of magnitude.

Answer 2

I have had good results with matlab matrix multiplication using the GPU

Answer 3

Matlab is built using fairly optimized libraries (BLAS, etc.), so you can't easily improve upon it from within Matlab. Where you can improve is to get a better BLAS, such as one optimized for your processor - this will enable better use of the caches by getting appropriately sized blocks of data from main memory. Take a look into creating your own compiled versions of ATLAS, ACML, MKL, and Goto BLAS.

I wouldn't try to solve this one particular multiplication unless it's really killing you. Changing up the BLAS is likely to lead to a happier solution, especially if you're not currently making use of multicore processors.

Answer 4

In order to avoid the transpose operation, you could try:

sum(bsxfun(@times, A, B), 2)

But I would be astonished it was faster than the direct version. See @thiton's answer.

Also look at http://www.mathworks.co.uk/company/newsletters/news_notes/june07/patterns.html to see why the column-vector-based version is faster than the row-vector-based version.

Answer 5

Matlab's raison d'etre is doing matrix computations. I would be fairly surprised if you could significantly outperform its built-in matrix multiplication with hand-crafted tools. First of all, you should make sure your multiplication can actually be performed significantly faster. You could do this by implementing a similar multiplication in C++ with Eigen.