Are there any highly optimized MKL routines or maybe performance primitives that can do rectangle matrix transposition but without scaling?
I've been using mkl_omatcopy but it seems to perform worse than a normal baseline implementation and I suspect this is due to the additional scaling that is performed. I've attached a plot running a naive baseline implementation with comparison on omatcopy and imatcopy. The latter I know runs very poorly on non-square matrices.
I just want to know whether I should start spending some time optimizing my own transpose routine with AVX/AVX2 and blocking or whether there's a very efficient one out there already.
Also, swapping indices is not viable for what I am trying to achieve.
Thank you in advance!
Thanks for your reply. The transpositions I am performing are related to the dimension lifted transposition as seen in Henretty et al (http://repository.cmu.edu/
Anyway, I am basically transposing these large vectors into MxN arrays where N is always the SIMD register size which for this case is 4 as I am doing double precision. Therefore, on the graph, all matrix sizes will be MxN where M=no of element/veclen and N=veclen.
I guess this could be a cause for the poor performance due to gather and scatters? By the way, I am running this on a Xeon E5-2650 (Sandy Bridge).
The code looks something like this:
// out of place MKL transposition
// retranspose data back into original format for y-sweep of flucrd
So basically I need to transpose the data into the DLT format and then back again. Originally, the matrices will have a rectangle shape format, as they represent distinct blocks from a multiblock grid.
Thank you in advance for your kind consideration.